Why the Right Data Governance Platform Matters for Hadoop

May 4, 2015

Having the right data management platform in place for your Hadoop project is very important. If you don’t, you won’t be able to scale from a pilot program to a full-scale deployment. You also won’t ever get the full value from your data, stunting your return on investment. What makes a data management platform the “right” one? It needs to do a couple of key things: 

  1. Operationalize and automate the maintenance of data to manage data quality and make the data readily available for consumption. Without this piece in place, you won’t be able to derive business insights from the data.
  2. Help you meet all of your governance requirements. This is a big issue for many enterprises looking to move Hadoop into production, but are on hold because they don’t feel the governance piece is robust enough. To manage data governance, there are several third-party governance tools available. Many of these tools are trying to figure out how to provide governance in Hadoop. 

Data quality

We’re all familiar with the saying “garbage in, garbage out.” This couldn’t be truer than for Hadoop. Managing data quality is absolutely critical to the success of a Hadoop project. “Wait a minute,” you may be thinking. “I thought the advantage of Hadoop was that it can ingest and store any type of data, no matter its quality.” This is true. However, you still have to apply metadata to qualify the data that’s loaded into Hadoop – including its level of quality – so that you know what you have and can find and use it. 

A good Hadoop data management platform should enable you to apply three categories of metadata to qualify your data: 

  • Technical metadata, which defines the structure and form of the data.
  • Operational metadata, which includes information about where the data came from, who loaded the data in, and how it has moved from raw data to transformed datasets.
  • Business metadata, which is the information data users need to find the data they need for analysis.

Applying all three categories of metadata to your data helps you manage data quality and gives you a more complete view of the inventory in your Hadoop data lake. 

Data governance

With the metadata in place, you can use it to enable your data governance strategy, i.e., policies and standards for the management and use of data. For example, specifying from where data can be acquired, who owns and is responsible for the data, who can access the data, how the data can be used, and how it’s protected – stored, archived, backed-up, etc. A governance strategy also defines audit procedures to ensure that the data remains in compliance with government regulations – which can be challenging when combining data from diverse datasets.  

You need a robust data management platform in order to provide the technical, operational, and business metadata that third-party governance tools need to work effectively.

Plus, the right platform saves you money

In addition to having less control over the management and usability of your data, without the “right” data management tool in place for your Hadoop project, you also run the very real risk of going over budget and potentially impacting your product and services’ time-to-market. Managing data with a robust platform means less time, effort and resources spent to analyze data and deliver business results – the ultimate goal of your Hadoop deployment.

Learn more about Hadoop data lakes from the eBook Architecting Data Lakes and see what a data lake management and governance platform can do for you.

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