The New Tech Paradigm of Blockchain

Blockchain in simple terms is a very long and distributed chain of blocks of transaction information. It uses the distributed ledger technology (DLT) which consists of continuously growing (endless) list of transaction records which are called Blocks (blocks of information).

Blockchain first came to existence in 2008, to make it secure the blocks were interlinked with a unique identification include date/time stamps and a trusted party digital signature/stamp. The blocks were also distributed across worldwide large pool of computers to ensure it is impossible to erase or alter any recorded block of information. The conceptual model was then introduced for use for crypto currency bitcoin from year 2009 and since then it served as public DLT for all bitcoin transaction records.

Let’s understand how blockchains work,

Blockchain Process Overview

1.     As Blockchains use the distributed ledger technology (DLT), they are managed using peer-to-peer (P2P) network of computers spread across the world.

2.     The computers called as nodes are generally running the digital ledger of transaction records called as blocks.

3.     Each node (computer) uses the consensus algorithm protocol for adding and validating new transactions records called as blocks.

4.     Every block is distributed across the world involving many computer systems. Each block has the previous blocks unique identification information for validation and integrity of blocks.

5.     Blocks hold information of transactions records in an encoded Hash Tree format where each leaf of the hash tree (in this case the block) has the encoded cryptographic hash of pervious block.

6.     The information on the blocks is interlinked to each other using this unique cryptographic hash and other relevant information about the transaction, date and time when the block was created and recorded.

7.     The blocks once recorded can’t be deleted or altered in any way. They permanently remain on the chain of records and remain retrievable.

8.     Blocks can be created but only the longest forming blockchain confirms the reliability of the block. In additions the entire network of computers can validate and confirm if the block and block chain is valid as per their records or not.

9.     As each block is replicated across the entire blockchain network of computers and each computer stores all the blocks information, it takes a lot of time for the blocks to be created and validated across the network. 

10.  As the size of the DLT (distributed ledger network) increases the speed of the block creation and validation reduces. E.g., a block creation for bitcoin having very huge network and extra-large size ledger could take up to 15 minutes while for the new bitcoins it can be done within 30 to 60 seconds as the ledger and size of network is limited.

11.  The blockchains can’t be altered so incase of new regulations or rules introduction from a specific date and time, will result in a New Hard Fork creation to identify and use the blockchains under that specific fork.

Now that we understand what blockchains are, let’s move to the types of blockchains. Here are the types of blockchain,

1.     Public Blockchain – Public blockchains are open to all decentralized blockchains. All people with internet access can use such blockchains to send their transaction records as well as become the node using consensus protocol to be able to validate records for their authenticity. Such blockchains work based on incentive mechanism where each node gets some incentives for the storage and validations done. E.g., Bitcoin and Ethereum are examples of public blockchain.

2.     Private Blockchain – As the name suggests, the private blockchain is restricted access blockchain. The access is strictly restricted for both creator and validator nodes. Such blockchains use distributed ledger technology (DLT) and can called as centralized blockchain to a large extent. E.g., Banking organizations can use Private Blockchain

3.     Hybrid Blockchain – A combi of Public and Private can be called as hybrid blockchain were certain areas of the blockchain can be kept public with open access and remaining areas private with restricted access. E.g., Healthcare organizations can use Hybrid Blockchain

4.     Consortium or Side Blockchain – These are blockchain where a ledger of blockchain transaction records (blocks) run parallel to primary blockchain. Entries from primary blockchain are linked to the side blockchain. Consortiums and Groups or even group of countries, companies etc. can use this type of blockchain. E.g., Monetary authority can run consortium blockchain with all banks and financial institutions, allowing banks to have their separate side blockchain with links to the primary consortium blockchain.

Here are some pros and cons of blockchain technology,

Pros for Blockchain include,

1.     Increased transparency and authenticity – Blockchain verify information across worldwide nodes to ensure transaction is authentic. The ledger in public blockchain is open to all for easy access and full transparency.

2.     Permanent Ledger of blocks – Blocks of transactions records never get old or archived. They always remain available in the blockchain across the world. So there is no chance of having any lost transaction records on the blockchain unless all nodes (computers) in the blockchain are altered.

3.     Easy Track and Trace – All blocks in the blockchain are always interlinked in such a way that entire chain of blocks remain fully intact. This makes tracking and tracing easier.

4.     Cost Savings – This depends on how the blockchain is used. E.g., if blockchain is used for smart contracts of real estate ownerships in a country. It will ensure the entire process is digitalized and contracts are put in blocks with all relevant information making them always available. Savings come from paper, archive stores and people.

Cons for Blockchain include,

1.     Implementation Challenges – Blockchain functions using distributed and often decentralized network of nodes spread across the world. It is difficult to trace, monitor and maintain the nodes efficiency and effectiveness. In addition, the adoption and usage itself could be also challenging depending on the area of use.

2.     Regulatory Compliance – Blockchain are still not widely accepted technology by major institutions. Although it functions well for crypto currencies and several other trails. For crypto currency itself regulatory compliance issues exist across the world.

3.     Technology complexity – Blockchain upgrades, maintenance and support as well as patching and security upgrades for nodes across the world can be very complex to achieve.

4.     Future Technology – While blockchain is one of the newest technologies but its adaption has been slow due to unforeseen future of what next and how the entire ecosystem can evolve.

There are many blockchain platforms in use across the world. Here some of the top ones,

1.     IBM Blockchain – IBM platform offers a fully managed blockchain-as-a-service solution which can be configured, developed and deployed by organizations for their own use.

2.     Ethereum – Ethereum is a decentralized blockchain platform that can be used for smart contracts and in many ways, it operates similar to the bitcoin blockchain platform.

3.     Ripple, Tron and Stellar – These are three different blockchain providers but they have similar capabilities as Ethereum.

4.     Chainalysis KYT (Know Your Transaction) – KYT blockchain platform is one of the latest and easy to use platforms with latest technology gaining a lot of popularity.

5.     Hyper Ledger – Fabric and Sawtooth – These are two different blockchain platforms that offer application creation ability. Sawtooth is an opensource version while fabric is not.

6.     Multichain – It is an open source blockchain platform for use by organizations with ability to create both public and private blockchains.

7.     Kakao Klaytn Blockchain – It is a blockchain platform by Kakao, its modular network design and architecture makes it easy to use for businesses to configure, customize and operate service-oriented blockchains based on the proprietary Klaytn framework.

8.     Amazon, Azure Quorum and Google Blockchain – The top 3 cloud providers also offer blockchain as a service platform for quick configuration and use by organizations. Here a simple overview of AWS Blockchain architecture as a reference.

Amazon Blockchain aș a service architecture overview

Over the years the acceptance and use of various types of bitcoins increased and still continuing to increase. This resulted in financial institutions, monetary authorities and governments to start thinking in the direction of how to control and add good governance model around bitcoins. Many countries are thinking of bringing digital currency (similar to bitcoin) but under governance and control of respective monetary authorities and governments. It is very likely that the underlying technology and ecosystem to be similar or enhanced version of Blockchain making them universally accepted and usable across the world.

For the year 2019 Gartner reported that only 5% of CIOs believed blockchain technology was a ‘game-changer’ for their business as blockchain usage is largely recognized for use only for FinTech organizations, the likes of banks, investment firms, credit card companies and finance divisions.

As we are now in 2022, Blockchain is used in various shapes and forms by almost all the major industries and sectors. Confirmed usage and use cases can be found for Automotive, Banking, Financial services, Government, Healthcare, HealthTech, life sciences, Insurance, Media, Entertainment, Retail, Consumer goods, Logistics and Telecommunications.

Everything you need know about ChatBots

The word chatbot is made up of “Chat” and “Bot”, it means chat powered and executed by bot (computer system). ChatBot actually emerged from “Chatterbots” back in 1990s. As it was too early to use them, the usage didn’t come to life until last decade. ChatBots were started to get used by organizations more effectively from 2015 onwards. In past 7 years the usage and industry for chatbots have grown by leaps and bounds.

A ChatBot is a computer software which helps organizations handle their customer queries automatically ay anytime (7×24 hours) anywhere (across the globe) using any device (mobile, tablet, laptop, PC, smart tv etc.) connected to the internet. 

ChatBots interact with diverse customer groups using messaging platforms which are designed to work and act as if a human customer service assistant is answering the queries based on keywords identified in the question and their model answers.

ChatBots require continuous update and learning as they are meant to answer customer queries related to product, organization, compliant, enquiry for information etc. There is also a need of having active human customer service agents behind the ChatBot to step in for situations where it is difficult for the ChatBot to answer further questions.

ChatBots are of many types, they can be very simple keyword based, rule-based Bots as well as sophisticated self-learning artificially intelligent (AI) Bots with built in natural language processing (NLP) capabilities as well as machine learning and deep learning abilities.

Here is a brief overview of Chatbot process and architecture,

ChatBot Process Overview and Logical Architecture

1.     The user generally opens a website or app to search for their need or to get support and guidance.

2.     The ChatBot popups with welcome greetings and either it starts by giving standard options that the user can choose to decide which area he needs help on or it allows the user to ask the query.

3.     User places the query which the ChatBot receives and processes. The processing is done through NLP (natural language processing) service consisting of NLU (natural language understanding) that helps understand users intent of the query based on keywords and phrases asked. The query is processed by the dialog manager and the response is given by Dialog manager to NLU (Natural Language Generation) which answers users query either in written or voice response.

4.     The user can see the response and ask further questions or after a waiting time of 1 minute the ChatBot can do the Thanks greeting to ask further questions and close the chat.

5.     In the background there are multiple technical architecture blocks and systems that operate to make the ChatBot work including storage systems, Knowledge base, API message Interfaces, Business Process Interfaces, IoT interfaces and ChatBot front end as well as back end conversational query engine.

We all are very familiar of ChatBots and use them in our daily lives. Here are some everyday examples of ChatBots including their types,

NLP Based ChatBot Overview

1.     AI based ChatBots – These chatbots are advanced and have NLP (natural language processing) capabilities. Some of them also have built in self learning abilities attached with machine learning and deep learning neural network algorithms.

a.     Siri on Apple Devices – The simplest form of AI ChatBot is NLP based Siri on Apple devices meant to serve our voice commands and answer our queries. Other than answering our queries, it is able to do many of our tasks, remember important events and remind us at the right time.

b.     Amazon Echo and Alexa – Alexa and Echo are voice enabled AI ChatBots and are enabled using NLP (Natural Language Processing) and AI capabilities. Other than answering our queries, it is able to search and order food, groceries and other items we need. Able to recommend the best price and online site to buy from.

c.     Google Home – Google Home is also voice enabled AI ChatBots and are enabled using NLP (Natural Language Processing) and AI capabilities. Other than answering our queries, it is able to search and book flights, movie tickets, hotels as well as suggest what is the best places to visit, with best time and price.

2.     Online Virtual Agent ChatBots – The other simpler form is the virtual assistant popup window we get on most of the websites greeting us and asking how can it help us. These are most common and seen across all business websites. Every sector and industry have their own ChatBots. E.g., Banking, HealthCare, eCommerce, Government Services, Logistics, Retail, social media etc.

3.     SMS Based ChatBots – There are also chatbots that are SMS based which are basically rule-based ChatBots that answer queries based on our questions and answers.

4.     Phone Call Based ChatBots – Phone call based chatbots are similar to SMS based chatbots but with voice enablement for communicating and handling user queries. During Covid Pandemic we have seen the use of these for managing online enquiries. We can also see them in use largely by banking, credit card and government services enquiry lines.

5.     Offline ChatBots – These are ChatBots used within organizations boundaries (E.g., HR, IT) and in some case for educational purposes by schools and universities. These ChatBots operate within specific knowledge domain to help manage queries from employees and students.

Now that we understand the type of ChatBots, Let’s understand the Pros (Benefits) and Cons (Downsides) of using them. 

Here are the benefits of using ChatBots,

1.     Improved 7×24 Customer Service – saving time and efforts for customers to find answers to their queries anytime anywhere with any device connected to internet.

2.     Standardized and automated processes – ChatBots make the respective customer service or query handling process fully optimized and automated for all possible scenarios. The process also undergoes continuous improvements and ChatBots continue to learn and feedback for corrections. Eventually making the organization more efficient and effective.

3.     Cost Savings – ChatBots can handle 1000s of customer queries at once using the same standard mechanism. This results in cost savings for the organizations’ operational costs as they will not need so many customer service agents working in multiple shifts and locations.

4.     Ability to Gather Customer Needs and Information – ChatBots can help gather customer needs and information through the questions and answers. The information gathered can then be used by sales, marketing and customer service divisions to engage with respective customers.

5.     Ability to generate leads – ChatBots can also help in generating leads on campaign sites and company websites by collecting interests and information of customers.

6.     Ability to Upsell and even cross sell – ChatBots can be setup to identify customer needs for products and services. Based on the identification, they can upsell and even cross sell products and services that can complement and better fit for the customer.

7.     Ability to sell extended warranty, accessories and CRP (customer replaceable parts) – ChatBots can be setup to identify customer needs for products and accessories. Based on the identification, they can sell extended warranty, accessories and parts to increase sell.

8.     Improved Compliance – ChatBots unlike humans are always well behaved in their answers to customer queries and every step of the discussion is recorded and audit log can be easily emailed to customers and stored in company logs for compliance purposes.

Here are some downsides and limitations of ChatBots,

1.     Limited Knowledge base – ChatBots can only handle limited set of queries and questions, as they are setup with a fixed set of keywords and questions identification and answers.

2.     Handle One Query at a time – In most cases ChatBots can only handle one question at a time. So if a user asks multiple questions the ChatBot will be able to answer only 1 question impacting user experience.

3.     Limited number of instances – ChatBots efficiency depends on the load and number of instances running parallelly. So organization must be vigilant to ramp up computing capacity for ChatBots based on their usage threshold.

4.     Complex Scenarios – There are complex scenarios in customer chat which ChatBot won’t be able to understand, answer and resolve. For such case the human service agents must keep a check on ChatBot discussions and step in to takeover such discussions.

5.     Need for Human Agent – It is normal that most humans would like to speak to a human services agent instead of ChatBots as conversing with ChatBots could take longer time especially on SMS and Phone based conversations.

6.     Language and Slangs – ChatBots understand normal language with correct grammar and slangs. They would not recognize certain slangs and language shortcuts that we normally use as humans.

7.     Over Loading – ChatBots can be attacked and kept loaded by hacking bots putting a lot of load on system capacity by opening many instances and asking many types of questions. Eventually impacting efficiency and effectiveness of the system and in some cases even crashing the system.

8.     Hacking – ChatBot platforms can be prone to hacking and hackers can alter the answers and language which can impact organization’s brand image as ChatBots are front face of organizations online presence.

There are many ChatBot software and platforms. Some of the famous ones are Hubspot, Zendesk, Intercom, Tidio, Mobile Monkey, Drift, IBM Watson and QlikSense. In addition, social media platforms like Facebook (Meta) also offer their rule based chatbots for business pages and ecommerce. Similarly, eCommerce Hubs like Shopee also offer their own ChatBot for online shops running on their own platforms. The top 3 Cloud providers Amazon (Amazon ChatBot), Microsoft Azure (Azure ChatBot) and Google (Google Dialog Flow) also have their own pay for use ChatBot platform offerings that can be easily built-up using scenario-based conversation flow configurations without any need of complex coding and development.

ChatBots are very much in use across the world by all industries and sectors. They are here to stay and they will become much more artificially intelligent in the upcoming years. AI based ChatBots combined with Deep Learning will open many more opportunities for their use to make our lives far more advanced, efficient and effective.

The Good and Bad perspectives of Deepfakes

Deepfakes have emerged strongly only in the past few years. The name comes from Deep (Deep Learning) and Fakes (virtual, imaginary computer visuals). Deepfakes are an existing image or video created using artificial intelligence (AI), machine learning (ML) and deep learning techniques by the computer system that can mimic to look & feel 98% similar to our actual image and videos.

Deepfakes are created using AI and ML to create visual and audio content that can easily deceive people. The key technology and learning methods used come from deep learning and involve use of generative neural network architecture for training machine and generating output. It uses autoencoders, decoders and generative adversarial networks (GANs) from deep learning. 

Here is a brief overview of the Deepfakes are created and operated,

Deepfakes Creation

1.     Deepfakes work based on an autoencoder which is part of neural network in deep learning architecture. 

2.     The autoencoder reduces a person’s image to a low dimensional latent space. 

3.     The latent space captures the key features of person’s face and body postures etc. 

4.     This is then used by the decoder to reconstruct the image from its latent representations.

5.     The decoder works as part of generative adversarial network (GAN). The GAN trains the output generator consisting of the decoder and discriminator.

6.     The generator constructs the image from the latest representation while the discriminator checks and determines if the image constructed is matching to the source.

7.     The model then places the reconstructed image and features on top of the person’s video or image to mimic like the real person itself.

8.     As the entire architecture is functioning with AI, ML and Deep learning the model continues to take feedback, learn and evolve and it becomes so perfect that it’s almost impossible to differentiate between original source and deepfake reconstruct.

Now that we understand how deepfakes are created and operated, let’s understand why they are worse than good for society and organizations.

Deepfakes are not just be used on image and videos but also for mimicking audio. Deepfakes are not just be used for one person but even the entire group of people can be mimicked in an image, audio and visual. This is what makes them very dangerous as we can’t easily interpret fake Vs real on our electronic devices.

Deepfakes are generally used for mimicking others. They are often used by bad people targeting celebrities and leaders. The internet is full of bad examples of deepfakes. Some are even so shameful that we can’t even imagine.

There are some good sides of Deepfakes too. On the good end deepfakes can be used for,

1.     Reviving memories of people who have left the world and bringing them to life with virtual images and videos of them.

2.     Reviving memories of patients with short memory and memory loss.

3.     Marketing and Creative agencies are using deepfake to save time and costs on advertisements where the celebrity can shoot the ad just once and the audio content of the ad can then be mimicked and targeted towards respective target audience in their language making it more personalized and effective.

E.g., one such ad was from the leading food delivery company that is targeting its customers in different states and cities in different language using the Deepfake technology. The Deepfake celebrity is mimicked so clearly and accurately that the viewers can’t make out any difference and ad became very personalized and highly successful.

4.     Mobile Phone and website based apps for online learning of concepts by mimicking the actual creators of those concepts. 

E.g., Einstein teaching his theories to school and college students using online learning videos.

Deepfakes require strong internet connections and generally with weaker connection the lag can easily show up that the video is fake. But as the technology improves and countries move from 4G to 5G as well as the AI platforms and solutions becoming stronger along with Metaverse possibilities, it can lead to serious problems for many areas.

The solution to protect ourselves from bad sides of Deepfakes is to be careful,

1.     Using your photos, voice and videos on social media and storage platforms openly.

2.     We must ensure everything is well encrypted and securely stored. 

3.     Always be vigilant in day-to-day life and be alert and aware while using internet. 

4.     Stay away from using open (unsecured) and free access wireless network points. 

5.     Ensure you always use strong password on all your devices and change it periodically this will ensure your devices and accounts are always secured.

6.     Stay away from mobile apps and websites offering Deepfake free trial or even paid trials.

Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL) and many other new technologies have many benefits for organizations, society and people. But its very important to have a solid and secured good governance on how these new technologies can be used for the greater good of all.

Understanding the perspective of Digital Twins

The need for digital twins was foreseen way back in 90’s when we started using computers for automation. In the past two decades our society and organizations have gone to great lengths in making hyper automation possible and we live in a gadgets world where every it connected and have some part of it managed through digital technology. We have actually started using Digital Twins meaningfully only in the past 5 to 7 years.

Digital Twins, when you hear these words for the first time, the first thing that you think is, it must be some sort of digital replica of physical things. You are half way there in understanding Digital Twins. 

Digital Twin is an exact virtual (digital) replica of physical product, object, system, process and even human beings to some extent. Digital twins is used for simulation, prototyping, testing and maintenance as well as for investigations of cause and effects for any major issues and risks we face in products, objects, systems, processes etc. 

Digital Twins are of three broad types based on their usage needs,

1.     Digital Twin Prototype (DTP) – As the name suggests this is not a real digital twin but a prototype and generally used for all types of digital prototyping giving a feel of the product, object, system or process.

2.     Digital Twin Instance (DTI) – DTI is an actual Digital Twin (virtual instance) of a  product, object, system or process. DTI is used for carrying our multiple scenario simulations and testing on the virtual instance.

3.     Digital Twin Aggregate (DTA) – DTA is combination of multiple instances (samples) of the same product, object, system or process. This is used to carry out detailed simulations and tests on aggregated instances. It is very useful for validating how the hundreds of thousands of instances of digital twins could work and what challenges and issues we might face as we ramp up or ramp down the instances, upgrades, maintenance and support. 

Digital Twins are used in 3D modelling, architecting, construction, manufacturing, retail, healthcare, automotives, aviation, shipping, smart cities and many more industries. Here are some major examples,

1.     Retail – Digital Twins are used in the retail industry to model consumers and study their behaviors to enhance user experience.

2.     Healthcare – Digital Twins are used in the healthcare industry for multiple areas,

a.     For products simulations and tests

b.     For patient simulations and tests

c.     For Hospitals and Clinics process simulations

d.     For staff trainings using digital twins

3.     Automotives – Digital Twins are used in the automotive industry for parts simulations as well as product simulations and testing

4.     Manufacturing – Digital Twins can be used to simulate how industry and manufacturing lines can perform in various scenarios.

5.     Architecting – Digital Twins can be used for 3D modeling and architecting simulations and tests.

6.     Aviation and Shipping – Digital Twins can be used for aircraft, spacecraft and ships simulations and testing

7.     Construction – Digital Twins can be used for major construction modelling and simulation testing.

8.     Smart Cities – Countries and Governments can use Digital twins for simulating smart cities and how they can meet their growth and sustainability goals.

Microsoft Azure Digital Twins Overview for Smart Cities

The need for digital twins is clear from above examples. Please also check above picture of Microsoft Azure Digital Twin Overview of a Smart City. Digital twins help in ensuring before we launch the new product, object, system or process as well as before we upgrade we do a thorough virtual simulation and testing to understand the quality, impact, issues and risks. The outcome helps in addressing all the areas sufficiently before launching. Here are some real examples specifying why we need digital twins,

1.     Imagine the impact for an airline company of recalling its aeroplanes for any parts defects post product launch.

2.     Imagine the impact of healthcare device recall post launch and how much it will impact organization’s brand and finances.

3.     Imagine an automotive company recalling its cars to address defective part which did not get sufficiently tested.

4.     Imagine a space rockets company having its rocket failure due to insufficient simulation tests.

5.     Imagine a trading company launching a defective trading app and how much of impact it can create for its customers.

6.     Imagine a construction company building a construction project without full simulation and tests.

These are only few examples why we need digital twins to improve simulation and testing with virtual instances. There are many more examples across all industries and sectors.

Here is a quick understanding on how the digital twins are setup and how do they function,

1.     Digital twins are setup and created as virtual instance(s) that get(s) data feed from the physical instance(s). 

2.     The data is fed using system interfaces or data loads for specific scenarios simulations and tests. 

3.     The results are collected and recorded from different parts of the respective product, object, process or system.

4.     The results are collated and visualisations are created for concluding on insights.

5.     Insights lead to actions and possibly some more simulations and tests until the respective product, object, process or system meets all criteria.

Digital Twins can be created using company proprietary or market standard platforms. Microsoft, Nvidia, IBM, Siemens, Cisco, Oracle, Ansys, General Electric and many other companies offer Digital Twin platforms, solutions and services.

Digital Twins are an essential part of Industry 4.0 and Digital Transformation for organizations’ digital value chain. Digital twins are also closely attached and work in conjunction with the IoT (Internet of Things) and IIoT (Industrial Internet of Things) ecosystems. Digital Twins can also be understood as Metaverse made for the reverse engineering of investigations and simulations.

Digital Twins are very useful for organizations, industries, sectors and countries. As it costs to have a Digital Twin, it is important to not apply Digital Twins for every product, object, process and system. Digital Twins should be applied to only mission critical, high impact and high risk products, objects, processes and systems.

Digital Twin are a necessity for organizations as their products and value chain becomes digital through digital transformation and digital business models. In near future more easy to use platforms, solutions and services will emerge that will make using Digital Twins simpler and faster for all.

The What, How and Why of Internet of Things (IoT)

The Internet of Things (IoT) is a term that came in real use more than a decade ago. It arrived when our mobile devices became more powerful and gained ability to connect to internet and communicate information. IoT opened a new world of connected eco systems for tracking, monitoring and reporting on all possible aspects of our lives.

IoT is the ability of mobile devices, scanners, sensors and tokens to connect to internet or to an internet enabled device to share useful information collected by the device, scanner, sensor and token. The information can be then be collated and stored on a cloud hosted systems that can then analyze the data and share insights and actions.

The IoT ecosystem consists of many devices connected to the IoT Hubs which are connected to the IoT Cloud Platform that collects and analyzes data which is accessed by the organization using their devices to gather insights and make decisions. The IoT ecosystem can be briefly described as follows,

Internet of Things

1.  IoT Devices – IoT devices consists of sensors, trackers, scanners either on their own or as part of products like smart band, smart watch, smart cars, smart phones, smart thermometers, smart weighing machines and many more. The devices are built in to communicate with IoT Hubs using protocols like IPv6, LoRaWan, ZigBee etc.

2.  IoT Hub – Each IoT device has ability to connect to the nearest possibly IoT Hub. The IoT Hub has the ability to receive and translate the data from IoT devices. It translates and tags it properly as well as encrypts it to send it to IoT Cloud Platform.

3.  IoT Cloud Platform – The cloud platform is a large storage space with suite of applications to store, analyze and interpret data patterns. It prepares the insights and visualizations for the organization to access the data and results to make effective decisions.

4.  User Devices – The data and insights stored in the IoT Cloud platform can be accessed by respective authorized users using mobile, tablet or PCs to make decisions and take necessary actions as well as do required communications.

There are many IoT Communication Standards as different manufactures and industries need different types and standards are not fully underpinned by one global entity. Here are some of the known standards that are generally used by IoT devices for communications.

1.  Long Range Wide Area Network (LoRaWAN) is a protocol for wide area networks (WANs) designed to support big size networks. It is generally used for large scale environments like smart cities with ability to connect and enable communications for millions of low-power devices.

2.  Low-Power Wireless Personal Area Networks (6LoWPAN IPv6) – It is an open standard that enables low-power radio frequency to communicate to the internet, includes Bluetooth Low Energy (BLE) and Z-Wave used for home automation.

3.  ZigBee – It is also a low-power, low-data rate wireless network used mainly in industrial settings.

4.  LiteOS – It is an operating system used for wireless sensor networks. LiteOS can be used with  smartphones, smart watch, smart bands, smart cars, smart homes and intelligent industrial.

IoT operates in the cloud (internet) so it needs a robust architecture platform and framework. The world known cloud providers have also their own cloud-based platform and framework for organizations to effectively operate IoT ecosystem. Here are some well-known examples of IoT platforms and frameworks.

1.  Brillo / Weave by Google – Google has developed Brillo and Weave platform for rapid implementation of IoT applications. Brillo is Android OS based platform that helps develop apps for managing and tracking embedded low powered devices. Weave is messaging protocol used between the IoT device and google cloud.

2.  AWS (Amazon Web Services) IoT platform – It offers services that enable interaction with IoT devices and enables ability to receive data from IoT devices. The platform also doubles up in analyzing the data and sharing visualization as well as insights for decision making.

3.  Azure IoT platform by Microsoft – The Azure IoT platform offers services that allow interaction with IoT devices as well as ability to receive data from IoT devices. The platform also offers data analysis, visualizations and insights from the collected data.

4.  HomeKit for iOS by Apple – Apple devices have several sensors as well as products like Air Tag, Smart Watch and Air Pods have several IoT services. The HomeKit is proprietary platform that can be used for iOS apps development to interact and receive data from Apple devices and sensors.

Here are some examples for you to better understand what IoT can do and how it helps in efficiency, effectiveness and continuity of services, 

1.  A sensor in the rental printer can communicate cartridge usage status and auto dispatch refill/replacement cartridge in time to ensure continuity of the printing services.

2.  A sensor in the smart watch can monitor and detect your health and predict your health conditions improvements over a period of weeks and months.

3.  A sensor in the smart watch can monitor and detect your fall and automatically call the emergency services to support you.

4.  A sensor in the logistics pallet can track and share the exact location of the pallet throughout its journey from warehouse to delivery. It can also help track areas where it takes longer than usual for clearance and movement of stocks.

5.  A sensor in the car can track your driving speed and alert you when you are crossing a certain speed limit.

6.  A sensor in the mobile phone can monitor battery health and suggest when it’s time to charge and or change batteries.

7.  Smart sensors in the manufacturing production line can track the performance and effectiveness of the production line and report alerts when its either too fast or too slow.

8.  Smart sensors in the retail shop can track how many customers come to the product booth, how many spend time trying the product to check the effectiveness of instore excellence and promotions.

9.  The sensors in the aircon can alert service provider for gas top or air con cleaning based on its usage.

10.  The sensors in the hospital MRIs can track and auto order parts replacement in advance to avoid any downtime of MRIs and impact to patients.

11.  The smart patch sensors put on patients’ body can track and monitor their heart rate, oxygen level, blood pressure and diabetes. The data can be then collated and accessible for the patient and care givers for review and actions.

Above list was just a few examples that you can relate easily. The IoT examples and usage is far vaster and there are many more examples. We use IoT across most of our home appliances, entertainment devices and health devices.

IoT has many benefits for organization. Benefits range from automation of repetitive tasks to productivity increase, from insights to costs savings and faster fact-based decision making. Here is a brief overview of key benefits from IoT,

1.  Automation of tracking and monitoring activities without any room for human errors.

2.  Time savings and productivity gain

3.  Cost savings and 7×24 monitoring, tracking and reporting

4.  Faster, Accurate and Facts driven insights and decision making

5.  Improved quality and customer experience

IoT also has some areas to be well taken care, as anything that is on internet is prone to possible cyberattacks and hacking. Here are a few areas to look out for,

1.  The data from IoT devices if not well encrypted can be hacked and even manipulated by hackers. 

2.  The IoT devices can be hacked or virus implanted which can make them malfunction and report bad data.

3.  IoT devices and sensors must be managed for effectiveness as over a period of years the organization could have thousands of such devices all over their customer base and its very difficult to track and manage all of them with speed.

4.  As the amount of data increases its important for organizations to have a policy on how to archive the data periodically to avoid systems from slowing down and increase in costs due to increase storage and computing power.

5.  Regular firmware upgrades and security upgrades for IoT devices and systems to avoid having any security holes making them vulnerable for cyber-attacks.

6.  Another important area for IoT is to ensure full adherence of Data Privacy and regulatory compliance especially for personal, healthcare and confidential data.

IoT has slowly and silently slipped in our daily lives. We all use it every single day and benefit from it to make our lives better. The usage is wide and almost every industry and every person uses IoT in one or the other form. The most common form is smart phone, smart watch and smart bands we use daily.

IoT devices and ecosystems will continue to remain and become even more smarter in the coming years to make our lives super effective and highly efficient.

11 Important areas for effective Digital Transformation

Digital Transformation framework and digital value chain deployment is a huge effort for large organizations as there are too many factors to be taken care for becoming successful.

Organizations’ depending on their size need a multiyear program to full achieve the transformation. For smaller and medium size organizations it is relatively simpler to achieve due to their smaller size and much lower complexities involved.

Alongside the Digital Transformation Framework, we must ensure entire digital value chain (value stream) is well equipped to function smoothly. This requires taking care of following 11 important areas while doing digital transformation of your digital value chain,

Important Areas of Digital Transformation
Important Areas of Digital Transformation

1.     Stakeholder Needs – Identify, Involve and Evaluate that all your stakeholder needs both internal and external are met. This must include your partners (e.g., suppliers, supply chain, marketing agencies etc.)

2.     Digital value chain – Clearly articulate your digital value chain. Define and measure your expected outcomes. Easier said than done, this will take a lot of efforts from all in the organization, especially leaders and management team. Focus is not just on what is the value chain but on what outcomes you want to achieve, for whom and how it will be measured for success.

3.     Competition – Evaluate what your top 3 to 5 competitors are doing and how digital transformation can give you an edge over them. Also, to maximize results, check where your specific industry or market is going and align to it. Use market research and industry papers from top consulting firms and government.

4.     Digital Work Culture – Define and Design work culture changes needed to adapt the digital mindset across the organization. Digital transformation is much more of work culture change then simply changing systems and processes. To be digital the culture must have aspects of digital behaviors embedded and articulated with examples.

5.     Balance Digital Capabilities – Ensure proper digital technical capabilities are in place with a good balance of inhouse and external resources/partners to deliver and run the ongoing digital journey. Also do a check whether your partners (SCM, Suppliers, Retailers, etc.) are well equipped to move in the digital space. There is no point having digital transformation done while rest of your ecosystem is not ready to adapt.

6.     Digital Capabilities Readiness – Establish full set of digital capabilities in terms of people, process and products. The emphasis here will be on people, to check and ensure digitally trained people are onboard to manage the value chain. Existing people can be trained. New people or positions should be opened and hired (e.g., if you don’t have digital marketing leader, you will need one for sure, you don’t have digital data analysts then know that you will need them too). HR must have a Digitally savvy TA Manager that really understands Digital needs and how to acquire the right talent. Lastly there has to be a good balance between how many people you really need inhouse and how much you can manage using your external partners (for Manufacturing, Marketing, SCM, Finance, Procurement, HR and IT).

7.     Partners are important – For digital transformation to be successful all your partners (e.g., suppliers, supply chain, marketing agencies etc.) must be fully onboard and equipped to move towards digital ecosystem. Often partners technical capabilities and system landscape standardization and connectivity for seamless transactions management becomes a major issue. This is one of the most challenging areas where organization’s core systems are not seamlessly connected using industry standard messaging platforms resulting in lot of manual work and Lean waste if not identified and handled in advance as part of the transformation. Challenge is generally faced with manufacturing suppliers, supply chain partners and eCommerce hub (shop in shop) partners.

8.     Big Data and Insights – One of the other major areas to watch out are related to Data and Insights. Going Digital means, the organization will now have a lot of data in various systems and these data must be used to generate meaningful insights using near real time online dashboards. Key value indicators (KVIs) and Key performance Indicators (KPIs) as well as various aspects of industry 4.0, digital marketing and online sales must be well defined and measured for growing the business and creating sustainable value.

9.     Compliance – As you embark on the Digital value chain, all the compliance needs must be redefined as per the digital value stream. Data privacy and online compliance regulations from global as well as local country specific compliance must be fully acknowledged and implemented.

10.  Digital Command Center – The more digital the organization becomes the more the organization would need digital command centers to check and manage their online presence and customer touchpoint platforms (likes of company websites, campaign websites, facebook, youtube, twitter, Instagram, Pinterest, LinkedIn and many more). One mistake on these platforms can cost a lot to the organization and its brand image globally (e.g., it can directly impact share prices).

11.  Cyber Security – Going digital brings a lot of positives but it also has its own negatives. If systems are not well architected and digital platforms are not secured with cyber security. System authorizations and authentications must be handled with end-to-end encryptions. All people must be trained in understanding basics of cybersecurity related to use of strong passwords, keeping the devices locked when not in use, authorizations are maintained and segregation of duty conflicts are vouched to ensure right people get right information (nothing more and nothing less). All servers, devices, systems and digital equipment are periodically scanned and patched for avoiding virus attacks and hacking.

Digital Transformation is like implanting a new brain in the organization (body) to make it more effective and efficient while still keeping its vision & purpose (soul) intact. Digital transformation helps to change the strategy of an organization on how it can unlock its full potential to achieve its vision, purpose and goals (north star).

The Digital Transformation Framework

Digital Transformation is a large commitment for organizations to change how it works, thinks and generates its business. Everything has to change to think, work and act digitally across the entire organization.

For being successful in making the leap for digital transformation, the organizations must follow a solid framework along with talents from both inhouse employees and outside partners. The digital transformation scope must also cover the landscape of the business partners to ensure they are equally digital equipped to work and deliver seamlessly.

The digital transformation framework provides a well thought through roadmap steps that can be applied to any organization to achieve transformation goals with agility, speed and accuracy. Digital transformation framework along with organizations’ digital value chain helps define organizations full path to succeed digitally.

Let’s do a quick run through on Digital Transformation Framework steps and supporting areas for achieving first time right successful transformation across the organization.

Digital Transformation Framework
Digital Transformation Framework

Step 1: Understand Impact of Transformation – Organization must clearly articulate and know what’s in it for them, why do they need to transform and what will be its impact across the organizations, taking in account all internal and external stakeholders involved. Eventually part of this is what the board of management will use for communicating towards the entire organization.

Step 2: Map the end to end As-Is value stream – This is an important step where the organization lays down its as is value stream on how their current business works. Full End to End mapping must be done involving talents from all divisions. It will also be useful to map the processes up to 3 sub levels deep to make it meaningful to understand processes and eventually gaps and improvements.

Step 3: Map the end to end To-Be value stream – Once the As-Is flows are mapped the important and often difficult step is to map the To-Be value stream. This can be achieved by taking input and lead from external partners hired for digital transformation. It can be also achieved by identifying gaps, manual steps, Lean waste in the processes and repetitive as well as duplicate work. The To-Be process map should result in automation and standardization of work. It should improve productivity and efficiency as well as competitive technology edge.

Step 4: Create a full list of Gaps and Improvements identified – Ensure the list is prioritized and owners assigned to ensure who would own and help drive which gaps and improvements. Eventually it will be a team work and many more people including technical IT inhouse and outside team members will be involved. The prioritization will set the tone for which are the important must have items that will create most of the impact and must be done first. As the list could be long, MOSCOW method can be applied to come to a shortened must have list.

Step 5: Create Transformation Roadmap and Blueprint – The information collected from step 1 to step 4 can now be converted to a transformation roadmap and blueprint documents. As this will be a long program involving multiple projects and tasks with dependencies, it will need to be well defined and executed with Lean, Agile and Design Thinking methods. The roadmap and blueprint will also ensure all compliance and audit related activities are well covered in the scope.

Step 6: Setup Multidisciplinary Program Team – Once the roadmap and blueprints are approved, the core program team can now setup the full Multidisciplinary program team required for various phases and projects to initiate execution. Inhouse talents as well as external partners must be onboarded to form the team.

Step 7: Manage Communication, Execution & Change – Initial communication of the program roadmap and blueprint must be shared widely across the entire team. The Daily dashboards and tracking and weekly/bi-weekly steerco updates and measurement metrics along with the definition of done must be communicated. The execution standards like Agile (Scrum or Scaled Agile) and Lean methods must be well communicated and ensure the team is trained to operate seamlessly. This is also the step where most of the technical execution and readiness takes place. So this step will require a lot attention and energy from everyone in the program.

Step 8: Deploy Solutions & Services – Once various tracks and projects have completed their execution, the important step of testing, cutover and release management comes up. In this step user acceptance testing, rework and sign offs on definition of done are achieved for all projects and for the entire program as a whole. 

Cutover activities planning including data migration and phase in / phase out of systems / solutions / services is planned. User trainings are done for all. The interdependencies including exact sequence, date and time along with primary and backup owners for each cutover activity are confirmed. This also involves getting our partners ready to cutover and release. Communication of release and cutover command center for effective release management are put in place. Multiple checkpoint meetings and Go/No-Go signoffs are achieved. Point of no return is clearly mentioned and underpinned. 

Go Live is achieved and first set of end-to-end golden transactions are carried to ensure entire process, people and systems are working as expected. Upon completion of the golden transactions the ramp up towards business as usual gets kicked off and generally with in 1 to 2 weeks of go live full ramp up and business as usual stage is achieved. Alongside from the day of go live 24×7 support to fix any teething issues is put in place. All transactions are tracked and progress reported on daily basis to ensure full control and attention on priority basis to keep the business smooth. This is also the step where best teams and team members are awarded/rewarded for their efforts. Overall team’s success celebrations are done.

Step 9: Measure Continuous usage and improve – In general the program team and its support structures continue for 6 to 8 weeks post go live to ensure a full cycle of 1+ month or even 2 months are done before concluding the closure of the program. This is also the step where community for practice (CoPs) are setup for users to interact with key users and subject matter experts. CoPs are online discussion forums of specific function, division or process experts and users to interact even after the program is closed.

Change control boards are also implemented to ensure changes are submitted, reviewed and approved through a board consisting of multi-disciplinary team that can approve/reject the changes. It serves both the purposes, one it to ensure continuous improvements are done to keep the business running and second is reject any unwanted changes that can lead to Lean waste again.

The Digital Transformation Framework ensures a successful implementation of organization’s digital needs of installing a new brain that can change how the organization functions as a whole, how it innovates (thinks) and how it operates (acts) to achieve its results.

Do look out for my second article covering 9 important areas that must be taken care for effective digital transformation and to make it successful.

The What and How of becoming a Technopreneur

The word technopreneur is made of Technology and Entrepreneur. The new startups as well as existing organizations creating new innovation and products using technology is called technopreneurship.

The process of becoming a technopreneur requires entrepreneurs to know about what problem they are trying to solve, what is their value proposition, who are their primary customers, what assets and resources will be required, how will the funding and investments be taken care as well as what will be the time to market and product roadmap. 

There are several other important areas to check (e.g. competitors, regulatory compliance etc.) for a successful technopreneurship. It is easier said than done as often the devil is in the details and you require a lot of time to think through all the details.

The technopreneur’s journey can be started with following steps,

1.     Create a Lean Value Proposition Canvas – Detailing out each area and segment of the business and answering the most important questions that matter most to start.

2.     The value proposition can then be refined by presenting it to various stakeholders and taking their point of views.

3.     The next stage is to use an ideation workshop to initiate design thinking steps and execution for creating ideas, validating them and creating rapid paper prototypes, brainstorming and seeing the product from the best and worst customer’s point of views using surveys and interviews. This stage could take some time unless it is planned and timeboxed in a manner that will work well.

4.     The outputs and feedbacks received from Design Thinking stage can be fed back to further strengthen the value proposition and making it more crisp with every round of updates.

5.     Next the idea and solutions have to be presented to the board or angel investors to get their buy in to initiate the execution process.

6.     Again, execution requires a proper organization setup with right resources of each type which could take some time unless those are pre-identified in parallel which is normally not the case and might not be possible as well.

7.     The real work starts from this step when the organization starts working and must adapt Lean and Agile processes which can be flexible yet solid as a foundation to get things done.

8.     The cycle of daily, weekly and monthly progress reviews using online dashboards and decision meetings with stakeholders begin to ensure we make progress as per the plan and report issues and risks requiring action and attention from stakeholders and board members.

9.     While all this is in progress, the team needs to prepare for go-to-market strategy for launch and how to handle competition and regulations in the market. Next releases and speed to market along with desired quality are very important to make it successful.

Technopreneurs are all around us, some of them are big brands while others are in the process of becoming one and some others at start up. There are many examples (like apple, facebook, google, WhatsApp and many more) of successful technopreneurs who started and achieved great success.

Important to note that while there are several examples of successful technopreneurs, there are also many more examples of failures where technopreneurs are either ahead of time or behind time or addressing wrong market segment or having bad user experience etc.

The technopreneurship journey requires thorough checks and solid commitment to keep on going in toughest of times to make it. There is a lot from technopreneurs that went ahead of us and how they have survived the journey as it requires long term commitment and lots of trial and errors to get it right.

The entrepreneur and technopreneur lifecycles are almost similar except that technopreneur innovations, solutions and services are fully technology based and they change and transform with much larger speed then other innovations, solutions and services.

Achieve Hyper-automation using Robotics Process Automation (RPA)

Robotics process automation (RPA) is about using computer system for automating all possible repetitive and logical or rule-based tasks. These include tasks that humans have to perform repetitively daily/weekly/monthly.

RPA enabled systems and processes can automate almost all functions and industries repetitive and rule-based tasks. Important is to ensure that the tasks and business processes chosen for RPA are firm best practice and not changing often as all steps and scenarios have to be programmed and configured for successful execution.

Computer systems can be trained to execute tasks round the clock, resulting in a lot of productivity gain and time savings as many of these tasks can be done even on holidays, weekends, after office hours etc.

RPA can bring huge benefits in long run for organizations. Benefits start from the very day RPA is put to use. The benefits seen in RPA usage are as follows,

1.     Increase in productivity gain due to full process automation execution by RPA

2.     Increase in accuracy as there will be no more human errors and machine will do the activities exactly as per the rules laid down.

3.     Cost reduction as the organization will need a smaller number of people for executing the same set of tasks. 

4.     Volume of tasks won’t be an issue as it will only cost in terms of adding one or more computer system. 

5.     The entire process becomes scalable and flexible as the execution resources (computer systems) can be increased or decreased with speed within hours.

6.     Business process throughput time and turnaround become predictable, quicker and possibly increase over time as network and computer systems are getting faster and faster.

7.     RPA also ensures a full audit trail recording and error reporting by email and management report logs. This makes audit and regulatory compliance easier as all details remain fully transparent.

RPA can be envisioned as robotics in the manufacturing production lines where the robotic arms do complex assembling and product manufacturing automatically with full accuracy and agility. Robotics are used for manufacturing while RPA is used for computer systems business process automation and execution automation. RPA can be applied to many functions and areas. Here are some examples,

1.     Customer order processing – RPA can help automate execution of entire customer order entry and processing steps in online (eCommerce, B2B, B2C, D2B, D2C, EDIs etc.) as well as offline (ERP systems, POS systems, CRM systems etc.)

2.     Logistics and Delivery processing – RPA can help automate execution of entire logistics and delivery processing steps by ensuring all systems and records are well updated round the clock (7×24 hours).

3.     Month End Closing – RPA can automate execution of month end closing activities of finance team by reducing their burden of manually updating 1000s of records and reconciling them to find the gap and adjustments etc.

4.     Banking Systems – RPA can automate execution of various (like statement and account reconciliation) repetitive banking tasks by reducing workload on bank employees, allowing them to focus on customer needs.

5.     Finance systems (Payroll, Claims, Assets etc.) – RPA can automate execution of Payroll processing, claims processing, assets depreciation processing and many other processes.

6.     HR and Customer Service Systems – RPA can automate execution of customer service-related tasks and processes like call center operations, complaints processing, service center claims processing, customer insights processing etc.

7.     IT systems and support – RPA can automate execution of many IT tasks and processes like, helpdesk operations, infrastructure and system support, IT security management etc.

Like all projects RPA implementation must be also handled as a business project with clear business case, scope and goals. It’s important to upfront understand,

1.     What processes are we planning to automate?

2.     Why are we choosing these processes?

3.     How are we going to execute, for how long and how do we communicate about this?

4.     Who all need to be involved and communicated?

5.     When will we complete and measure benefits?

There are many RPA tools and organizations that help in deploying these tools. Major players as per Gartner’s magic quadrant are automation anywhere, blue prism and UiPath. Here are brief understanding of each,

1.    UiPath – It is used widely as it works smoothly with drag and drop features and built automation components reducing the need for customisation and development. It is highly preferred and used by many world known organizations for automating repetitive tasks.

2.    Automation Anywhere – Automation anywhere is owned by Microsoft and generally preferred for deployments across big enterprises. It can address wide range of complex automations. This is also largely used as its under Microsoft and equally efficient.

3.    Blue Prism – It is in many ways similar to UiPath having drag and drop features to get the automations done. It is used by medium and small enterprises for automations and it offers programming using C sharp language.

It is very important to choose a right fit for the organization keeping the long-term view in mind. As each of them have their own pros and cons in terms of features, time to market, costs etc.

RPA is not new for most organizations as all major MNCs have deployed RPA in one or the other part of their business, often more in Finance, Supply Chain, Human Resources and Information Technology divisions.

There is still a lot of room for automating repetitive tasks in all pockets of many organizations. The more we automate the more it makes the organization productive and agile allowing them to divert all their energy and focus on their customers, solutions and services.

Leading your way with Digital Transformation

Digital Transformation is often misunderstood as equivalent for technology adoption. Digital Transformation has a much bigger and broader purpose for any organization embarking on it. It is a multi-year journey and often continues even beyond the decade as the change in technology landscape continues to evolve itself rapidly.

It has become a necessity for all organizations to ensure they are continually digitally transforming as a large percentage of customers and consumers are digitally savvy and have a much more powerful mobile computer (mobile phone) in their hands. Generation X, Y and Z are comfortable using internet for searching and finding solutions and services online as their first and most important step.

The key purpose for Digital transformation is meet the need of customers and consumers, allowing them to find organizations solutions and services when they want and wherever they want it. Allowing them full flexibility of time and device and giving them ability to find, compare and choose the solutions and services that will best meet their needs.

Digital transformation impacts the very fabric of an organization by seamlessly and securely connecting all stakeholders with each other and automating various touchpoints of all stakeholders both internal and external. It needs a complete change in organization’s culture, thinking and ways of working where entire organization has to think and do things online digitally. It creates an entire digital ecosystem where organization, its supplier and customers of all types able to interact and do business using digital technology solutions and services.

The focus is largely on the outcome of all this for customers and consumers. The focus is on improving productivity, making the processes easier, seamless & automated, increasing time to market, increasing accuracy and ensuring full compliance across the end-to-end process of innovation, design, build, test, deploy, market, sell and support lifecycle of solutions and services offered by the organization.

Digital transformation in general has large benefits across the entire ecosystem for all involved stakeholders. Important is to understand that it is not a small undertaking of only some areas or divisions. It is certainly not about technology adoption. It is about bringing an end-to-end change on how any organization is designed to operate digitally and organization’s ability to adapt to the changing world of technology.

Digital transformation requires organizations to create new business models, new operating models and relook at upskilling employees or bringing new talent with desired new skills to operate the new business and operating models. The change starts from top and it requires the board of management to think and live digital. All the important organization metrics must be made digital and as real time as possible. It requires full commitment from all tiers of management staff to ensure they walk to talk of becoming digital.

Digital Transformation applies to all industries. Every industry and business sector need digital transformation. Here are some names Banking, Consumer Goods, FMCG, Healthcare, Investment, Insurance, Manufacturing, Logistics, Retail, Tours and Travel, Utilities etc. The list is long and digital transformation has been even adopted by governments to have better reach, communication and delivery of services for their citizens, business, employees and people.

Digital transformation means digital landscape and digital landscape means underlying new technology platforms, systems and gadgets that enable to seamless automation and user experience. Here are some of the new technologies used to build the digital landscape for the organization.

1.     Artificial Intelligence (AI) and Machine Learning (ML)– AI and ML systems are of many types where the systems handle complex set of data and help to get predictions and insights.

2.     Big Data and Analytics – Big Data and Analytics systems are meant to collect the data, clean it, structure it and feed it to create analytics for timely and meaningful decision making.

3.     Cloud Computing – Cloud computing platforms are generally used to deploy systems, store data and securely access them online from anywhere at any time. These are highly scalable and flexible pay per use platforms ensuring high reliability and speed.

4.     Internet of Things (IoT) – These are generally sensors and gadgets attached to product, machines and packages for tracking certain aspects and insights like usage, temperatures, date and time stamps, pictures, scanning etc.

5.     Robotics – Robotics are largely used in manufacturing and industry 4.0 digital transformation initiatives to automate production lines. Robotics are also used now a days in restaurants, food courts, warehouses, air ports and ports for automating food making, product handling (pick, pack, ships) etc.

6.     Robotics Process Automation (RPA) – Robotics process automation is used for automating business process execution. The entire process is handled by computer systems instead of any human interventions ensuring speed, scalability and accuracy.

7.     Social media and Digital marketing – Social media platforms and digital gamification are used for reaching customers through online campaigns, digital platforms and online shopping malls and hubs. It involves use of Metaverse and other gamification techniques to engage customers and sell their desirable products.

8.     Blockchain and Digital Currency – Blockchain is used in fintech for digital assets and digital currency management. Blockchain also have possible use cases in logistics, healthcare industries, and online auctions tracking and maintaining of records. Digital currency will open a lot of new opportunities for all. There is a need to move in this direction as our customers and we ourselves have been using less and less of physical currency. Everywhere we are able to pay online. Governments and monetary authorities are looking into this as it will ease the burden of printing and maintaining so much of physical currency across the world.

9.     Augmented, Virtual and Mixed Reality – Augmented, Virtual and Mixed Reality solutions open new doors for gaming, online training and learning, online try (visualize) and buy solutions. It helps create a virtual world which feels like real experience with real to life digital environments.

Digital Transformation as the name suggests is a transformation of an organization, its entire ecosystem and it’s a portfolio of offerings (products, solutions and services) using digital new technology solutions.

Many organizations have gone through digital transformation and some think it’s over for them but its far from over. It’s just the beginning and the transformation must continue as the world evolves and as the customer needs change.

Understanding Machine Learning and It’s How’s!

Machine Learning (ML) is part of artificial intelligence. In the background of most AIs there is machine learning at play. The term machine learning emerged in 1980s when computers started gaining their grounds. As the them name suggests machine learning, it is programs that help the machines learn and do statistical analysis and give results on probabilities and possible outcomes based on data.

Machine learning (ML) helps analyze large volumes of data based on several mathematical algorithms and share possible probabilities and possible outcomes within minutes. ML gives results based on algorithms and the results vary based on conditional variables and algorithm types we apply.

The results can’t be always called accurate because a lot varies based on what parameters and algorithms are applied to the data set. It also varies based on amount and quality of data used.

ML is used on large volumes of data which are impossible for humans to analyze with speed and accuracy. Machines can do this based on rules and programs set to produce results with speed. We can run the data through multiple algorithms and collect all the insights. Based on the insights collected further slice and dice can be performed and then we can decide the based way forward.

ML can also be used on any type of data, so not just text and numbers but also images and videos. In general, the ML process starts with,

1.     Cleaning the data set to ensure its consistency and no missing data. 

2.     Dividing the data set into training data set and data set for analysis.

3.     The training data set is used to train the machine learning algorithm on how to identify and what results to be expected. 

4.     The outcome of training data set is the trained model. To this model we feed the test data set to get the desired results. 

5.     The systems classify the test data set based on the patterns learned and logic used as part of the trained model.

6.     The prediction results can be verified with the trained model outcome to see the consistency. Based on the consistency the results and prediction outcomes can be accepted.

The machine learning is of 3 types. Namely Supervised Learning (SL), Unsupervised Learning (UL) and Reinforcement Learning (RL).

Supervised Learning (SL), as the name suggests, it is supervised means its trained with the labelled data set for identifying data sets and patterns to produce results based on the trained data set and other rules and algorithms to produce predictions. Supervised Learning (SL) works best for the classification problems, E.g., if we feed orange and other fruit images to the model as test data set along with the label specifying ‘its orange’ and for other fruits we ask it to label as “not orange”. Once the model is trained, we feed in the test data set with all fruits to check if it can identify and categorize fruits correctly in the outcomes.

Unsupervised Learning (UL), as the name suggests, it is unsupervised means it learns on its own from unlabeled data set. The algorithm automatically identifies various patterns in the data set and share the prediction outcomes based on the same. The full dataset if fed as input in the Unsupervised Learning (UL). UL identifies and creates its own group of data set based on various data attributes. E.g. if we feed multiple images of fruits to the data set, it can identify and group them based on attributes, like color, shape, weight, size etc. It will not know that this is a fruit or not but it will still efficiently group it and share the predictions and outcomes based on the data set attributes.

There is hybrid approach of combining Supervised and Unsupervised learning as Semi-Supervised learning where both SL and UL are used as certain attributes of dataset can be classified and trained while other remain unclassified and predicted using unsupervised learning. E.g. in the dataset if we know oranges clearly then we can train the model for identifying oranges accurately under supervised learning while all unknowns can be fed to unsupervised learning and that can group it and give predictions based on other attributes and features available in the data set.

Reinforcement Learning (RL), this works through a reward system where the system is rewarded for all right predictions and outcomes. This allows the system to learn based on its own actions that lead to rewards or penalties. Rewards are given for right predictions and penalties for wrong predictions. E.g., in a robotics factory every right action done by robot gets the reward and wrong actions gets the penalty. This allows the robotic system to learn the right and wrong actions based on the incentives (feedback) received.

Machine Learning (ML) has several algorithms and methods that are applied on the data set to build the complete model and share predictions that are impossible for human brain to manually achieve with in time limit of minutes and even hours.

Machine Learning (ML) is used for seemingly difficult predictions and probabilities to make effective decisions based on patterns identified by machine. Decisions like, 

1.     How many customers we must keep and which customer segment is most important for an organization in long term.

2.     Based on trends of past 2 decades when will be the predicted stock market crash.

3.     What age group purchases similar set of products

4.     We should tie up with which brand to combine our product bundles to increase sales

5.     Will Heart attacks increase and by what percentage

The results of the ML model can be verified using various performance metrics scores. In general, 6 performance metrics scores are checked to make a decision on viability of outcomes. The methods used are confusion matrix, F1 score, accuracy, precision, recall, and specificity.

As far as the tools are considered many of the ML capabilities are built in MS Excel. There are also standardized tools like Microsoft Azure ML, Google ML, IBM and many other preoperatory software tools. ML can be also managed using Python, R, Java, Julia, LISP programming languages.

Machine learning (ML) is heavily used in our day-to-day AI applications. ML is used by many market research, commercial, industrial organizations for identifying patterns and making difficult decisions. 

There is a lot of room for organisations for using ML to its fullest to make decisions. Opportunities are immense for all organizations and possibilities are wide spread across industries as we have big data available.

Understanding Artificial Intelligence and where it’s leading us!

It’s been almost a decade since we started recognizing the word artificial intelligence (AI) in our day-to-day life. AI has been around since many decades already but as it was more in the form known to us as computers and super computers only.

In the last decade it started to emerge more strongly as we started using artificial intelligence more widely in the form of search which after some years became voice enabled and even image enabled.

Artificial intelligence in simple terms is intelligence exhibited by machines and computers to do human like thinking, calculations, understanding and even day to day activities we carry out.

As the name suggests, artificial means not real human but it can more or less do everything that a human can do. Although we are still far from having 100% human like AI machines and robots but we are fast progressing in the direction of having it.

Artificial intelligence is of three types or we can call them having three different phases. The first one is Artificial Narrow Intelligence (ANI) followed by Artificial General Intelligence (AGI) and lastly Artificial Super Intelligence (ASI).

The ANIs (Artificial Narrow Intelligence) are initial use systems that perform normal tasks in one or two areas purely based on programmed rules. They can’t self-learn and can only execute specific tasks as per programmed. All their decisions and actions are pre-determined based on various options assigned to them. E.g., Google Translate, Email spam filter software, Chess app., Speech recognition etc.

The AGIs (Artificial General Intelligence) are more advanced than ANIs (Artificial Narrow Intelligence) and work seamlessly across multiple functional areas for problem solving, decision support and reasoning. E.g., Siri, Automatic Stock trading software that decide on multiple factors, statistics and news on when to buy and when to sell stocks for higher gains.

The ASIs (Artificial Super Intelligence) are not yet fully ready. As the name suggest super intelligence means human like abilities to multitask, decide, act, learn and relate things like humans do. There are trials ongoing and robots as well super computers are being made that can achieve this but we are still not full there and it might take still 1 to 2 decades to reach this level of AI solutions.

Artificial intelligence (AIs) solutions are very helpful in improving efficiency and effectiveness for almost all functions. Here are some examples,

1.     For industrial site solutions of robotics help improve production significantly. 

2.     For innovation and development, the solutions on product development, test automations and statistics decision-making help prepare viable solutions for meeting customer needs.

3.     For Commercial organizations, the solutions on robotics process automation, machine learning based statistical analysis for accurate decision making and real time dashboard with built automated decision guidance.

4.     For Consumers and Customers, the solutions on recommending right products based on their choices and know how in various areas and stages of life.

5.     For Customer Support, the solutions on full detailed understanding of their customer base and how to keep them as well as engage them to increase sales.

Similarly, there are also solutions based on industry specific needs. E.g., Your MD, Google DeepMind, Netflix, Snapchat, Apple Siri, Google Tools, Autonomous vehicles etc.

Artificial intelligence is used almost all of us in various shapes and forms. Simply put all our day-to-day apps we used in mobile phone and computers have AI built in to support us by enhancing our productivity and decision making.

AI has many positive sides but it also has worrying negative sides to it. The negatives are related to ethics and deception. How do we know that the AI is ethically deciding and making decisions as its all programmed, it can be designed to deceive. AIs can also be prone to hacking and manipulations of decisions and actions. There are still many open ends to AIs safe & secure usage and reliability. 

The other major worry for people is, if we automate everything with AIs and Robotics, then what will happen to current job market. There will be a significant shift of jobs needed as almost all of current manual jobs can be taken up by AIs with in this decade.

Possibilities are immense and when computers came there was a huge transition of jobs, a similar transition will take place in this decade with AIs becoming fully operational and even more when they evolve to Super Intelligence level in the coming decade.

DevOps – End to End IT Development and Operations Management

DevOps is combination of development (Dev) and operations (Ops). As the name suggests DevOps is a framework and set of tools that ensure tight integration of development and operations management cycle.

DevOps need become stronger after year 2007 when internet technologies and platforms including social media platforms started being used heavily. This created a need for software companies and organizations to ensure that their IT development and operations management teams become tightly integrated to reduce errors, time delays, costs and more importantly become faster, highly reliable and accurate.

From year 2012 onwards DevOps became the norm for all software companies while from 2016 onwards most of the multinationals embarked on deploying DevOps frameworks and tools. In the past five years several specialized and automated DevOps tools have come up that offer seamless step by step integration for end-to-end IT development and IT operations management.

DevOps brings the development and operations team to work collaboratively together throughout the application lifecycle ensuring there is no gaps. DevOps is managed through set of automated tools that ensure each stage of handover is well checked and moved on to next stages without any gaps. DevOps framework and tools ensure continuous delivery and continuous integration along with version controls, logs and automation of tasks for all stages.

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The DevOps high level framework consists of 8 stages. Let’s understand each of them briefly.

1. Plan – DevOps planning stage is focused on agile planning and delivery. Agile development is an iterative development approach for continuous delivery.

2. Code – DevOps code stage is focused coders completing their coding packages and passing it on to build.

3.  Build – DevOps build stage is focused on combining and compiling the code packages to get them ready for testing.

4. Test – DevOps test stage is focused on testing and related rework completion to get the packages ready for release.

5. Release – DevOps release stage gets the finalized tested and built packages ready for final quality check and release. This is also the phase where continuous delivery and continuous integration comes together.

6. Deploy – DevOps deploy stage gets the packages deployed in production and moved on to monitor stage.

7. Monitor – DevOps monitor stage is focused on monitoring the success of deployment and address any issues faced.

8. Operate – DevOps operate stage is business as usual stage where normal operations continue. From this stage the next set of changes are fed to the plan stage and then the cycle continues.

Now let’s have a quick look at key tools that can be used at each stage of the framework. Organizations that operate the DevOps environment, use these tools to automate the entire DevOps application life cycle. There are many more tools and platforms available while below are the best tools/platforms that are used by many organizations.

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There are other aspects of DevOps as well which are slowly evolving. In past couple of year DevSecOps has come up as security is important. DevSecOps brings integration of IT development (Dev), security (Sec) and operations (Ops) ensuring IT security is taken care at each stage of the framework and entire end to end lifecycle.

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The latest framework relating to DevOps is called MLDevOps or MLOps. It integrates Machine Learning (ML), Development (Dev) and Operations (Ops) together. MLOps or MLDevOps is currently used by organizations dealing with big data analytics where they need to clean and model data using data science and machine learning techniques and algorithms. MLOps helps integrate and automate the entire process for machine learning applications life cycle management.

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There are many benefits of DevOps, while only organizations requiring continuous delivery and continuous integration should invest in this. DevOps will become a mandatory need for most organizations as all of them use internet-based applications that require agility and accuracy in managing changes and releases.

As we move towards using machine learning, artificial intelligence, robotics automation, mixed reality, blockchain, metaverse technologies, we will need full end to end automation of application development and operations lifecycle. We will need all of it to work seamlessly with agility and accuracy. 

I hope by now you have developed a good understanding on DevOps and why we need it. The need for DevOps and its evolving frameworks will continue to increase in coming period.

The Famous Eight Digital Business Models

We are in the digital economy and digital era where everything around us is digitalized. We all use many different forms of digital technologies to fulfil our daily needs and demands. Many of us also work and delivery digital technologies that support in digitalizing our business models.

Internet got introduced in the decade of 1990s, post that in the millennium (2000s) decade many foundations level digital business models evolved and started. The models became more powerful and started disrupting traditional business models and industries in the last decade. 

In the past decade many newer digital business models came up and many became largely successful while some perished as well. 

Let’s have a brief look at the digital business models and which brands are using them.

1. Free Business Model

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a.  The free business model is where the business is offering a service for free that can help the entire or subset of internet user community across the globe. 

b. The business model offers a free platform for users to use and fulfil their needs of searching, learning, sharing, connecting etc. 

c. The business model earns revenue using advertisements which are shown as part of the service.

d. The famous examples of the free model are Google, Facebook, Instagram, YouTube, Pinterest etc. E.g. when we search on google we get advertised and paid products on top of the search.

2. Freemium Business Model

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a.  The freemium business model is similar to the free model but its not entirely free. 

b. The business has the basic version of the solution, service or product free for all. 

c. The users have to pay for the standard and premium versions for getting additional features. The revenue is earned through subscriptions and data insights. 

d. Famous examples of the freemium model are LinkedIn and Spotify both have basic versions free to ensure they acquire the user base. Once users like the services, they subscribe and pay for more features.

3. Ecommerce Business Model

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a. The eCommerce business model is the most used and well know business model that all organizations and users use across the world.

b. In this model the organizations sell their products through an online platform to their customers as well as consumers to generate revenue.

c. There are many variations of this business model. The two most used are,

      i. Brand’s own shops are generally called D2C (direct to consumer) or B2C (business to consumer) business models where products are directly sold to end consumers using the online platform.

     ii. Brand’s retailers and professional customers generally use the B2B (business to business) or D2B (direct to business) business models where products can be ordered by retailers and professional customers at their agreed product prices and discounts in bulk for selling through their sales channels (shops, online channels etc.)

d. Famous examples of eCommerce models can be easily seen for any of the big brands’ online shops. E.g. Apple, Philips, Sony, Panasonic etc.

4. Marketplace Business Model

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a. The marketplace business model is next famous and widely used business model across the world.

b. As the name suggest, this business model is an online marketplace where consumers can find all brands’ products for online purchase.

c. The marketplace place providers create huge consumer base through digital marketing tools and techniques.

d. They ensure they are able to handle the full order to delivery cycle for the products sold. They also ensure brands’ can create their own shop in the marketplace to sell their products.

e. Famous examples of this model are eBay, Lazada, Shopee and many others.

5. On Demand Business Model

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a. The on-demand business model is what many of us use on daily basis. 

b. In this model the consumer calls or uses he on demand service and pays for the service based on the usage.

c. The model works through online on demand service request and pay per use.

d. Famous examples of this model are taxi and food delivery services like Grab, Gojek, Uber, Comfort, Ola, Deliveroo, Food panda etc.

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6. Subscription Based Business Model

a. The other largely used busines model is subscription-based business model.

b. In this business model we pay a recurring monthly or yearly subscription fees for using the product and services.

c. The business model is fully digital and online service consumed by consumers.

d. Famous examples of this model are, NetFlix, AppleTv+, Amazon Prime, Office365, Salesforce, AWS, Azure and several other online software and apps that we use on our laptop, tablet and mobile devices.

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7. Sharing Business Model

a. The sharing busines models have evolved in last decade where we rent or assets like property, car, equipment for use by others.

b. The busines model is fully digital where the consumer can reserve the asset for a certain number of days and pay for using it on those days.

c. Famous examples of sharing business model are car rentals and home rental companies like Airbnb, Uber and HyreCar etc.

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8. Open-Source Business Model

a. The open-source business model has gained more strength in the last decade while it started already in the millennium decade.

b. The business model earns revenue through training, hosting and other services while the core product itself is fully open-sourced for users to use and adapt.

c. Famous examples of open-source business model are Redhat Linux, Mozila FireFox, Github, Magento, MySQL and Python etc.

These are the eight famous and widely used digital business models that everyone must know. There are a few more models li

In this decade we will see further evolution of digital business models using next gen technologies namely artificial intelligence, robotics automation, data analytics, mixed reality and blockchains.

I hope this will help you learn and understand these models clearly now and every time you use it you will know how that business model and business works.

Creating Value with the Value Proposition Canvas

The value proposition canvas is important for all businesses. It can be used for new products, initiatives foundation. It can also be used for knowing the value proposition of our existing business as well as for any new businesses and ventures.

The value proposition canvas consists of two major areas. One is focused on the customers and their needs while the other is focused on value proposition, we offer to fulfil our customer needs.

Value proposition canvas can be seen as the foundation and most important layer for forming any business and ventures. It can be also easily linked to the business model canvas Value Proposition and Customer Segments sections.

It is fairly easy to prepare and gives a full overview of our customers and how we fulfil their needs with our unique offerings.

We must start with our customer segments and define a unique persona in the “Customer” section. Based on the customer persona, we can fill up the “Want – Gain” section. 

Ideally if you have completed the “Empathy Map” and “Persona” for your ideal customer then you can easily fill in the “Want – Gain” as well as the “Need – Pain” sections.

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Let’s get the Customer section filled up,

1.     In the “Customer” section fill up the ideal customer persona information. Focus on defining the key aspects of the ideal customer segment.

2.     In the “Want – Gain” section fill up the customer wants and/or gains. What will the customer gain if they get the solution they are looking for.

3.     In the “Need – Pain” section fill up the customer pain points and/or needs. Think about what pains will be resolved and what the customer really needs.

4.     In the “Early Adopters” section fill up the customer sub segment that will be the early adopters of the product / solution / service.

5.     In the “Early Majority section fill up the customer sub segments that will be the early majority of the product / solution / service.

After completing the “Customer” section, let’s move to the “Product” section and fill up the benefits, features and user experience parts. Let’s get the Product section filled up,

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1.     In the “User Experience” section fill up what customers will experience when they use the product/solution/ service.

2.     In the “Benefit(s)” section fill up the key benefits of the product.

3.     In the “Feature(s)” section fill up the key features of the product.

4.     In the “Unique Value” section fill up the Unique Value offered by the product. This should be something that none of our competitors or alternatives offer.

5.     In the “Alternatives / Competitions” section fill up what are the current alternatives include what competitors are offering.

Please do remember to ensure the value proposition product and customer sections are in synchronised with each other and complimenting.

Here is the complete overview of the “Value Proposition Canvas” and how it will look like.

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This completes the value proposition. Do remember to review and revise it a few times as you might not get it 100% right in the first attempt and there will be need to review it with team and key stakeholders.

With the value proposition canvas, you should be able to share the entire proposition to stakeholders with just one slide.

Please keep it simple and brief. The details for each section can come from respective detailed exercises of “empathy map”, “persona”, “product prototype” etc.

I hope you will like, share and use this for your creating value propositions. In my next series of articles, I will share about more useful concepts that I have used over years and which are necessary must have for everyone to learn and know.

Know everything about the business with in an hour

We all need to know our businesses and stakeholders well. In this brief article, I would like to share an important and east tool that I have used significantly and it works very well.

The best and the easiest way to understand the complete overview of any business as well as our key stakeholders is to create a business model canvas for them.

The process is relatively straight forward. All we need to do is to setup 30 minutes call with respective business owner and/or stakeholder and run through the series of questions which they can easily answer.

Important is to ensure you note or record answers to all questions meticulously. Also pay attention to listening and body language as your stakeholders speak. This will help you engage and understand the real issues and which areas are well defined versus areas requiring attention.

The Business model canvas is 8 segments consisting of 3 to 5 questions for each segment. Ideally it is best to fill it up in the below order.

1. Customer Segments: Focuses on knowing all the involved key customer segments of the business. Questions to ask are,

  • Who are our most important customers? 
  • For whom are we creating value? 
  • Is our customer base a Mass Market, Niche Market, Segmented, Diversified, Multi-sided Platform etc.

2. Channels: In channels we specify the business channels we use for business. Questions to ask are,

  • Which Channels do we use to reach our Customer Segments? 
  • How are our Channels integrated? 
  • Which ones work best (profitable & cost-efficient)? 

3. Customer Relationships: In the section we discuss and document about how we manage customer relationships.

  • How do we manage customer relationships with our customer segments? 
  • How are they integrated with the rest of our business model?

4. Value Propositions: This is one of the important segments where we clarify the value proposition(s) of the business.

  • What problems are we helping to solve? 
  • What unique value do we deliver to our customer segments? 
  • Which customer needs are we satisfying?

5. Key Activities: Key activities are key activities we do to meet our core business needs and deliver the unique value propositions

  • What Key Activities do our Value Propositions require?
  • Which Key Activities do partners perform?

6. Key Resources: In this segment fill up the list of key resources we need to run the business.

  • What Key Resources do our Value Propositions require?
  • Which Key Resources are we acquiring from partners? 

7. Key Partners: Here we define and list down our key partners for the business.

  • Who are our Key Partners? 
  • Who are our key suppliers? 

8. Costs Structure: This is an important to highlight all our business costs and including our liabilities.

  • What are the costs for our business?

9. Revenues Streams: In this section we note our revenue streams and how we generate revenue including our assets.

  • What are our revenue streams?
  • How we do generate the revenue?

Once we collected all the answers, we can document them on one slide for easy overview. 

You can also use this for knowing about any businesses of your choice. This simple overview can help you understand the business clearly within an hour.

Once you have understood and documented the business model, you can easily understand what are the key areas and where are the potential pain points of the business.

In my next article I will share an alternative version of business model canvas that can be used for any new business startups.

Learn and Grow – The 21 Good Management Lessons

Here are the 21 good management lessons from 2021. Please take it to learn, share and relate to your own experiences.

  1. Always be your honest and absolute very best in everything you do. 
  2. Make suer you have solid values and clear conscience to lead, coach and genuinely help people grow.
  3. Only do things that will help the team and business grow in legit ways as per organization’s core values, purpose and vision.
  4. Dare to challenge the status quo and bring the change that others are overlooking or afraid to do.
  5. Use your connections and build your network of team members who can team up to make the difference.
  6. Manage and support your boss as well as management team in achieving the overall organization goals.
  7. Build Trust and Empower your team members. Trust and support team members and the team will reciprocate by delivering a much greater value in return.
  8. Ensure that the team players and their workloads are closely monitored to balance it in such a way that everyone has at least 10% to 15% time per week to balance their work, learn, share and grow themselves.
  9. Team building in smaller teams focused on specific programs or goals is essential. Team building must happen at work as part of the work. The real team building doesn’t need special high spending (costly) events as team members stick together and find time to bond and enjoy together as a team on their own.
  10. Recognition awards and some time off for members that have over worked for delivering results is a much better way of rewarding and creating work life balance.
  11. Refrain from calling team members after office hours, on public holidays and over weekends as that is not very professional unless that is required for specific preplanned events or activities.
  12. Appraisal is the time that should be seen in a positive way to give and receive feedbacks as well as to ensure every 2-3 years based on performance people are promoted and well paid for their efforts year on year. 
  13. Let the team participate in suitable internal and external competitions to shows the best of the best solutions. Support the team by allowing them time to prepare and do the needful on their own but with your and other guidance where required.
  14. Ensure individual team members are well recognised for their work and role model behaviours. Make sure right team members get the needful credit for the work done.
  15. If the team has done a great job in delivering value then ensure each of them get their shining moment in the organization as well as encouraged to continue their journey of delivering more greater value.
  16. Teaming up is much less about dividing & conquering and much more about taking joint ownership to deliver on commitment.
  17. Take teams credit to get the award from top management & in return give the actual team players close to nothing but more complex assignments.
  18. Hiring interns and contract staff to bring new generation & different nationalities on the team is a great step forward but ensure that they have a good career prospect for long term. Groom them well to identify talents and hire them for long term success.
  19. Keep a transparent environment and let team members have skip level meetings or lunch sessions with top management to share their views and ideas.
  20. Be as transparent as possible and be open with team members, never lie to them about organization changes as they will eventually know if you lie. Being transparent will ensure team members are fully aware and well prepared to move forward as a team.
  21. Always remember that as a leader or even a manager you have careers of your team members in your hands. Be the honest leader your team wants you to be.

There are many Good management lessons for sharing. The above list is just my observations from year 2021. I hope you will be able to relate to it and use it for the betterment of everyone in the team.

Learn to Unlearn – The 21 Bad Management Lessons

Learn the 21 bad management lessons to unlearn. Take it in a fun way to learn and relate to your own experiences and share your own views and lessons.

  1. Always be on your boss’s favourite list. Being the top favourite will ensure fully secured career and growth path. Just do whatever your boss says doesn’t matter if that’s a wrong thing to do. Because the Boss is always right.
  2. Use connections to get ahead as it doesn’t matter if you really know the job you are hired for. You get new positions purely based on your connections.
  3. Just manage upward (your boss and your bosses boss) as they are most important for your growth and not your team. Use your team to secure your own growth.
  4. Track your team members. Hire & put few people in team, responsible of tracking others.
  5. Load team players with so much work that they will have no time & life left outside work.
  6. Once every quarter choose 2-3 people that worked 60/70 hours per week and give them $100 vouchers for their 100 hours of extra work.
  7. Call up team players after work hours and over weekends to give them additional work as urgent top priority. This will ensure you can relax while you take every hour of your team players family life.
  8. When it’s appraisal time, talk about demotions coming, budget cuts for all to scare everyone. This will ensure no one ever asks for promotion & salary increases.
  9. Buy awards for systems that are not even in use and showcase as the best award ever to improve your own image & credibility with top management.
  10. Take teams credit to get the award from top management & in return give the actual team players close to nothing but more complex assignments.
  11. If a team player is doing a great job on a complex program/project then remove them after the program has crossed most complex milestones and put your own favourites. This will ensure favourites shine and get great image.
  12. Snatch team players’ work (Divide & Conquer) and pass it to your favourites. This will ensure team players fall flat & loose all the remaining motivation and your favourites grow.
  13. Hire lots of interns and contract staff even if you don’t really need them. This will create great image with top management that you are employing and creating value.
  14. When you are asked to reduce headcount, you just need to remove all the hired interns and contract staff that you hired earlier and meet your reduction target.
  15. For growing the team size, just remove the most hardworking players and then hire 6-10 new people to fulfil the same needs. This will automatically increase team size and budget.
  16. For the skip level meetings with your bosses boss, train the team with who can ask what questions, which questions are allowed, who is allowed to talk, what to say and what to hide. In addition join the meeting even after being asked not to join. This will ensure the team will only speak what’s discussed and not ask any meaningful questions.
  17. To ensure you are able to track who has submitted the employee engagement survey when and whether or not all have participated; make it mandatory to share screen print of when you submitted the survey. This will ensure full participation in the survey.
  18. To ensure only your favourites win the lucky draw at team events, ensure only their names are announced doesn’t matter who’s name really comes out from the draw chit.
  19. Ensure only your favorite and friends company receives contracts so that you can benefit and get your % cut. It doesn’t matter if their prices are high till the time you are benefiting. Create multi (3) years blanket POs which will make you gain even more.
  20. To meet your value creation KPIs, simply count in previous year value in this year. No one will know and no one will ask as everyone likes bigger numbers. Even if people get to know, so what, you are the Boss.
  21. To scare team members remove the hardest working, honest and experienced team players and block them from getting a new job. This will ensure no team member dares to speak about wrong things and no one will dare to leave either.

I hope you enjoyed reading these lessons and some of you smiled as well. Please unlearn these 21 bad management lessons, if you are doing any of these. Please help others incase you see anyone still doing any of these.

Look forward to my next article of 21 Good Management Lessons. Catch up with you all soon.