Data governance has moved to the forefront in today’s discussion of all things data, including data management and data security. But to do data governance effectively, organizations shouldn’t just jump in feet first. Instead, they first need to develop a data governance framework that will provide a model for how they address data issues like data security and data privacy.
What is data governance?
Before even developing a framework for data governance, it helps to understand the term first. According to the Dataversity, data governance is the “collection and practices and processes which help to ensure the formal management of data assets within an organization.” Put another way, data governance is the practices and processes for the formal management of an organization’s data.
Truth be told though, there’s no universal definition of data governance, and any definition really depends on how the term is used. Some use data governance to refer to the standards and guidelines that govern data management. Others use the term data “governance” to refer to how data is stored and maintained. For the purposes of building a framework, however, data governance refers to the policies and processes that govern how data is managed.
Why is data governance so important?
Data governance has risen to the forefront only in the last few years. But it didn’t get there all by itself. It was pushed there by events. More specifically, a number of high-profile data breaches over the past several years have pushed data governance to the top of the agenda of many information technology professionals.
From these beaches, and from the overall concern about data privacy, have arisen laws like GDPR and CCPA. These are placing tighter constraints around how organizations use the personal data they collect, how they manage that data, and even what data they can collect. As a result of the increase in data breaches and the advent of data privacy laws, many organizations are adopting data governance frameworks to formalize how their data is managed.
Why a framework for data governance software is needed
It’s become a cliché, but today’s organizations run on data. And we are producing ever-increasing amounts of it every day. In fact, over the last two years alone, 90% of the data in the world was generated. With the rise of our connected society and the advent of the Internet of Things, that volume of data will just keep growing faster.
What a data governance framework does is put a structure in place for an organization to manage it all. Without such a framework, organizations are more likely to treat their data haphazardly, developing policies around such issues as data privacy and data security reactively and randomly, rather than proactively in a systematic fashion. One can even think of data governance as an insurance policy, helping companies mitigate any risk that might arise from their data and reducing their liability.
Four pillars of the data governance framework
For organizations to make their data a successful asset, there are four pillars to consider:
- Distinct use cases: it is essential to connect data governance to business results by considering revenue, cost and risk.
- Quantifiable value: the impact of the data governance implementation needs to be measurable.
- Product capabilities: data governance capabilities should provide for individual needs within data processing.
- Deliverable model: data governance should be a scalable service which means that the more cases addressed the more value the organization brings.
What are some data governance benefits?
How will your organization reap rewards from instituting effective data governance? Let us count the ways…
- Data consistency means everyone is sharing the same data, no matter where they are in the organization, which prevents a host of operational and risk management issues.
- Data quality improvement means your decisions and operations are running based on accurate and complete information, rather than misleading or outdated data.
- Better decisionmaking thanks to the use of optimized data, so decisions from the C-suite on down can be precise and in touch with actual conditions and operational realities.
- Enhanced business planning as enterprise leaders are able to better project ahead in making key choices.
- Improved financial performance since data governance eliminates errors (and possibly penalties), and optimizes data-driven decisions and systems so they’re delivering better performance.
Key questions to ask before developing a model
Before even starting to develop a data governance framework, ask yourself some fundamental questions:
Why do we need a data governance framework?
Before even starting out, you need to ask: Why does my organization need a data governance framework? What’s motivating you and your organization to have one? Is it because it’s the shiny new tool someone heard about and now wants? Or are you responding to an adverse event that happened with your data and you want to ensure it doesn’t happen again? Regardless, first articulate precisely why you need a framework around data governance.
What does my current data governance look like?
You need to understand what your organization’s current data governance framework looks like. Do you even have one? And if you do, what sort of controls and policies are in place? What’s more, you need to understand if your current data governance is up-to-date and reflects current best-practices…or if it was written several years ago and hasn’t been updated.
What are we trying to achieve with a data governance framework?
This goes beyond the general question of whether or not you need a data governance framework; what do you specifically hope to realize by having one? In short, what is the end goal of having a data governance framework? What KPIs can I attach to its implementation? It’s a fundamental question that will help determine what sort, if any, of data governance framework will best help you reach sound business objectives.
What does the future state of my data look like?
Once you’ve identified what you’re trying to achieve with data governance software, you next have to identify how your data might look in the future – and how that differs from today. What does your information technology group have planned for data management or the next three years, or the next five years? What kind of customer/consumer data will you even have on hand, in light of expanding regulations like the GDPR and CCPA? These are the sort of questions that will help you accurately frame the future state of your data.
Build a cross-disciplinary data governance team
Frameworks aren’t built by an individual, but by teams. And that’s the case in building a data governance framework. In assembling a well-rounded data governance team, look for a mix of subject matter experts, data security experts, IT personnel, project managers, and the like.
But the team doesn’t have to entirely stocked with people with an IT bent: Round out your data governance team with business line professionals who can offer frontline and cross-functional experience to the team.
Creating your data governance framework
Once you’ve answered the questions above and settled on your team, you’ll have a solid foundation on which to build a data governance framework. But in building it, you can’t proceed randomly; there’s a logical progression of steps you need to follow.
Determine your data governance strategy
Create a data strategy by bringing together existing processes, people, and workflows.
Start small
Start with just one business area or data issue and expand from there. Hint: It’s good to start with one where significant problems or imminent risks make it a good testing ground for data governance.
Pick the right framework
Choose a data governance framework that best aligns with your data strategy.
Communicate
Establish a communications strategy to inform people across the organization about data governance.
Keep it updated
Continue to make refinements to your framework as your business needs change.
Data governance best practices
1. Data governance is not data management
Data management is the actions taken to facilitate the data governance software framework. Data governance is the decision making function over data management decisions.
2. Collaboratively made frameworks are the most effective
Employees within the organization who know how to best manage the data should play a critical role in the framework design as this will ensure optimal optimization of the process.
3. Data governance needs to be integrated organization wide
Once the framework is functional ensuring it is implemented across the organization will ensure consistency in data collection to help every team achieve their goals.
4. Risk milestones
Data is valuable and when it is shared within the organization risk increases. Establishing risk milestones will put the spotlight on potential risks to help avoid costly breaches.
5. Continuously refine
As your organization grows, keep revisiting the data governance strategy to ensure it is still meeting the needs of your customers and your organization.
Control your data and safeguard compliance.
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