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Nonprofit Leverages Collibra Quality Tool to Cleanse and Standardize Its Data

A nonprofit firm that provides executive search services needed to improve the quality of its executive candidate and contact data. The firm hired FSFP for an engagement that used Collibra’s Data Quality (DQ) tool and focused on improving the quality of the firm’s data. We assessed and cleansed data, created business rules, developed data management best practices and more.


Industry: Consulting (nonprofit executive search firm)

Background: Nonprofit offering executive search services to higher-education institutions, associations and related organizations.

Engagement Type

Three-week engagement included a one-time review and cleansing of all records in the client’s candidate network dataset, housed in Microsoft Dynamics 365, using the Collibra DQ tool. The data set had multiple sources, such as a legacy database, direct extracts, client’s customer-facing website and data manually entered by power users.

Engagement also provided guidance and best practices to keep the data high quality and trusted going forward.

Opportunity Areas

  • Help identify and produce source data in an acceptable format
  • Identify critical data elements (CDEs)
  • Help define and document business rules for the CDEs
  • Produce data profiling results for CDEs
  • Cleanse and standardize data
  • Support data remediation in Microsoft Dynamics 365
  • Produce and socialize data management best practices for building client’s future data quality program

Engagement Snapshot

Prepare data set and ingest – Upload client-provided data file to Amazon S3 bucket to facilitate connectivity to the data set.

Create business Rrules – Document CDEs and business rules and code them in the Collibra DQ tool to run against source data.

Use Collibra DQ’s rules library – Leverage the tool’s out-of-the-box functionality, which checks formats such as valid phone number and email address, US state names, etc.

Find duplicate data – De-duplicate the data set per business rules and provide a copy of the data set to the client for remediation.

Export exceptions data set – Capture data set records with business rule breaks and provide to the client.

Assess data quality score – Use the DQ tool’s customization features to get a meaningful quality score that prioritizes business rules that are most critical.

Produce data management best practices – Create a reference document for the client and conduct training sessions. The document includes FSFP’s data quality life cycle* framework and recommendations for:

  • Organizational and culture change
  • Why there’s a need for dedicated resources for a DQ program
  • Process for identifying CDEs


  • Created a process to remediate and cleanse bad/inaccurate records in the source data based on insight provided by the DQ tool
  • Greatly improved candidate database and job placement opportunities with duplicate and inconsistent data removed, using an additional third-party tool
  • Enabled the client to get a rapid assessment of their data’s quality, leveraging FSFP’s DQ tool expertise
  • Ended the engagement with the client knowing data quality best practices and the data quality life cycle, so they can keep data accurate going forward

This engagement’s consultants included Praveen Maddi and Jason Martens from FSFP.

* Data quality life cycle includes: data profiling; quality assessment; cleanse and standardize; quality monitoring; data issue management; issue remediation; quality progress and impact reporting; identifying CDEs; data quality tools; organizational and culture change; and dedicated data quality resources in the organization