It’s Time to Take Data to the Next Level
The self-service technology culture allows each business user to access data for analytical purposes. Yet it has created an abundance of rogue data sets across enterprises that may contain outdated or inaccurate information and that fall outside of organization’s data governance structure. With the introduction of the right data intelligence strategy and stewardship, enterprises can improve data quality, build trust and enable collaboration that will impact the bottom-line.
Data is the lifeblood of an organization. It is at the heart of executive decision-making, risk evaluations, customer engagement, regulatory requirements and efficient operations. Yet, not all data used for these business decisions and reporting is made equal.
According to a recent TDWI survey report, “Reducing Inefficiency and Increasing the Value of Analytics and Business Intelligence,” only 11 percent of respondents said they were very satisfied with their companies’ investments in data and analytics projects to meet strategic goals for enabling data-driven decision-making or actionable customer intelligence.
The problem is that the self-service culture has created an abundance of rogue data sets and proliferated data across the enterprise where governance officers and IT professionals have no control over who is using the data and how they’re using it. Business users may be using outdated or inaccurate data for their analysis.
And there is no way to reel back data access as these same self-service analytical, visualization and data preparation applications allow enterprises to be nimble and use data for finding meaningful business insights. The trick is finding the balance between open data access and internal data control: Effective governance and data quality improvement only come when the right data intelligence strategy and stewardship is in place.
Keeping the data together
Most business users state they can access the data warehouse or data lake, but many also assert they can’t find the right information for analysis. TDWI’s report found that 44 percent of individuals can find the right data; one in five can access trusted data sources without IT support; and only 18 percent can track data lineage to a source, which leads to a decay in analytical confidence.
When these users struggle to access relevant data for analysis – information that is correct and up-to-date – organizations suffer from inefficiency and error-prone judgements. Individuals say that 61 percent of their time is being spent on finding and preparing data. And even when they turn to peers for assistance, it is done in ungoverned formats, such as email, word-of-mouth and internal networks.
Successful analysts and business users require data stewardship assistance that will eliminate hurdles and provide them with trusted, well governed sources for analysis. TDWI also found that a mere five percent are using data marketplaces to build trust and better access. A centralized data management system is critical for self-service analytic tools to be effective, as it brings trusted sources together in an easily accessible format.
Strategizing the right plan for data use
And while making data accessible for analysis is critical, enterprises need a data steward who will institute a more formal data intelligence strategy that enables data sources to be found, highlighted for project relevancy and tapped for shared data knowledge. This strategy wraps a governance banner around data use, providing greater data lineage for compliance reporting.
Fifty percent of respondents stated that their organizations do not have a formal data governance strategy, which directly impacts how data usage is tracked for a complete data lineage. This lineage is key to increasing trust in data; yet nearly two in five (38 percent) are only somewhat confident about the lineage of the data used in reporting and analytics, and 18 percent report no confidence. Lineage is critical for regulatory adherence – whether for GDPR or other compliance reporting.
An effective data steward understands these governance and regulatory requirements and improves the efficiency of data access and analytics processes to increase the value of any data project. The TDWI survey found that most individuals believe their current governance strategy focuses mainly on regulatory compliance, leaving a significant opportunity open for enterprises to up their game: Only 36 percent of respondents find that data stewards are helping with the selection of data sets, and 22 percent indicate they share feedback or rate the analytical outcomes shared across the company.
Data stewardship and sharing insights is essential to increasing trust in data, enforcing data governance policies and expanding collaboration across the enterprise. An effective data leader will ensure that business users leverage their self-service analytics, data preparation and visualization tools for effective communication with executives and data-driven decision-making.
Wrapping it together
Enterprises have a wealth of data at their fingertips, but seldom is it properly or correctly leveraged to bring the company to the next level. Not only are data stewards essential for ensuring regulatory compliance and that governance policies are followed, but they are the innovative leader in how data is used and analyzed. Their implementation of data intelligence strategies will allow analysts to thoroughly analyze data related to the customer experience, departmental operations and overall company management. By taking steps to improve data quality, collaboration and intelligence across the organization, firms are in a better position to call themselves a “data-driven” company.
Jon Pilkington brings more than 18 years of experience in the business intelligence market to his position as Chief Product Officer at 
