Maximizing Analytical Insights with Data Governance

Using Data Governance to Unlock a Treasure Trove of Analytical Insights

Mark PriebeOctober 10, 2018

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You hear it time and again: data is an organization’s most valuable asset.  But it gains much of its value when it’s analyzed to uncover insights to drive efficiency and innovation. The process of analyzing massive amounts of data to derive these analytical insights is quite the challenge for businesses today. To unlock data’s potential, organizations need to ensure they’re using appropriate, accurate data for analysis and act quickly on the intelligence to optimize everyday business operations, enhance the customer experience, support business decisions and ultimately increase profit. But without the right data governance framework in place, analysis may produce outdated, irrelevant, or unreliable information.

Businesses need a comprehensive data governance solution to unlock the full potential of their data assets. However, not all data governance solutions are created equal. While many organizations will say they have data governance in place, their solution often fails to realize the data’s maximum potential because it’s incomplete. Organizations require an integrated approach to data governance that cultivates collaboration among diverging lines of business and matures over time.

Unlock Data’s Potential with Comprehensive Data Governance

 Data governance is all about formally orchestrating people, processes and technology within organizations to leverage data as an enterprise asset. At its core, it is about data understanding and ensuring an organization’s data is appropriately used to maximize the value of analytical insights.

Business users from different departments will only utilize their data assets when they fully understand the information. By establishing a business glossary, data catalog and data dictionaries, tracking data lineage and creating uniform policies and procedures, organizations develop a clear-cut framework for data responsibilities and standards, which in turn increases data understanding among all data users.

Business users also need to trust the quality of their data if they are going to leverage it for analytical insights. Data quality should be scored and monitored as part of a comprehensive data governance framework. As data travels through the data supply chain, it may be subject to new processes, uses and transformations, all of which can impact data integrity. A comprehensive data governance process with data controls and quality monitoring will prevent downstream data issues from arising.

Data governance is not a finite project, it’s an ongoing initiative. The process evolves and improves over time. Once the data governance model matures, organizations can then layer in analytics to continue to strengthen organizational data quality. By performing analytics and machine learning in sync with data quality business rules, organizations substantially enhance the efficiency and effectiveness of their data integrity checks.

However, successful governance requires a modern solution suite with a set of capabilities that work in concert with one another and maximize analytical insights.

 Taking Advantage of Modern Data Governance Technologies

A complete data governance solution suite should contain a multitude of capabilities beyond data governance. The solution should also include data quality and analytics to deliver complete transparency into an organization’s data landscape. These essential technologies help organizations understand what data is available, its owner and steward, lineage and usage, and its associated definitions, synonyms and business attributes. Business users can also quickly define, track and manage all aspects of their data assets to quickly derive analytical insights.

The most effective solution suite should also conduct data quality checks such as data profiling, consistency, conformity, completeness, timeliness, and reconciliations to validate the integrity of data and ensure continued trust among business users. In addition, an all-inclusive solution suite should apply machine learning algorithms for self-learning to regularly upgrade data integrity.

An all-inclusive solution with a foundation of data governance is an effective way to maximize the effectiveness and competitive advantage of analytical insights.

If you would like to learn more about leveraging data governance to optimize your analytics strategy, download the data sheet below.

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