Data Cleansing, Enrichment, Taxonomy & Governance

Clean and accurate data is a must for insightful data analysis. Data cleansing and enrichment is critical step in the overall data quality enhancement process. The process involves consolidating data from different systems, identifying relevant data, and cleansing & enriching it to create a master database.

Data cleansing involves reviewing and segregating the data by quality and nature of issues. For example –

Data enrichment enhances the quality of data by adding information that is complete, correct, and current. Important step in this process is line-item level specification enrichment as per the standard data taxonomy & structure. For example –

Overall data cleansing and enrichment process involves below steps –

Collect, consolidate, and segregate the data by quality and nature of issues.
Cleanse to eliminate inconsistent values and resolve redundancy in the database.
Enrich specification by normalizing the item description using Noun-Modifier pairs.
Identify and remove duplicates.
Further enhance through validations from authorized sources of information.

Cleansing & structuring the database is one-time activity and is only a half job done. To sustain good data quality over longer term, it is paramount to implement a data governance structure.

We also assist in designing & implementing a robust data governance process that includes – policy, rules, protocols, approval hierarchy, and master data management. Three broad areas are –