Substation Data Validation, Cleansing,

De-cluttering, and New Data Creation

Overview

UMS Group was engaged to examine and repair data on substation assets in SAP:

  • What information is stored in SAP for each asset type?
  • Where are there errors or gaps that can be repaired now?
  • What pull-down lists are available to users when characterizing each asset?
  • Define new standards for each characteristic
  • Define a correction strategy for moving to the new data standards

Methodology

Data Fields:

Example 1: Data cleansing process for the “Manufacturer” field:

  1. Compiled a list of unique entries
  2. Standardized the naming convention
  1. Updated the Manufacturer name to reflect mergers and acquisitions through research, e.g.

Example 2: Parsing Out and Correcting Individual Data Columns:

Functional Location Description field

Example 3: Added new fields for asset attributes that were parsed out of existing fields

1. Metal Clad (Y/N) – based on Description
2. Transmission or Distribution – based on FD Class
3. Interrupting Medium – based on Interrupt Medium
4. Single or Multi-Pressure – based on Description
5. Single or Multi-Tank – for Oil Breakers, based on Number of Tanks

Example 4: Filled in missing fields from correlating to other sources:

1. Substation Name – 5,506 rows added
2. Zip Code – 250 rows added
3. Substation Address – 82 rows added, – now 98.3% complete

Results

We identified 15 (of the original 100) fields are empty or have only garbage data in them, and recommended deletion to clean up the data – decluttering it by 15% and making room for new more useful fields.

We looked for opportunities to develop and fill in missing data…

The project ultimately resulted in a more accurate, complete, and robust data set with the confidence of the client, including new data standards and strategies for improved data governance going forward.

Embark on Solutions That Transform

 

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