Measuring Success using the Data Management Framework

By User Group for Microsoft Dynamics 365 & AX posted Nov 20, 2017 5:08:06 PM

“Garbage In = Garbage Out” underscores the importance of quality information in a data management project.  Regardless of the size, scope or timelines of your project, accuracy should be the primary objective.  Tracking of data as it travels into and out of the Data Management Framework (DMF) in Dynamics 365 allows for before and after reporting on the success of your efforts.  What to track and how to validate results needs to be defined.

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What to track

Most data management projects process large sets of data and tracking every single value would require exaggerated timelines.  Focus on critical values that lend themselves to before and after reporting

  • Quantity - Counting records is a standard measurement that provides a rough gauge of success. In most projects, you should at least end up with as many records imported as what you exported. 
  • Quality – Key numeric fields that can be totaled provide assurances in the quality of the data being moved. If you are moving payable or receivable data, knowing that the total dollars exported matched those imported is crucial.
  • Categories – Grouping data into categories provides summary totals that divide and conquer large amounts of information. Measuring the percentage breakdown of certain data points lets you qualify data placement.

How to validate

The framework for data management in D365 tracks record status, provides data set counts and allows for filterable exports or queries of your processed data.  There features allow you to compare high level record counts and focused totals on critical fields.

  • Total Records – Import and export projects in DMF report the summary record counts for you to track and compare. The “Execution details” function presents this big picture value as the first indicator of project success.
  • Record Status – Each record in a project has a status field that flags records as “Not started”, “Completed” or “Error”. The “View staging data” function takes you into the processed data grid form, allows filtering by record status includes error messages that point to the source of the problem. 
  • Reporting – Any data column in the staging data form can be filtered on in multiple ways. Data may also be exported to Excel from the staging data form which allows for any number of validations to be performed.
  • Summary Evaluation – Once data in imported, issues are fixed or accounted for and record counts match you should perform an evaluation of every critical field on a percentage of records. Reviewing field level data by a subject matter expert such as a department head, helps to find any missing or misplaced data.

While it is a critical step, tracking and measuring data in a migration project can be tedious especially on larger endeavors, but DMF in D365 does provide some good tools to help lighten the load.

 

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