Governance to Avoid a Data Landfill

Social Media Strategist, Information Management

Garbage in, garbage out. It’s an old refrain embraced by many data professionals who advocate for data governance. Many worry that big data will just compound this problem, turning a dumpster of dirty data into a landfill-sized problem.

But going into big data with a strong data governance program can help. Rather than just adding more and more data to the pile, a smart strategy can help you actually benefit from your data. Most tend to agree that governance should do a few key things for your data:

Keep it clean: A good data governance program will include several provisions to ensure that data is squeaky clean, both at the point of entry and once data is in the database. This should involve measures for de-duplication and remediation.

Protect it: Consumer (and legislative) expectations of privacy are changing rapidly, but as Larry Dubov explained, a “privacy by design” approach can help organizations address the risks and needs of privacy compliance.

Steward it: Data stewardship involves policies and processes to ensure the right data gets to the right systems or people to make decisions.  As data volumes grow, this becomes even more important.

Of course, each of these aspects raises several questions. Do they still apply in a big data world? Can traditional data quality measures scale to handle bigger, faster data? Do you need new tools? Are there new stages in the information lifecycle?

Join our panel of experts for a #bigdatamgmt tweetchat on Wednesday, February 27 at noon ET to discuss how data governance can handle big data – and what you need to consider. Our panel includes experts from across the spectrum, including:

John Furrier, SiliconANGLE

Jeff Kelly, Wikibon

Mike Martin, BTRG and IBM Champion   

Craig Mullins, Mullins Consulting and IBM Champion  

Alex Philp, GCS Research and IBM Champion   

David Pittman, IBM Big Data

Follow along with the #bigdatamgmt tag to see the conversation and share your thoughts.