Any organziation with a data-driven strategy should understand the definition of data governance. In fact, in light of increasingly stringent data regulations, any organzation that uses or even stores data, should understand the definition of data governance.
Organizations with a solid understanding of data governance (DG) are better equipped to keep pace with the speed of modern business.
In this post, the erwin Experts address:
- Data Governance Definition
- Why Is Data Governance Important?
- What Is Good Data Governance?
- What Are the Key Benefits of Data Governance?
- What Is the Best Data Governance Solution?
Data Governance Definition
Data governance’s definition is broad as it describes a process, rather than a predetermined method. So an understanding of the process and the best practices associated with it are key to a successful data governance strategy.
Data governance is best defined as the strategic, ongoing and collaborative processes involved in managing data’s access, availability, usability, quality and security in line with established internal policies and relevant data regulations.
It’s often said that when we work together, we can achieve things greater than the sum of our parts. Collective, societal efforts have seen mankind move metaphorical mountains and land on the literal moon.
Such feats were made possible through effective government – or governance.
The same applies to data. A single unit of data in isolation can’t do much, but the sum of an organization’s data can prove invaluable.
That’s because data’s value depends on the context in which it exists: too much unstructured or poor-quality data and meaning is lost in a fog; too little insight into data’s lineage, where it is stored, or who has access and the organization becomes an easy target for cybercriminals and/or non-compliance penalties.
So DG is quite simply, about how an organization uses its data. That includes how it creates or collects data, as well as how its data is stored and accessed. It ensures that the right data of the right quality, regardless of where it is stored or what format it is stored in, is available for use – but only by the right people and for the right purpose.
With well governed data, organizations can get more out of their data by making it easier to manage, interpret and use.
Why Is Data Governance Important?
Although governing data is not a new practice, using it as a strategic program is and so are the expectations as to who is responsible for it.
Historically, governing data has been IT’s business because it primarily involved cataloging data to support search and discovery.
But now, governing data is everyone’s business. Both the data “keepers” in IT and the data users everywhere else within the organization have a role to play.
That makes sense, too. The sheer volume and importance of data the average organization now processes are too great to be effectively governed by a siloed IT department.
Think about it. If all the data you access as an employee of your organization had to be vetted by IT first, could you get anything done?
While the exponential increase in the volume and variety of data has provided unparalleled insights for some businesses, only those with the means to deal with the velocity of data have reaped the rewards.
By velocity, we mean the speed at which data can be processed and made useful. More on “The Three Vs of Data” here.
Data giants like Amazon, Netflix and Uber have reshaped whole industries, turning smart, proactive data governance into actionable and profitable insights.
And then, of course, there’s the regulatory side of things. The European Union’s General Data Protection Regulation (GDPR) mandates organization’s govern their data.
Poor data governance doesn’t just lead to breaches – although of course it does – but compliance audits also need an effective data governance initiative in order to pass.
Since non-compliance can be costly, good data governance not only helps organizations make money, it helps them save it too. And organizations are recognizing this fact.
In the lead up to GDPR, studies found that the biggest driver for initiatives for governing data was regulatory compliance. However, since GDPR’s implementation better decision-making and analytics are their top drivers for investing in data governance.
Other areas in where well governed data plays an important role include digital transformation, data standards and uniformity, self-service and customer trust and satisfaction.
For the full list of drivers and deeper insight into the state of data governance, get the free 2020 State of DGA report here.
What Is Good Data Governance?
We’re constantly creating new data whether we’re aware of it or not. Every new sale, every new inquiry, every website interaction, every swipe on social media generates data.
This means the work of governing data is ongoing, and organizations without it can become overwhelmed quickly.
Therefore good data governance is proactive not reactive.
In addition, good data governance requires organizations to encourage a culture that stresses the importance of data with effective policies for its use.
An organization must know who should have access to what, both internally and externally, before any technical solutions can effectively compartmentalize the data.
So good data governance requires both technical solutions and policies to ensure organizations stay in control of their data.
But culture isn’t built on policies alone. An often-overlooked element of good data governance is arguably philosophical. Effectively communicating the benefits of well governed data to employees – like improving the discoverability of data – is just as important as any policy or technology.
And it shouldn’t be difficult. In fact, it should make data-oriented employees’ jobs easier, not harder.
What Are the Key Benefits of Data Governance?
Organizations with a effectively governed data enjoy:
- Better alignment with data regulations: Get a more holistic understanding of your data and any associated risks, plus improve data privacy and security through better data cataloging.
- A greater ability to respond to compliance audits: Take the pain out of preparing reports and respond more quickly to audits with better documentation of data lineage.
- Increased operational efficiency: Identify and eliminate redundancies and streamline operations.
- Increased revenue: Uncover opportunities to both reduce expenses and discover/access new revenue streams.
- More accurate analytics and improved decision-making: Be more confident in the quality of your data and the decisions you make based on it.
- Improved employee data literacy: Consistent data standards help ensure employees are more data literate, and they reduce the risk of semantic misinterpretations of data.
- Better customer satisfaction/trust and reputation management: Use data to provide a consistent, efficient and personalized customer experience, while avoiding the pitfalls and scandals of breaches and non-compliance.
For a more in-depth assessment of data governance benefits, check out The Top 6 Benefits of Data Governance.
The Best Data Governance Solution
Data has always been important to erwin; we’ve been a trusted data modeling brand for more than 30 years. But we’ve expanded our product portfolio to reflect customer needs and give them an edge, literally.
The erwin EDGE platform delivers an “enterprise data governance experience.” And at the heart of the erwin EDGE is the erwin Data Intelligence Suite (erwin DI).
erwin DI provides all the tools you need for the effective governance of your data. These include data catalog, data literacy and a host of built-in automation capabilities that take the pain out of data preparation.
With erwin DI, you can automatically harvest, transform and feed metadata from a wide array of data sources, operational processes, business applications and data models into a central data catalog and then make it accessible and understandable via role-based, contextual views.
With the broadest set of metadata connectors, erwin DI combines data management and DG processes to fuel an automated, real-time, high-quality data pipeline.