You’d be hard pressed to find an organization today that doesn’t have some kind of data management program, and if you do, they might very well be on a list of companies filing for Chapter 11. Why? Because most businesses understand that data is the key to revenue growth and longevity, and that they must find a way to leverage those assets for insights to gain competitive advantage. What some organizations are only recently learning is that any effective data management strategy requires data governance as an essential element, because it will make or break how successful they are with their data-driven initiatives.
With a successful governance model, organizations ensure business users have high-quality data they can easily understand and leverage to make complex business decisions. However, not every organization is equipped with a prosperous data governance model.
Many organizations are still leveraging overly technical, IT-centric tools, or outdated methods like Excel spreadsheets or SharePoint documents. With data being accumulated, disseminated and deployed at an accelerated pace, more often than not, leveraging unsuitable tools results in mismanaged and misunderstood data, and creates a multitude of business problems.
The goal of every business is to make their data an actionable asset that can drive better business decisions. Organizations need a foundation of data governance that makes their data accessible, understandable and valuable to every business user. With an optimized enterprise approach, businesses can assure they’re positioned not only for success today, but long into the future.
In the past, data governance was often a task assigned within specific departments. One department may have tackled regulatory requirements and compliance issues, while another possibly used data governance to define their data resources. Different departments used disparate legacy tools for various tasks in combination with spreadsheets and SharePoint documents. Resources were frequently neglected and updated irregularly, creating widespread mistrust. Data governance tools used by IT were often highly technical, leaving business users unable to use them and uncertain of overly technical jargon. This mistrust and misunderstanding around data governance understandably led many to have a negative impression of the role and reliability of such programs.
When data governance is done right at an enterprise level, business users are empowered to quickly find, understand and apply the right data sources to be analyzed and acted upon, and they’re assured that the integrity of those data resources are maintained. Today, data governance success is predicated upon a collaborative, enterprise approach that encourages self-service and establishes data trust among business users. It requires organizations to bridge the IT and business divide, and create unity among data owners, stewards and consumers to remove the ambiguity faced by business users as they perform important business operations and make vital decisions.
Modern enterprises generate a ton of valuable data for every department, meaning data is now the concern of everyone. Organizations must engage all parties and clearly define roles and responsibilities among data owners, stewards and users to ensure a full understanding of data across the entire enterprise and data supply chain. To achieve this goal, organizations must also move away from legacy tools toward a centralized solution suite that delivers a business-friendly perspective of an organization’s data assets to build understanding and encourage collaboration.
What has changed from just a few years ago? There is both a mindset and a tool-set strategic shift underway that is changing how leading thinkers are reconsidering their approach to data governance. Governance at its core is about change management.
Change is encapsulated around four interdependent areas comprising people, process, technology and data. While this isn’t new, what is also changing is the convergence of two historically separate disciplines that were once mutually exclusive and now are beginning to overlay. Take data quality, for example. This underappreciated function, traditionally relegated to the back office, was typically disconnected from front line business users. These users either assumed the data was of good quality, or perceived—based either on gut feelings or bad experiences—that the data was untrustworthy. This perception issue is based on past experiences and to change a perception of the quality of data we will need more than just facts.
Let’s assume we had governance visibility into data quality metrics to solve the “facts” part of the equation, so that business users could be confident that a data set was good or bad. Would that be enough? Not entirely, just as in the boardroom people don’t unequivocally trust the numbers and want more than fancy meter dials to show data is greater than some threshold to indicate if it’s good data. Business users also want three additional things that fall under people, process and technology. They want to interact with the data owner, they want to initiate governance workflow process into data quality, and they want the technology to automate notifications when data changes from good to bad. Without the intersection of data governance and data quality, this will not be possible.
Walk into most organizations and you will not see the strategic shift in data governance overlapping with data quality. A few simple questions to assess the maturity are:
If you can’t answer “yes” to three or more of those questions regarding your governance tool, it’s safe to assume you haven’t undergone the strategic shift in data governance with data quality. Your data management landscape likely looks more like two separate silos.
While this strategic shift converging governance and quality sounds good in principle, there is even better news. To see how a change management initiative to tie data governance and data quality together improves operations and outcomes, let’s look at two real world examples:
Finding a business transformation initiative, like one of the examples above, is one way to make a business case to align to this strategic shift in data governance to be inclusive of data quality. And the best news is that it’s must easier to gain widespread stakeholder support for a change management initiative when it’s tied to solving key business problems that just so happen to be solved by a one-two punch – data governance and data quality.
If you would like to learn more about fostering collaboration through data governance and data quality, please download the data sheet below.
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