Data Rationalization Tools

Finding the right data is as important as using the right data

Data Rationalization Overview

What is Data Rationalization?

Data rationalization is often generalized as understanding and controlling duplicate or redundant data, but it’s actually much broader. Data rationalization also includes changing an organization’s culture when data is considered a company asset that should be protected as much as any physical asset. For example, sensitive data can be copied for a department’s use and (unintentionally) lose its sensitive metadata characteristics, putting the organization at security and financial risk.

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The Cost of NOT Rationalizing your Data

Redundant data has its costs. Besides the storage costs described below, redundant data tends to make the search of data more complex than necessary. How big of a problem is data redundancy? If you are like most companies, it is much bigger than you would care to admit. Without a focus on data rationalization, you may end up with a data quagmire.

Redundant data is more than simply one group duplicating another group’s data. For every production database, you also spawn a development, test, QA, staging, experimental and reporting database. It doesn’t take long for that single database to result in 6x more total volume. That additional volume requires additional database licenses, plus storage, as well as database administrators to manage the whole thing.

Primary Data Rationalization Classifications

Primary Copy

Users will see the unadulterated data set (their best choice). Often the data’s origin.

Deprecated Copy

See disapproved or adulterated data sets. Keeping users away from bad data is as important as pointing them toward the primary data.


Easily see duplicates of the primary data. Reducing the amount of duplicate data insures data quality.

Using a Data Catalog to Rationalize Data


Automate the Rationalization Process

The resolutions to these duplicity and inefficiencies issues are relatively straight-forward. By implementing Waterline’s AI-driven Data Catalog, your organization can document the location, quality, and lineage of data located in relational sources, in the cloud, or in Hadoop clusters of all data types.  The Waterline Data Catalog also automatically tags the data fields across different data sets with common business terms, and documents its overall quality, existing data lineage, and inferred missing lineage using Waterline’s patented fingerprinting technology. During this process, Waterline Data provides a true view of your organization’s data lake through Waterline’s Data Rationalization dashboard. The dashboard also identifies where you have high levels of redundant data that can be rationalized.

How Data Grows Exponentially

For a 1TB database, up to 50 TBs of data may be require for backups, non-production databases and data warehousing.

The Profusion of Data

In fact, if we think about the data proliferation pyramid, the multiplication factor is almost 50x. Cloud providers provide lots of affordable space

Data Rationalization Processes and Benefits

How Does it Work?

The Waterline Data Catalog is built on the premise that there are three steps to the data cataloging process.

  1. Helping data professionals to discover, organize, and curate their data using Waterline’s Data tools including the Rationalization dashboard.
  2. Allowing governance professionals to refine tagged data derived from Waterline’s fingerprint technology for compliance reporting and access control.
  3. Exposing the more organized and easily searched data catalog for business professionals to use through self-service analytics.

Benefits for Data Rationalization

Automating the end-to-end data cataloging process with Waterline’s AI-driven data catalog includes:

  1. Reducing the cost of hosting, securing, and managing redundant data.
  2. Rationalizing excess data, which lowers the cost of redundant database storage software licensing & support.
  3. Helping the organization think and use data on an organizational level and not siloed in a department.
  4. Reducing business risk due to “dark” data being stored or used incorrectly exposing potential risks with government and compliance regulations.

The Bottom Line

Data rationalization is essential to a more unified, effective data strategy. Only by automating the underlying process for inventorying, tagging, and curating data and data lineage can redundant data can be identified and rationalized. The bottom line is that data redundancy is a much larger hidden and indirect cost than most organizations realize.

Waterline’s AI-driven data catalog can facilitate the visibility and removal of redundant data. More importantly, it can facilitate a change in a company’s data culture.


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