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Is data governance undermining your business optimization efforts?
Here’s how to fix it.
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Organizations who reach peak business optimization usually have highly functioning, well-defined, and intuitive data management processes. These processes codify how information is captured, stored, accessed, and used—and are the foundation of effective performance monitoring, program management, and decision-making. Without data management processes, organizations don’t just miss opportunities to improve operations, they create waste across the enterprise. In our experience, organizations with poorly functioning or non-existent data management processes have an ineffective data governance strategy.
The pitfalls of poor data governance
Ineffective data governance creates inefficiencies and missed opportunities that can ripple across the enterprise. Imagine an organization that wants to develop a generative AI tool that uses company data. If the data are not organized, uploaded, managed, and structured to integrate easily into the tool, it won’t get used, which lowers return on investment.
Or one that does its financial reporting without a solid data governance strategy. The use of inaccurate or inconsistent data can lead to misguided business decisions, compliance issues, or financial penalties. Finally, imagine an organization that wants to identify business optimization opportunities, but has incomplete information because every department formats its data differently. Leaders quickly discover that it’s hard to make good decisions with inconsistent, missing, or inaccessible data.
Five data governance fundamentals to optimize your business
To avoid these pitfalls, test your organization’s data governance against these five fundamentals to assess whether it is helping—or hurting—your business’ efficiency.
1
Assign clear ownership
Data governance codifies data accountability. The irony is that in many organizations, no one is accountable for this function. Does it belong to IT or the business? The answer to this question is often, “No one knows.” Uncertainty exists because data are kept in different systems managed by different functions, which in itself causes tremendous operational inefficiencies. When no one has the authority to set shared standards, there isn’t a clear practice that everyone respects and follows. Rather, it’s a loosely defined promise that you hope people keep.
Who has the authority to govern data collection, storage, access, and use across the enterprise?
2
Know who needs the data
Designing data governance in isolation of people is disastrous in regards to optimizing your business. It becomes purely academic, based on theories of how things are supposed to be rather than how they actually are. This is why it’s critical to develop data governance models that align with the organization’s culture, behaviors, needs, systems, and processes—warts and all. It’s key to know who needs access to what data when and for what specific purpose. In addition, organizations should map where data are located. This data “inventory” creates a foundation for designing effective data governance, and it requires both top-down and bottom-up discovery to be complete.
Who needs to use the data and for what purpose?
3
Solve for people’s bad habits
Even the best data management strategies can fail if people bypass protocols, misuse data, or don’t follow best practices. Employees often “MacGyver” their own methods for storing and accessing data when official processes are too complex or slow. This creates siloed data that isn’t properly governed, increasing risks and reducing data reliability. Data quality clearly depends on people, and organizations pay the price of unreliable insights that undermine decision-making when they don’t enforce good data hygiene. To avoid this, the key is to plan new models with proper controls—keeping a laser focus on creating a positive user experience. The goal isn’t to develop a top-down system of complex rules and policies. It’s to make the right behaviors the easy behaviors for employees to follow.
How can data governance model designs minimize risk of non-compliance?
4
Educate people and evolve the culture
Developing data governance is only the first step to boosting efficiency around data use. Long-held behaviors (and bad habits) won’t change on their own. Education and change management programs that communicate data governance practices and explain why they exist and the benefits they deliver are essential. It is also key to strengthen organizational data literacy to cultivate proper data management mindsets. Doing this provides even more context into the value of data governance. The more that people understand the “why,” the more likely they are to comply. All of this is ultimately a matter of embedding the right behaviors into the organizational culture. Governance is just the rules of the road. Adoption happens when the rules are infused into the culture.
Do our people understand why data governance matters?
5
Never stop adapting data governance strategies
The business environment is dynamic, and data governance must be too. It’s essential to conduct regular reviews of governance policies and practices, updating them as necessary so they remain relevant amid changing business needs, market shifts, and technological advancements. By focusing on how data governance can help you continuously optimize your business operations, organizations can avoid being caught unprepared and create a living framework that promotes excellence in data management.
Do our people understand why data governance matters?
The big takeaway
In today’s environment, strong data governance is an unsung hero of business optimization. It provides clear standards and processes for managing data throughout its lifecycle. With better data quality, consistency, and accessibility, organizations can trade misunderstandings, rework, and redundancy for greater operational efficiency.
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