Digital Transformation Journey

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

It is normally viewed that one of the advantages of BOTs is their consistency. BOTs do exactly what they are calibrated/ programmed to do, and this provides consistency and repeatability or even eliminating unpredictability in how a process is performed.  However, reduction in unpredictability can be both good and bad, depending upon how we interpret our process performance data, and how we apply it towards this reduction in unpredictability. No doubt, automation eliminates unpredictability or variation. While humans may perform a task with a wide range of skills, BOTs are much more consistent, but they will perform the task the same way each time, every time, unless an exception is experienced.  Hence, modeling process performance based upon average process cycle times can often lead to suboptimal choices in process, and suboptimal performance expectations. Operating metrics are often used to identify processes worth automating. It is common to select target processes based upon their

As we all know RPA takes the robot out of the human. In general terms, RPA is a type of software that mimics the activity of a human being in carrying out a task within a process and freeing them to do other tasks requiring human strengths. RPA projects follow Robotics development life cycle (RDLC), very similar to software development life cycle (SDLC), like any IT project. After the process prioritization and requirement gathering stages, the most important phase of RPA development is solution designing. We should give a sufficient amount of time to solution design before any implementation/coding happens because the success & failure of the deployment heavily depends on it. Also let’s make sure that each identified automated process should have a solution design document. While designing the solution we should always keep note of these few points… Trigger Point: Before any real coding or development happens, think about how the BOT will be triggered in the future. It is importa