Data is messy. It comes from many internal and external sources, traverses multiple systems and applications, and is subject to a multitude of transformations as it’s leveraged in many ways throughout a business enterprise. It doesn’t take long for data’s origin, age, quality, and even meaning to become obscured or unknown.
It stands to reason, then, that big data only amplifies the potential disarray. The sheer volume of data that organizations are collecting and storing today has resulted in a proliferation of business problems caused by mismanaged and misunderstood data. As an increasing number of organizations develop big data environments, they must also consider that in the age of big data, data governance is of the utmost importance to break through the clutter and extract valuable business insights from their data assets.
Data governance is the formal orchestration of people, processes, and technology that allows an organization to leverage data as a business asset. It is about ensuring understanding and properly managing data to enable organizations to derive the maximum value from it, while juggling adherence to internal policies and external regulatory demands. But there isn’t just one data governance approach.
Implementing a data governance strategy is not without challenges. While there is growing acknowledgment of the importance of data governance that will likely buttress organizational support, there may very well be differing visions on how that should be accomplished and the data governance approach that will best serve organizational needs. But different approaches come with different challenges.
When considering a formalized data governance approach, numerous factors need to be considered such as the size of the organization, existing governance initiatives, the type and scope of data environments, etc to identify the right data governance approach. Some organizations likely already have massive amounts of data, and rely heavily on their IT department to spearhead governance efforts because that’s where the resources and expertise reside. Others use a business approach because they may be responding to regulatory needs or that is where the expertise resides. Let’s take a look at the common challenges we’re seeing in the marketplace with these two approaches.
The IT Approach: With this data governance approach, IT is in the driver’s seat when it comes to data governance. Areas such as compliance are of course involved from an operational and oversight perspective, but IT is tasked with developing and implementing a data governance solution. On the positive side, the data governance tools favored by IT are often very effective at increasing IT’s ability to understand a wide variety of information about their data like its lineage, where the data came from, how old it is, what’s the quality of it, and transformations that have taken place. But on the business side, their highly technical nature makes these tools about as effective as asking me to fix my car. I like cars, but you don’t want me working on them. Business users get lost in the technical aspects of the tools, don’t understand SQL and other technical jargon, and usually can’t use the tools without IT assistance. This has the net effect of both draining precious IT resources and discouraging business users from leveraging valuable data assets.
The Business Approach: Business users are the ones who need to derive business insights from data, so tackling data governance from a business perspective seems a logical approach. But business users too often rely on spreadsheets and SharePoint to maintain policies or processes and manage data governance when they take the lead on data governance or if they find that IT data governance tools are overly technical. But these efforts are usually undertaken at a departmental level, involve massive manual effort, and are neither scalable nor sustainable. As the business grows and more departments leverage these resources, they have a difficult time keeping control of documents. There are no rules for maintaining or updating data, no one knows who’s responsible for what term or definition, and there is no way to track lineage. This data governance approach tends to build frustration, siloed and outdated information, and creates a nightmare as users try to keep governance current through manual processes. It also doesn’t give IT the information they need such as technical lineage and transformation logic.
The Hybrid Approach: As is often the case, the ideal data governance approach probably borrows the best of both, minimizing the challenges of each. Businesses can combine the two approaches for a complete data governance approach built to meet the evolving data governance needs of both IT and business users.
Data governance is fundamentally about increasing understanding, and it needs to serve the demands of an entire organization. An all-inclusive data governance approach that creates a tech-savvy, business-friendly data governance solution can break down the communication barrier between the business and IT department and breed collaboration between the two.
The right solution should deliver complete transparency into an organization’s data landscape allowing any user to define, easily track and manage all aspects of their data assets. It should include a comprehensive business glossary that defines not only the data vocabulary across an entire enterprise, but ensures consistency of business terms and clarification of any variations. Data governance needs to not only promote data understanding, but also define ownership and accountability across the organization. Finally, it should enable easy collaboration by providing business users with a simple interface, clear visualizations and easily navigable workflows to promote communication between all data stakeholders. With full visibility, the entire organization can gain valuable insights into not only the details of their data assets, but how to leverage those assets most effectively.
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