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3 Data Governance Challenges and How to Address Them

Authors Photo Precisely Editor | April 9, 2023

In this era of big data, data governance is taking on a more important role than ever before. Data governance drives competitive advantage by lowering costs, increasing visibility, and improving customer and vendor relations. Organizations that neglect data governance challenges, risk developing longer-term problems that can act as a drag on their effectiveness.

Data governance is no longer a nice-to-have; it is a necessity. As the volume and scope of data grow and as unstructured data comprises a higher percentage of organizational data assets than ever before, the challenges of data governance grow larger.

Let’s look at some of the key challenges of big data governance and how today’s leaders can address them.

1. Lack of Organizational Commitment to Data Governance

As author and speaker Simon Sinek has pointed out, there needs to be a clear “why” to motivate productive human activity. The case for data governance is crystal clear, but in many organizations, the “why” behind data governance is not well understood.

Let’s begin with data quality. Ask the people who rely on data every day, and you’ll probably hear about a lot of examples where poor data quality resulted in bad business outcomes. For the marketing department, duplicate customer records result in money being wasted sending two or more promotional mailers to the same household.

For the shipping department, bad addresses mean delayed shipments or carrier surcharges, which can add up to a lot of money and often result in dissatisfied customers.

In the C-suite, information is more valuable than gold. Poor data quality at scale translates to poor business decisions. As the ability to summarize, manipulate, and draw insights from data improves, data quality must keep pace.

Then there is the issue of compliance. The European Union’s General Data Protection Regulation (GDPR) changed the game for companies around the world, establishing rigid guidelines for collecting, securing, retaining, and sharing data. California’s Consumer Privacy Act (CCPA) has established similar guidelines, and numerous jurisdictions around the world are considering legislation to protect the privacy and security of personal information.

That means companies must have policies and procedures in place to assure that data is secure from unauthorized access and that requests to delete personal information are acted upon quickly and accurately. Organizations must also know where data is stored. In a recent EU court decision, it was mandated that the personal information of EU citizens could not be stored on servers in the United States, for example. Data governance has become a legal requirement.

Fines for violations of GDPR can be significant, in addition to generating negative publicity.

Organizational leaders should take the time to articulate a clear “why” behind data governance initiatives. That means digging into the details, talking to departmental leaders, and learning more about the specific data governance issues that affect them.

Although the case for data governance is very clear to some, it is far less obvious to others. It is well worth investing some time and effort to build organizational clarity and alignment around data governance initiatives.

2. Data Overload and the Rise of Unstructured Data

As the volume and variety of data increases, the challenges of effective data governance will only grow more difficult.

Digital transformation technologies have resulted in an explosion of new data sources. Mobile phones, for example, have made it possible to understand human activity and mobility at a granular level that would have been inconceivable just 15 years ago. Location intelligence can help companies better understand detailed foot traffic patterns – not just the volume of visitors to a location, but information about who is visiting and when. That’s a game-changer if you have a good handle on your data.

The Internet of Things (IoT) is providing up-to-the-minute information on machinery and equipment, shipments in transit, vehicles, and more. That data can be used to make supply chains more efficient, to improve vehicle safety, and to decrease machine downtime with predictive maintenance.

Straight lines entering into a box, and then exiting as circles.

Then there is the challenge of unstructured data. Videos, e-mails, social media posts, and similar unstructured data sources create a whole host of new challenges in data governance.

As the adoption of artificial intelligence and machine learning accelerates, organizations must be prepared to rein in the chaos. That means taking a comprehensive and proactive approach to data governance. The process begins with a complete inventory of data assets, identification of the most important elements of that inventory, and a prioritized approach to moving forward with data governance.

Read our eBook

Fueling Enterprise Data Governance with Data Quality

The time to start the journey is now. To learn more about how your organization can achieve excellence in data governance, download our free report.

3. Lack of Dedicated Ownership

In many organizations, it is a commonly held belief that the IT department owns data governance. While IT clearly has a role, organizations should recognize that data governance spans multiple domains within the business and is best served by a dedicated ownership model.

In other cases, data governance occurs across a collection of silos in which marketing owns the CRM database, accounting owns core financials, and logistics looks after inventory and fulfillment data. Unfortunately, this results in a disjointed approach to data governance, which is to say, no data governance at all.

Cross-functional collaboration is key for successful data governance initiatives. Someone whose primary role is data governance needs to take the lead. This, in turn, requires senior management alignment and the willingness to put budget, resources, and enforcement authority behind the data governance role.

When organizational leaders are successful in communicating the “why” of data governance, the other two pieces generally follow more easily. As big data capabilities continue to increase, the mandate for effective big data governance becomes more and more obvious. So, too, does the realization that effective governance requires a commitment – that it cannot be a part-time endeavor fulfilled by IT, marketing, or any other department whose primary focus is elsewhere.

To learn more about how your organization can achieve excellence in data governance, read our eBook “Fueling Enterprise Data Governance with Data Quality”.