Organizations can transform their raw data into real competitive advantage, provided it is accurate, trustworthy and properly managed. Without high-quality data, however, businesses may lack the ability to extract insights that generate good strategic and operational decisions. Worse yet, business decisions driven by flawed data and false assumptions can lead to lost opportunities, operational missteps, customer dissatisfaction, lower revenue and reputational risk.
Beyond the effect on business decisions and operational insights, data quality can negatively impact regulatory compliance efforts. If an organization has inconsistent, inaccurate, duplicate or missing data, they can’t ensure that the proper controls and checks are being applied in accordance with regulatory directives. Worse yet, they may not even realize they’re reporting inaccurate data. This can result in noncompliant data and processes, creating the perfect storm of penalties, substantial regulatory fines and additional reputational threats.
According to Gartner research, the average financial impact of poor data quality on organizations is $9.7 million per year. Additionally, organizations that Gartner has surveyed indicate that poor-quality data is costing them on average $14.2 million annually. The potential costs of bad data have never been higher.
To maximize the value of data assets and mitigate these risks, organizations should implement a data governance framework to improve their data’s quality, usability and reliability. When data governance is implemented correctly, it applies data quality across the data supply chain to increase the use of data to drive better business decisions and bridges the technical-to-business divide by engaging all parties across the organization. Data governance can not only assess data quality levels, but monitor for data quality improvement. For users to trust and use data, they must have confidence in its quality. The other part of the equation is that they must understand it. One of the biggest benefits is that data governance provides a complete picture of an organization’s data assets, including what data is available, its owner/steward, lineage and usage, and data’s associated definitions, synonyms and business attributes. Adding quality to governance provides organizations curated data.
Capturing a complete inventory of an organization’s data landscape helps business users gain valuable insights, achieve efficiencies across financial, social, and economic aspects of governance, and identify the risks associated with its use across business applications. In addition, it helps organizations navigate the increasingly complex regulatory environment.
As businesses across every industry seek to leverage the full value of their data assets, the last few years has brought a dawning realization of the importance of implementing data governance. Data democratization has taken hold and implementing data governance using curated data can have substantial positive impact. But as organizations look to break down data, understand it, and make it available to the organization within a governance framework of policies and business rules for data management, many underestimate the significant role analytics can play for data governance success.
Leveraging Analytics to Help Automate Data Governance
Analytics are key to developing a comprehensive and successful data governance framework, and the reason is simple: automation. Analytics help automate critical tasks that could typically take large teams weeks or months to accomplish, and may uncover additional insights into data that would otherwise go unnoticed. By applying analytical techniques such as machine learning algorithms to data governance, organizations can automatically detect data anomalies, rather than a person setting a rule and sifting through a vast amount of data to try and identify outliers.
Due to budget and resources, organizations often take a patchwork approach to their data management strategy, and data quality tools are often implemented separately from data governance. But analytics can enable data governance to act as another driver of data quality, as machine learning algorithms can continually monitor multiple data environments and self-learn as data quality issues or anomalies in data are identified and resolved. Couple this ongoing quality monitoring with data quality scoring, and your data governance program can develop deep trust in your data assets among business users, encouraging utilization for other data analytics to drive better business decisions and outcomes.
From a compliance standpoint, analytics are equally important to a data governance program. Traditional data governance does an excellent job of implementing compliance measures to data, policies, and procedures, but falls short if you want to know where potential areas of noncompliance may lurk. But analytics-enabled data governance will actively and automatically monitor to identify these areas, allowing you to proactively investigate and resolve any issues before fines or penalties are a factor.
A timely example of this is the General Data Protection Regulation (GDPR). To ensure GDPR compliance, organizations that handle or process data of European Union (EU) residents will soon need to provide governance around personal data documentation, identification, tracking, and usage approval. By delivering enterprise-wide control and visibility into personal data processing risk areas, automatically identifying where proper oversight may be lacking, and utilizing machine learning to account for any hidden personal data, organizations may streamline GDPR compliance.
An All-Inclusive Approach to Data Governance
A data governance framework should serve as the foundation for an organization’s data management strategy, and provide a complete view of an organization’s data landscape, allowing stakeholders to easily define, track, and manage all aspects of their data assets. Strong data governance enables collaboration, socialization, knowledge-sharing, and user empowerment through transparency across an enterprise, bringing business and IT together to optimize the data supply chain and leverage data’s full potential.
Bottom line, data governance is about increasing understanding and collaboration to maximize the value of an organization’s most important asset. Analytics-enabled data governance helps you streamline your overall data management strategy, integrating data quality and compliance initiatives to ensure that as data is ingested or created within your organization, and flows through processes, systems, and environments enterprise-wid. In this way, users can rely on its accuracy, consistency, completeness, and trustworthiness. This will facilitate truly successful data governance: maximum data reward and quality with minimum compliance risk.
About the Author
Emily Washington is the Senior Vice President of Product Management at Infogix, where she is responsible for driving product strategy, product roadmaps, and vertical solution initiatives. Since joining Infogix in 2002, Emily has worked closely with product development teams and customers to drive introduction and adoption of all new products. Emily has been instrumental in creating Infogix’s vertical solutions strategy and bringing to market the Affordable Care Act solutions. Before Infogix, Emily worked at Cyborg Systems and Respond.com. Emily holds a Bachelor of Arts degree from San Jose State University. She also holds a certification in graphics design from The Art Institute.