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LESSON - Adding Data Governance to Improve Business Decisions

By Tony Fisher, President and CEO, DataFlux

Anyone who has ever flown a plane—or even glanced into a cockpit when boarding a commercial flight—can appreciate the complex array of gauges and monitors that the pilot must check. All the data about a plane’s speed, course, fuel, and other details are within easy sight, each giving the pilot the information necessary to make sound, safe decisions.

Similarly, organizations rely on data to provide the foundation for business decisions. For years, companies have implemented business intelligence (BI) programs to achieve one goal: make better decisions from their corporate information. Many companies have discovered one inescapable truth: it’s impossible to make an informed decision based on outdated or erroneous information. Just as a pilot needs to monitor the health of the aircraft, organizations need to constantly gauge the health of their data.

“Once and Done” Is Not Enough

The impact of data decay can influence—and hinder—many enterprise initiatives. Imagine a manufacturing company that builds a data warehouse to serve as a single repository for all of its information about customers, products, and inventory. From that data, it can uncover trends about customer adoption, resource allocation, and future needs.

After a review of the data, this company finds that new, nonstandard information is constantly arriving at the repository. The effect of this bad data may not be felt until much later. Whenever the company explores this data to identify patterns or tendencies, the presence of bad data can skew the results.

The solution for building high-quality corporate data on an ongoing basis is data governance. With data governance, technology and business users can create rules to examine data automatically to uncover problems as they occur. These users can also chart metrics related to data quality on a periodic basis and begin to address some of the underlying reasons that bad data is being collected in the first place.

The Role of Data Monitoring

Just as pilots continually monitor their gauges, companies need to steward their data as a valuable resource. Instead of loading questionable information into their data warehouses, companies can use data governance programs, which often include data monitoring, to check and control incoming information in order to maintain high levels of data quality.

With data monitoring, you can:

  • Detect problems from incoming data. Validate existing data against established business rules to uncover and address data integrity issues—before they become a problem.
  • Generate instant alerts. Set up automated system notifications and e-mails to flag problematic data as a new, inconsistent record enters the system.
  • Identify trends in data quality metrics. View ongoing statistics about data to see when the value of data starts to decline.

Data monitoring extends the reach of traditional data quality programs by making good data a corporate priority. When data does get out of control, users know immediately—and they can react to problems before the quality of the data declines.

For organizations that have already started an effort to improve data quality, most of the elements are already in place to build a data governance program. In fact, data monitoring is an extension of the effort required to get data into a reliable state in the first place. The same business rules used to cleanse, standardize, and verify data in the initial data quality project can serve as the rules to examine and flag data integrity issues over time.

Building consistent, accurate, and reliable data is not easy. Periodic fixes will only provide temporary relief from the various problems that can arise because of bad data. With data monitoring, companies can better control their data and build more reliable information to support any future business intelligence efforts

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