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Enterprise Data Management

How Organizational Culture Drives Successful Data Governance

August 2, 2022
| Infoverity
Ian Ferré, Associate Director, Infoverity, Inc.

In professional sports, organizational culture is often as important as the players on the team. A disciplined, supportive culture can sustain success even when a team loses a talented player or has a talent roster that’s not as strong as their opponent’s. The converse is also true, however: teams can be mired in mediocrity for years, or even decades, due to a corrosive culture, no matter how much money management invests in talent.

This isn’t true only in sports, though. In the realm of data governance, culture drives success as much as the technology itself. When a company commits to implementing data governance across the organization, it becomes a daily practice and a part of the company DNA. But data governance must be incorporated into the existing organizational culture of the business at large and of the departments that own the data, or it’s destined to wither or even fail altogether. This article examines the cultural side of data governance and why it is so integral to success.

The Culture of Data Governance

Data governance is the practice of understanding organizational data, curating data assets, ensuring data quality, and establishing processes to find, expand, cultivate, and use that data in a systematic and controlled manner. Data governance strategy sets the rules by which companies conduct data management, which includes functions like data quality and integration, metadata management, and business analytics.

Companies increasingly realize the importance of data governance, especially once they start asking where good or bad data comes from. As a result, they’re adopting tools and implementing processes to establish business-wide data governance best practices. However, their success in these efforts rests on adoption and the buy-in of those tasked with carrying it out. That’s why making governance a routine part of corporate culture is so vital.

Data governance can be successful through a variety of approaches, from tightly centralized to highly decentralized. What’s key is that the approach is a cultural fit to the people and nature of the data. In a highly regulated industry like healthcare, where data is protected by law, a centralized model may work best. In contrast, in a company with a more dispersed workforce and more localized decision-making (such as a technology company or highly regionalized business), a decentralized approach might be the right fit.

There is no one-size-fits-all solution for data governance. Company leaders must make an honest assessment of their current culture and ways of working in determining which approach to follow.

Crafting a Data Governance Strategy that Works for Your Business

If you’re looking to adopt a new governance model or adjust your existing one, there are some key questions you should ask yourself about your business:

Will the data governance initiative be funded? If so, by whom and to what extent? This is vital because unfunded mandates seldom work and only generate resentment.
How old is the company? Older companies generally tend to move slower and can be more resistant to change.
How much emphasis does our company place on culture as a whole? If an organization has a clear focus on culture and robust strategies for how it operates and handles change management, data governance usually can be added and accepted fairly readily. By comparison, if a business is more focused on transactions and profitability and places little emphasis on data, it can be difficult to introduce and enforce data governance.

Real-world Example

Our team worked with an organization that had few standards in place for how their IoT devices captured temperature data. It was being recorded in Fahrenheit, Celsius, and Kelvin, depending on the device. As a result, the data was essentially worthless in its raw state because they had no process for distinguishing the measurements or normalizing the data once it came into the pipeline. Yet, the company did have a strong culture of product development. To solve the problem, we used our deep understanding of the company to make improvements in data capture within the existing development process and helped them implement the change. This ensured all temperature data was defined the same way, allowing them to better understand product performance and ultimately develop a better product.

Your data is your identity and your greatest strategic asset and should be treated as such.

This case illustrates several things. It highlights the role your technology partners can play in helping you with governance, the benefit of a strong product development process, and the fact that data governance is not separate and distinct from the company’s identity. In today’s data-driven world, your data is your identity and your greatest strategic asset and should be treated as such.

Data Governance Tools and Technology Are Not A Cure-All

Technology, in and of itself, can solve some data governance problems. But adoption will lag if it’s used in a brute-force manner. Technology, for example, can be used to force standardization. Granted, this can fix certain problems. But such fixes don’t always get to root-cause issues or encourage lasting changes in behavior. It is far preferable for people to change on their own because they understand the benefit of governance to their work and to the business as a whole.

Technology should be used hand-in-hand with culture to generate buy-in and prove the value of governance practices. If an organization is resistant to change and to new processes, technology will have to be leaned on more heavily than in a business that has a more receptive culture. Automation can help greatly in this case. For example, a team that moves from managing their work through cumbersome spreadsheets will see obvious benefits from having a new tool that automates their workflows. Technology shouldn’t be a replacement for business processes. Instead, it should be a tool or extension of a culture that embraces the human and business benefit of workflow automation.

In a culture that has discipline, the implementation of new technology is much smoother.

If a company recognizes it does not have a disciplined data culture, technology will have to be relied upon more heavily to solve data quality issues. Conversely, in a culture that has discipline, the implementation of new technology is much smoother. Agreement around how to operate under new data governance policies will come more easily, and data quality will be higher from the start. In companies with multiple subcultures, the top-down approach likely won’t work. These organizations should provide assistance and support to staff over time and tailor a steady drumbeat of messages around why data governance matters.

The rollout of governance measures will generally take longer in a less disciplined business. But if leaders can get adoption and buy-in from cross-functional champions within key functions of the organization, and prove the value of governance, they can help to expand data governance adoption more readily. In companies that lack an identifiable cultural framework, employees may be far less engaged in the outcomes of the business and may only show up to draw a paycheck. This type of environment can directly undermine a data governance program because employees are going to be less engaged with the data and its relevance at every stage of its life cycle. That’s why it’s crucial for leaders to be honest about the type of culture or cultures the business has, so they can understand their risk and opportunity and manage data governance programs and tactics accordingly.

Creating a Shared Vision and Measuring Success of your Data Governance Framework

The most effective way for companies to tell whether or not their data governance strategy is effective and meshing properly with corporate culture is by measuring adoption. Lack of adoption is the clearest sign of failure. If there’s an attempt to set standards for the organization and adoption lags, then those implementing governance practices have either not understood the culture or been able to prove the value of the changes. In contrast, with successful implementations, belief in data governance and adoption increases over time.

Another indication of cultural integration of data governance practices is the level of enthusiasm employees have for governance — not only about the governance processes but about what they can accomplish with high-quality data. Over time, people begin to experience benefits from having better data. This creates a virtuous feedback loop that bolsters adoption. Employees are able to do their jobs faster and more effectively, which increases overall buy-in to the culture of governance.

Ultimately, there’s a cultural maturity curve for governance across businesses.

The lowest level of maturity: simple data quality and system controls. In this scenario, users are forced to care about data even if they do not because they have to begin entering data in standardized ways. In companies that don’t think about or prioritize data, even simple data quality measures can create an immediate positive impact.

Next level: the business moves from data quality to master data management. At this level, the value of data is widely recognized, everyone is operating with the same definitions of data, and data is used to deliver a better customer experience.

An even more mature business: tackling metadata management. At this point on the maturity arc, the greatest value will come from using analytics to gain new insights and build models that create better business outcomes.

Regardless of where a company falls on this maturity scale, data governance remains a bit like the sports metaphor referenced at the start of this examination. By playing to the known strengths of your business and your people, and by being honest about any weaknesses, you can create the best possible data governance playbook — one that’s as unique as the business itself and increases your odds of winning the long game of data governance.

About Infoverity

Founded in 2011, Infoverity is a leading systems integrator and global professional services firm driven to simplify and maximize the value of their clients’ information. Infoverity provides MDM and PIM Strategy and Implementation, Data Governance and Analytics, Content Management, Data Integration, Enterprise Hosting, and Managed Services that help large enterprises in the retail, consumer goods, manufacturing, financial and healthcare sectors. Infoverity, a 100% employee-owned company, is on the Inc. 5000, recognized by IDG’s Computerworld as one of the Best Places to Work in IT, as a Wonderful Workplace for Young Professionals and as a “Best Place to Work” by Inc. Magazine and Business First. Infoverity’s global headquarters is in Dublin, Ohio, the EMEA headquarters and Global Development Center is in Valencia, Spain. Additional offices are located in Germany and India.

For more information on Infoverity solutions, contact us today.

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