Digital Transformation with RPA and APA

Introduction

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

Why data analytics from personal experience

  • Different sources have their distinct ways of storing and representing data.
  • 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.
  • Alteryx
  • Microsoft
  • Oracle, and there are many more…

RPA + Data analytics

  • pulling databases, files, and API’s
  • 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?

How to Approach?

  • Challenge the current process
  • 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

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Lahiru Fernando

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