Typically, organisations are ingesting or generating raw data from a wide variety of internal and external sources—and unfortunately, its quality is often poor, unknown or suspect. If the data is untrustworthy, data consumers may hesitate to utilise those assets for analysis, preventing extraction of critical insights that support strategic business decisions. Even worse, producing decisions based on low quality data can lead to missed opportunities, operational indiscretion, unhappy customers, diminished revenue and reputational risk.
Beyond understanding and ensuring data quality, realising optimal returns on data assets requires a firm understanding of data from a business context. To maximise the value of data and establish widespread data intelligence from an operational standpoint, organisations require a data governance framework to improve their data's quality, usability and reliability.
The increasing importance of data governance
As businesses across every industry seek to leverage the full value of their data assets, the importance of implementing a data governance framework cannot be understated. When data governance is implemented correctly, data quality levels can be assessed and scored across the entire data supply chain, increasing the appropriate use of data to enhance business decisions and improve outcomes.
With the empowerment and enablement that data governance can deliver, business users will no longer have to rely on the IT department to handle all their data needs. But for data governance to be successful at the enterprise level, it requires buy-in and support across the organisation. While organisational acceptance, accountability, and collaboration are critical, so too are ease of data access and understanding—and analytics can play a pivotal role in data governance success.
Applying analytics to automate data governance
Tasks like ensuring data quality as data travels through the complex data supply chain, where it may be subject to alteration or transformation would typically require a large team of people.
The process can take weeks, if not months, to accomplish. However, analytics can help automate tasks like data quality and help uncover insights into data that would otherwise go unnoticed. By applying analytical techniques like machine learning algorithms to data governance, organisations can automatically detect potential data defects, rather than an employee taking the time to set a rule and comb through a vast amount of data to try and discover any anomalies.
Often, organisations aren't successful in their data governance efforts because they don't apply an all-inclusive approach to their data management strategy. They are equipped with disparate and outdated tools for data quality, data governance and analytics. Each task is performed separately, wasting time, money and resources.
Ideally, these tools should work in sync with each other to provide a complete picture and offer better control of data. Analytics can then enable data governance to act as another driver of data quality, and machine learning algorithms can continually monitor an organisation's data environment to self-learn as data quality issues are discovered and fixed.
Add data quality scoring to data quality monitoring, and data governance can help organisations develop complete confidence in their data assets among business users, encouraging utilisation of additional data analytics to empower better decision making.
A comprehensive approach to data governance
A data governance framework is the foundation of an organisation's data management strategy. It should serve as a mechanism to increase understanding and collaboration to maximise the value of data assets.
Successful organisations need a business-focused, centralised data governance framework that focuses on a company-wide understanding of data across the entire enterprise. With the proper tools, organisations can enable business users across that enterprise to easily understand the data landscape. When data consumers know where data is located, how to access to, who owns it, and what it means, they are enabled through understanding to leverage it and drive success as they perform important business operations.
Through simple visualisations and quickly navigable workflows, data governance should allow IT and business users alike to easily define, track, and manage all aspects of their data assets.
Knowledge-sharing and user empowerment through transparency across the enterprise fosters enterprise-wide collaboration. Today, all organisations must ensure that their data delivers a competitive advantage and does not turn into an expensive liability. Between the combination of people, processes and tools, a comprehensive data governance programme can ensure that organisations derive maximum business value from their data assets.
Contributed by Emily Washington, senior vice president of product management at Infogix.
*Note: The views expressed in this blog are those of the author and do not necessarily reflect the views of SC Media UK or Haymarket Media.