Digital Transformation with RPA and APA

Introduction

In today’s world, every action we perform generates data. Similarly, the business processes we focus on today for automation deal with huge amounts of data. The business processes receive data from different sources such as:

  • External applications
  • Scanned, computer-generated documents.
  • Emails
  • Social media and many more…

Why data analytics from personal experience

I started my career eleven years back as a data analyst. Since then, I have been in the data analytics domain for many years. I have experienced the importance of extracting information from different silos of data and generating analytical information to support decision-makers and operational activities. Generating analytical data is a very painstaking activity due to some of the following reasons:

  • Data used in business processes are sometimes structured and sometimes unstructured.
  • Cleansing and transforming data into meaningful and usable format takes a lot of effort and time.
  • Data quality in source systems may be low due to human errors and limitations of source systems themselves.
  • Microsoft
  • Oracle, and there are many more…

RPA + Data analytics

Many vendors provide RPA solutions. However, UiPath provides the best state-of-the-art RPA platform to automate manual, repetitive actions performed by humans. UiPath robots can easily interact with any software platform to mimic human interactions using multiple technologies. On the other hand, many Analytical Process Automation (APA) tools such as Alteryx provides the capability to easily build a dataflow to perform various complex data related actions such as:

  • Prepare cleanse and combine data sets
  • Perform complex forecasting, geospatial, and predictive analytics
  • Send data to files, databases, APIs, BI/ reporting/ dashboard tools and, many more.

What makes this combination so powerful?

RPA is capable of automating high-volume repetitive business processes by introducing a virtual workforce. The automation is developed based on the business requirements and the outcome of the data they expect. Hence, you can see that; RPA or APA, it is all depending on the data. Many business processes in industries such as finance, banking, logistics consist of steps that require the generation of predictive, geospatial, and other types of analytical data to perform critical procedures.

How to Approach?

It all starts from the requirement gathering phase.

  • Identify all the different data sources of the current process.
  • Capture the exact steps carried out during the process
  • Capture at what point in the process it requires to perform the analysis of the data
  • Capture the types of analysis performed on the raw data and how to streamline the steps
  • Capture what is the final output of the analysis process
  • Capture how the generated analytical data is used in the downstream requirements.
  • Identify what other manual processes use the generated data and the steps carried out in those processes.
  • Identify the process inefficiencies and data issues that need attention.

Conclusion

Every business process is unique. It is vital to properly understand the exact requirement for RPA and APA before committing to any technology or concept. Combining RPA and data analytics expands the scope of RPA, enabling us to consider more and more business processes for automation. Considering the facts, the combination of RPA and APA could actually be a game-changer for any organization.

UiPath MVP/ Executive Lead — Robotic Process Automation/ UiPath Community Leader for SAARC Region/ UiPath Community Moderator