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.

IT Service Management

IT Service Management is largely focused on effectively and efficiently managing the IT Operations processes, people, technology and tools.

Managing wide variety of IT services across multiple domains, platforms and standards requires standard framework, operating procedures, partners and tools.

ITIL (IT Infrastructure Library) Framework is used by all successful organization worldwide since 2003. The most robust ITIL framework that lasted whole of last decade was ITIL Version 3.0.

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For the current decade given the shift of entire technology platform and how we manage IT, there was a need for a new framework that can integrate well and work hand in hand with Agile, Lean, DevOps and Digital/Business Transformations.

The new ITIL framework ITIL Version 4.0 has reshaped ITIL framework to meet the current decade IT Service Management needs.

Here is a quick overview of what has changed from ITIL V3.0 to V4.0.

The new framework is more generic and holistic to ensure it can easily fit for all organizations.

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1. The core concept has changed from “service lifecycle” to “service value system”.

2. The processes have been changed to practices. The new framework has 34 ITIL practices.

3. ITIL guiding principles have been revised and reduced to 7 principles.

4. The 4Ps of service design are changed to 4 Dimensions of service management.

Here is a more detailed overview of changes from processes to practices.

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1. ITIL v4.0 shifts from 5 process domains to 3 practices domains.

2. Number practices have been increased to 34 when compared to 26 processes.

3. A lot of General Management practices have been added, also several new practices added in service management as well as technology management domain.

4. Best practices and process domains from ITIL v3.0 are all retained under ITIL v4.0 practices.

5. In ITIL v4.0 focus is increased on value creation instead of just managing the service lifecycle.

6. Integration and links with other frameworks and methodologies is now possible making ITIL v4.0 fit for agile, lean and devops frameworks.

7. ITIL v4.0 is much more aligned with new business strategies and new technology platforms and frameworks.

Overall ITIL v4.0 is best suited and fit for managing IT services and operations. Deploying the ITIL v4.0 framework is relatively straight forward for organizations already using ITIL v3.0 framework. IT service management in many organization is managed through IT strategic operations partners. All we need is the right operations partner and tools.

There are many IT Service Management tools in the market. Large organizations mostly use ServiceNow while small & medium size organizations use online web based pay per use (SaaS) tools.

The way we serve our internal and external customers will change entirely using automation, machine learning algorithms and artificial intelligence.

Automation has taken over the helpdesk and support part to a large extent through artificial intelligence, chatbots, robotics process automations as well as data analytics platforms. Automation and self service support models will continue to become more stronger in coming years but that will not entirely remove the need for IT service management as a whole.

IT service management is an important core of IT services and is here to stay for this decade and beyond.

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.

Create New Venture and Startups using Lean Canvas

The Business Model Canvas can be used to document and understand the existing business overview. For new entirely new businesses we can use the Lean Canvas Model.

Lean Canvas business model can be easily filled up if the business information is clearly available. It helps to bring structure and thought on important aspects of the business.

Lean Canvas helps in defining new venture and startups with ease. It consists of 9 major segments which can be defined in one single slide while details on each can be put up on separate slides or documents for reference, inputs and refinements.

Now let’s briefly define the segments for better understanding.

1.     Problem: List down the problems of your customer segments. Specify which key problems will be solved.

2.     Existing Alternatives: List down what are the existing alternatives and how customers cope with these problems or solve them using alternatives.

3.    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.

4.     Early Adopters: List the sub segment of customers that will be early adopters of the solutions and services.

5.     Unique Value Proposition: This is one of the important segments where we clarify the value proposition(s) of the business.

a.    What problems are we helping to solve? 

b.    What unique value do we deliver to our customer segments? 

c.     Which customer needs are we satisfying?

6.     High-Level Concept: List the high-level concept of your unique value proposition. 

7.     Solution: Outline the solution and service for each problem. Also check that the solution matches the unique proposition and high-level concept elements.

8.     Key Metrics: In this section specify your top 3 to 5 Key Performance Indicators which will help you measure your business and its success. Use SMART method to defined clear, concise and measurable metrics.

9.     Unfair Advantage: In this segment specify your unfair advantage. It is something that cannot be easily copied. E.g. your years of experience of specific industry.

10. 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)? 

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

  • What are the costs for our business?

12. 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 you noted all the information, you can easily transfer it to the one slide overview.

Now go ahead and give it a try. You can also use this for clearing your understanding on any ideas, start-ups and ventures. This simple overview can help you understand and explain your start-up or venture clearly to others. It will also help you sharpen your business and make a call whether its worth trying as well as identify gaps that you will need to fill.

After you have understood the Lean Canvas model, you can easily understand what are the key areas and where are the potential gaps are for the business.

In my next article I will share an overview of value proposition model that can be used for any new business ideas with in existing business.

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.