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Improving Quality Data Migration with Advanced Analytics

Contributors: Skip Berry

Traditionally, when companies are implementing a new business solution, one of the biggest challenges they face is migrating data from their old solutions into the new solution.  When migrating data from one system to another, it is critical that the project team has a focused approach that transfers data to the new system that is complete, accurate, and ready to use.

Historically, this has been a very extensive effort requiring the transport of data out of the old system and into Excel programmatically.  Then, significant time is spent scrubbing the data clean for use in the new system.  Scrubbing typically involves looking for duplicate data, removing records that are obsolete or invalid for any number of reasons, and looking for missing data that is critical to the new system.   Additionally, it is not unusual for the new system to require data that did not even exist in the old system.  This means that new data must be added to the records, either set to a default value or deduced using some kind of algorithm, then dropped into the Excel tables.

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Excel is a powerful tool but even Excel can only do so much.  This leaves a lot of tedious and time consuming effort for the project’s quality data migration team to make sure the data is correct for the new system’s go-live.

Because Clarkston has been involved in countless numbers of system migrations throughout our years of partnering with our clients, we understand the challenges that data migration can present for companies.  This has driven us to develop an efficient and accurate quality data migration approach using the most advanced techniques and analytic tools available.

As more and more firms migrate from older legacy systems to SAP’s leading-edge SAP S/4HANA solution, Clarkson created an efficient approach for data migration approach specifically tailored for S/4HANA. This enables our clients to rapidly and accurately make the transition and begin unlocking the value of their new S/4HANA system with minimal business interruption.

Clarkston’s S/4 Quality Data Migration tool includes the following critical steps:

  • Data Extraction
  • Data Quality Assurance
  • Data Profiling
  • Data Standardization
  • Data Cleansing
  • Data Mapping & Transformation
  • Pre-Load Data Verification
  • Post-Load Data Verification

In each of these critical steps, Clarkston has developed an approach that takes advantage of the best analytical tools, thus ensuring our clients data migration effort is as streamlined and accurate as possible.  In this way, we help our clients ensure that data in their new system is complete and ready to use.

If you are interested in learning more about Clarkston’s strategic approach to S/4HANA quality data migration please download and view our webinar “Data Quality with Advanced Analytics” by clicking on the link below.

Download the Full Webinar Here

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Contributions from data analytics expert Shantanu Mukherjee.

Tags: Advanced Analytics, Data Analytics & Insights, Data Quality
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