One might assume that cybersecurity threats are the No. 1 risk to businesses. According to Gartner’s annual Audit Plan Hot Spots Report, however, data governance now tops the list of concerns cited by chief audit executives. Almost 80 percent of executives surveyed said that failure to fully utilize data will cause their organizations to lose competitive advantage, creating significant business risk.
Data governance is an overarching framework for managing the availability, usability and quality of an organization’s information assets. It recognizes the strategic importance of data, and establishes policies and procedures for consistent data management across the enterprise.
An effective data governance program not only reduces risk but boosts the bottom line. High-quality data provides business insights that can increase operational efficiencies, enhance the customer experience and drive new business models. Poor data quality, on the other hand, costs organizations $15 million a year on average, according to a Gartner study.
But while organizations recognize the importance of data, few have implemented a data governance framework. In a global survey conducted by TRUE Global Intelligence for Splunk, 56 percent of respondents admitted that “data-driven” is just a slogan in their organization, although 79 percent believe they must transform that slogan into reality.
Problem is, establishing a data management framework is an enormous undertaking compounded by huge volumes of siloed data. Most organizations have an application-centric view of IT that has resulted in hundreds of discrete data stores. Each application has its own database, with a schema that’s specific to that application. Correlating that database with data stored by other applications is mindbogglingly difficult.
Then there’s the “dark data” — all of the unknown and untapped data generated by systems and devices and user interactions with applications. In the Splunk survey, 60 percent of respondents said that more than half of their organization’s data is dark, and one-third said that more than 75 percent of their data is dark. Most organizations lack the skills and resources needed to locate, prepare, analyze and use dark data.
The first step toward effective data management is shifting from an application-centric to a data-centric approach. This shift is as much cultural as technological. Organizations need to understand the kinds of data that can help drive their business strategy, and prioritize the capture, storage, organization and correlation of that data. The applications that enable those functions are secondary to the data itself. The goal is a “single version of the truth” that can be tapped across the enterprise.
Next, organizations should implement tools and processes that enable access to and analysis of data. This was supposed to be the role of big data analytics but those systems are unable to deliver the real-time insights organizations need today. Artificial intelligence will be required to manage and synthesize data, ask the right questions, and extract maximum value from data assets.
Not surprisingly, Gartner has identified AI-augmented data management as a top technology trend. Machine learning tools can be used to automate many of the tasks associated with ensuring data quality, integrating information sources and managing data across the enterprise.
Data is an organization’s most valuable IT asset, and poor management of that data creates significant business risk. Organizations need to prioritize the implementation of a data governance strategy that leverages automation and AI to provide high-quality data for strategic decision-making.
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