Data Assessment Bundle

Importance of Good Data

As organizations look to adopt the new wave of coming technologies, like automation, artificial intelligence, and the Internet of Things, their success in doing so and their ability to differentiate themselves in those spaces will be dependent upon their ability to get data management right.

This will become increasingly important as connected devices and sensors proliferate, causing exponential growth in data — and commensurate growth in the opportunity to exploit the data.

Those that position their organizations to manage data correctly and understand its inherent value will have the advantage. In fact, we may see leaders pull so far in front that it will make the market very difficult for slow adopters and new entrants.

The Importance Of Data Quality — Good, Bad Or Ugly [Forbes, June 5, 2017]

Good Data Benefits Bad Data Impacts
Improved Decision-Making Undermining Business Confidence
Increased Productivity Missed Opportunities
Improved Compliance Lost Revenue
Improved Marketing

Reputational Damage

Goals + Assumptions

The primary goal of the project will be to identify any potential problem areas with the legacy data that may need to be addressed beyond normal data migration efforts.

  • Assessment is to improve data quality, minimize data integrity issues, and reduce data volumes in preparation for conversion to SAP.
  • Identification of legacy system(s) to be considered as part of the complexity assessment,
  • Identification of respective master data entities to be evaluated i.e. Customer, Vendor, Material, Bill-of-Material, etc.
  • Access to the legacy system(s) and/or extract(s) of data from that legacy system (s) and any documentation on legacy data will be provided for metadata evaluation.
  • No changes to the data will be performed during the assessment.
  • Data and fields will be evaluated in the context of standard SAP best practice master data requirements.

Activities and Deliverables by Phase

Complexity Definition

Determination Criteria:

  • Structure – Data from different sources, or even different tables from within the same source, could often refer to the same information but be structured entirely differently.
  • Size – The amount of data you collect, Tall Data – tables that contain many rows and Wide Data – tables that contain many columns.
  • Data Type – Different types of data have different rules, especially semi- & unstructured data.
  • Dispersed Data – Data stored in multiple locations.
  • Growth rate – Speed at which your data is growing or changing.

Additional Considerations:

  • Query Language – Different data sources speak different languages, SQL popular.
  • Level of Detail – Granularity at which the data requires exploration.

Omnipresence of Data Quality

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