Digital Transformation Journey

Automation and transformation have become one of IT’s dynamic pair: Where you see or hear one, the other seems sure to follow. They both are often discussed in the same breath, but do they always go together? They’re certainly linked by their priority and popularity in IT and business circles. Both have been atop the strategic roadmaps of leaders for years now. Let’s first try to check the relationship in clear definitions. Digital transformation is nothing but is the integration of digital technology into all areas of a business, primarily changing how you manage and deliver value to customers. It’s also a cultural change that requires organizations to constantly challenge the status quo, experiment, and get comfortable with failure. Whereas IT automation or automation or BOT’s, sometimes referred to as infrastructure or process automation, is the use of software to create repeatable instructions and processes to replace or reduce human interaction with IT systems. You can have a

In part I of this series, we have gone through methods of data capturing. In this part let's explore why and how to automate the captured data. Data automation is considered vital for business sustainability. Entire business ecosystem is built on a foundation of data that is always changing. That data is housed across and used by many distinct systems across organization. The efficiency and effectiveness of business operations heavily rely on how easily data can be collected, updated, and passed between systems. According to the International Data Center (IDC), the global data sphere is expected to grow to 163 zettabytes by 2025. That’s equivalent to 163 trillion gigabytes, or ten times more than the amount recorded in 2016. That’s a lot to deal with, isn’t it? This is where data automation comes to your rescue! So, what is data automation? In a very simple language, we can say - It’s the use of technology to automate the way we collect, updates, processes, and stores data. B

Data automation is the process of updating data programmatically. Automating the process of data handling is important for the long-term sustainability of any data program. There are three common elements to data automation: E xtract, T ransform, and L oad. E xtract : the process of extracting your data from one or many sources’ systems Transform : the process of transforming your data into the necessary structure. Load : the process of loading the data into the final system. In these three parts of write-up, first we will take up how to extract the data or methods of collecting the data before diving into the technical nitty-gritty of data automation. Having a complete clarity beforehand about all the three elements will help you to pick the right process and engage the right people at the right time within your project management initiate. In general Data capture is the process of extracting information from a source and converting it into data readable by a

We all know that now day's RPA is more and more seen as a silver bullet for many of the difficulty’s businesses face. Some companies see it as a direct path to reducing operating costs, while others view it as a way to enable consistent quality and a better customer experience. But remember implementing an RPA solution isn’t as simple as throwing a few BOTs at a manual task. So, whether we have been working with RPA for some time or are just exploring its usefulness for business, we have well-thought-out RPA BOT failures as a challenge to our program’s success. There is no doubt that BOT has become an integral component of our business & production but do have we learned the art of dealing with them? Surprisingly we don’t have proper answer for the same. There are tools available in the market to govern & monitor work queues, schedules & execution rules. But the problem with these tools is that they are reactive in nature. They act only after the failure has already occ

It has been quite a while since corporate corridors are abound with news of digital disruption especially Robotic Process Automation (RPA) and AI impacting every walk of life. While strategy heads and digital officers have amassed enormous amount of information on trends in Automation, one key question that we face while working is on identifying the right starting point.  The straightforward criteria for a perfect automation / RPA use case are that the process is transactional in nature, the logic required to complete the transaction is rules based and the data required to run the automation in a structured, digital format. In short Process stability is one of the most commonly endorsed criteria to appraise RPA prospects. The older argument is that the process should not be in a state of unpredictability if we are going to pull RPA to automate it. While this is ideal and it should be considered a nice-to-have. But there are several reasons why we should not reject an RPA opportunity b

Now we all know that RPA saves money. It cuts down on employee time spent on tedious tasks. It makes business processes move along more quickly. But, what does RPA cost? This is a critical question that anyone contemplating RPA must think through as thoroughly as possible. The costs will vary greatly depending on a range of factors. It is possible to arrive at a realistic budget estimate, however, if one considers all of the cost elements that go into an RPA project. At the outset, it makes sense to separate the costs required to launch RPA from the project-by-project and BOT-by-BOT costs that come once the platform is up and running. The cost of the automated BOT implementation depends on variety of factors like the complication of the BOT (multiple conditions, multiple checks, and multiple operations or just one streamlined process), time and efforts spent on the initial business processes analysis, initial setup and programming required and, finally, costs of all APIs and apps used

Companies in every major industry are turning to semiautonomous computers (better known as BOTs) to automate large- and small-scale business processes. This technology replaces human intervention in back-office operations, improving operational efficiency, reducing costs and increasing margins. However, organizations cannot employ this technology effectively unless the BOTs are engineered properly. And one of the most important component of this BOT engineering is Exception handling or in simple words right categorization of the errors and then developing solutions around the same to deal with those errors to meet the expectations of the RPA owner. Occasionally, in the course of its work, an RPA BOT definitely encounter a situation it was not programmed to handle – and, in those cases, it will stall. Those cases can be for foreseeable reasons – security or access constraints – or it can be for unforeseeable reasons – like missing or incorrect data. Increasingly, RPA developers are ab