By Emily Washington

It’s a frustrating scenario when various members of the same organization realize their data doesn’t match. They pulled data from the same pool of information, yet different results were delivered. This is damaging for boardrooms, for business and for customers. Yet, this isn’t a scenario experienced by a certain industry or brand; this is an experience many data users have encountered — many times more than once.

To avoid these types of scenarios, organizations are implementing data governance initiatives that are inclusive of processes, frameworks and technologies to enable the use of data as a reliable asset. When data is not governed correctly, accuracy and reliability are questioned, and valuable information quickly turns into a liability.

To prevent bad data from derailing data-driven initiatives and business plans, more and more smart organizations are unifying diverse lines of operation, improving data governance methods and equipping themselves with modern tools. They’ve come to realize that a strong team without state-of-the-art processes, technologies and governance programs is not enough—all three are needed and must work in harmony. The technology must support the people while the people must support the process.

However, full visibility across the data landscape is not enough. If data is to be governed, it needs to be of the highest quality. What’s the sense in governing data you can’t trust? By integrating data governance with data quality, businesses establish enterprise-wide trust in data to ensure information remains a business advantage. Below are two tips to get your data governance program off the ground.

Aligning Data Governance with KPIs

According to a recent Gartner survey, more than 87 percent of organizations have low business intelligence (BI) and analytics maturity. This does not bode well for beating the competition, and the lack of a formal data governance program is a primary reason.

Businesses must ensure their data governance program lines up with enterprise business objectives and key performance indicators (KPIs). Once identified, the next step is to create processes and business rules around these business objectives. It’s also important to identify critical applications and data elements to support business goals.

Once the key processes and governance rules are defined, it is time to apply data quality rules to make sure the data is, and remains, accurate end-to-end as it flows through the data supply chain.

Data Trust Continuity

Data quality is crucial to the success of any data governance program. Without high-integrity data, users do not trust the information, which leaves them even more vulnerable—studies show that data quality problems cost businesses upwards of $15 million annually. Yet, when data quality is top-notch, users depend on their data, encouraging data utilization and producing and monetizing valuable business insights.

To restore trust in your data, there are two types of data quality rules an organization can implement: basic and advanced. Basic rules are the most common, the most used in organizations today and involve validity, completeness and conformance. Applying these rules ensures that data meets basic standards so users can leverage the insights and translate the information into business success.

Advanced data rules involve checks for integrity and accuracy. Data moves from point to point across the data supply chain and within and between systems. Integrity checks track the movement to make sure data values are not compromised or dropped. When combined with data lineage, data quality rules give an impressive view of the end-to-end data environment.

Companies want teams from various departments to trust their data and use it as a valuable asset. However, to do so, organizations must combine both basic and advanced data quality checks within a strong data governance program.

About the Author

Emily Washington, executive vice president of product management at Infogix.

Emily Washington is executive vice president of product management at Infogix, where she is responsible for driving product strategy and product roadmaps. Since joining Infogix in 2002, Emily has worked closely with product development teams and customers to drive the introduction and adoption of all new products. Before Infogix, Emily worked at Cyborg Systems and  Emily holds a Bachelor of Arts degree from San Jose State University. She also holds a certification in graphics design from The Art Institute.