By Clarity Insights,
Clarity Insights is a strategic partner to the nation's leading data-driven brands.


Data analytics is a powerful tool for any organization, but if you don't cover all of your bases--including data governance--you will run into significant problems.

Businesses across the globe are ramping up their investments in advanced analytics tools. A February 2017 Gartner report predicted that the global business intelligence and analytics software market would be worth $18.3 billion by the end of the year.

Data governance can derail big data analytics projects if not properly accounted for.

Moreover, that figure will reach $22.8 billion by 2020. In eagerness to get advanced analytics projects off the ground, are companies overlooking a critical requirement? Data governance, in particular, can make or break an analytics project.

Although not quite as scintillating an endeavor as leveraging advanced analytics solutions to streamline operations and solve long-standing problems, data governance is essential to effective and secure big data strategies. Data governance casts a long shadow, covering everything from cyber security to data integrity and availability. Neglecting this aspect of data management could be disastrous, yet many organizations continue to do so.

Let's take a look at three common data governance failures and how you can avoid them.

1. Collecting data with no clear intent

Some organizations never get past the first step in their analytics program. They collect data, deposit it into a data lake and then wait for an opportunity to leverage that information. Without a clearly established goal, however, there's a good chance that information will continue to languish. The problem, as outlined by InformationWeek contributor Lisa Morgan, is the inability to properly assign value to data.

Any bit of data should have a clear goal attached to it.

When project stakeholders are unsure what information is useful and what can be safely discarded, they'll stockpile everything, resulting in a massive, unmanageable data repository. From a security and compliance standpoint, that's problematic, especially with the European Union's General Data Protection Regulation looming on the horizon. GDPR, which goes into effect next year, requires all European consumer data to be collected for a specific purpose. That means any company overseeing a large data lake containing such information, with no clear goal in sight, will be in violation and subject to financial penalty.

Any bit of data an organization collects should have a clear goal attached to it. Goals mean businesses can stay compliant with GDPR and other data governance regulations while also mitigating the deleterious effects of a sprawling repository.

2. Too few or too many data stewards

Another data governance failure is a lack of a data steward to watch over the whole endeavor. As TechRepublic's Rick Vanover explained, this task should fall to a data steward who oversees various aspects of data governance, including delivering relevant information to stakeholders and ensuring compliance. Without that individual or team, there is no established order for managing data, and that will lead to inefficiencies and ultimately impede analytics efforts.

Conversely, companies can have too many cooks in the kitchen, which causes problems as well.

"[Having too many data stewards] may increase the risk of things falling through the cracks or being incorrectly governed," Vanover stated. "This is can be especially problematic in a situation involving the deletion of data that one group needs and another group doesn't need."

Data stewardship should be crystal-clear to avoid any confusion about who is in charge of what. Responsibilities should be explicitly defined so everything is accounted for and assigned. 

Having too many data stewards can be as problematic as having none at all.Having too many data stewards can be as problematic as having none at all.

3. Failing to prioritize total commitment

Organizational buy-in is a must for any advanced analytics initiative, and that holds true for data governance. Commitment goes beyond simply creating space in the budget for hiring a data steward or forming a governance committee, though. TechTarget contributor Rich Sherman explained that business leaders have large roles to play here, establishing business goals and criteria for success in the form of key performance indicators. Without that support and commitment from the business side of operations, analytics stakeholders will have a difficult job outlining effective projects and measuring their success.

Furthermore, if data governance involvement isn't prioritized, it will likely fall to the wayside for business leaders who have plenty of other concerns to tackle. Organizational buy-in should be all-encompassing, and everyone from the executive suite to IT staff members should fully support data governance and analytics efforts.

Dodging these data governance failures can be made a lot easier by working with an experienced third party. At Clarity, we know how to set up analytics projects for success, and that includes optimizing data governance strategies with industry best practices. Contact one of our experts to find out more today.

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