The What & Why of Data Governance
Modern data governance is a strategic, ongoing and collaborative practice that enables organizations to discover and track their data, understand what it means within a business context, and maximize its security, quality and value.
It is the foundation for regulatory compliance and de-risking operations for competitive differentiation and growth.
However, while digital transformation and other data-driven initiatives are desired outcomes, few organizations know what data they have or where it is, and they struggle to integrate known data in various formats and numerous systems – especially if they don’t have a way to automate those processes.
But when IT-driven data management and business-oriented data governance work together in terms of both personnel, processes and technology, decisions can be made and their impacts determined based on a full inventory of reliable information.
Recently, erwin held the first in a six-part webinar series on the practice of data governance and how to proactively deal with its complexities. Led by Frank Pörschmann of iDIGMA GmbH, an IT industry veteran and data governance strategist, it examined “The What & Why of Data Governance.”
The What: Data Governance Defined
Data governance has no standard definition. However, Dataversity defines it as “the practices and processes which help to ensure the formal management of data assets within an organization.”
At erwin by Quest, we further break down this definition by viewing data governance as a strategic, continuous commitment to ensuring organizations are able to discover and track data, accurately place it within the appropriate business context(s), and maximize its security, quality and value.
Mr. Pörschmann asked webinar attendees to stop trying to explain what data governance is to executives and clients. Instead, he suggests they put data governance in real-world scenarios to answer these questions: “What is the problem you believe data governance is the answer to?” Or “How would you recognize having effective data governance in place?”
In essence, Mr. Pörschmann laid out the “enterprise data dilemma,” which stems from three important but difficult questions for an enterprise to answer: What data do we have? Where is it? And how do we get value from it?
Asking how you recognize having effective data governance in place is quite helpful in executive discussions, according to Mr. Pörschmann. And when you talk about that question at a high level, he says, you get a very “simple answer,”– which is ‘the only thing we want to have is the right data with the right quality to the right person at the right time at the right cost.’
The Why: Data Governance Drivers
Why should companies care about data governance?
erwin’s 2020 State of Data Governance and Automation report found that better decision-making is the primary driver for data governance (62 percent), with analytics secondary (51 percent), and regulatory compliance coming in third (48 percent).
In the webinar, Mr. Pörschmann called out that the drivers of data governance are the same as those for digital transformation initiatives. “This is not surprising at all,” he said. “Because data is one of the success elements of a digital agenda or digital transformation agenda. So without having data governance and data management in place, no full digital transformation will be possible.”
Data Privacy Regulations
While compliance is not the No. 1 driver for data governance, it’s still a major factor – especially since the rollout of the European Union’s General Data Protection Regulation (GDPR) in 2018.
According to Mr. Pörschmann, many decision-makers believe that if they get GDPR right, they’ll be fine and can move onto other projects. But he cautions “this [notion] is something which is not really likely to happen.”
For the EU, he warned, organizations need to prepare for the Digital Single Market, agreed on last year by the European Parliament and commission. With it comes clear definitions or rules on data access and exchange, especially across digital platforms, as well as clear regulations and also instruments to execute on data ownership. He noted, “Companies will be forced to share some specific data which is relevant for public security, i.e., reduction of carbon dioxide. So companies will be forced to classify their data and to find mechanisms to share it with such platforms.”
GDPR is also proving to be the de facto model for data privacy across the United States. The new Virginia Consumer Data Privacy Act, which was modeled on the California Consumer Privacy Act (CCPA), and the California Privacy Rights Act (CPRA), all share many of the same requirements as GDPR.
Like CCPA, the Virginia bill would give consumers the right to access their data, correct inaccuracies, and request the deletion of information. Virginia residents also would be able to opt out of data collection.
Nevada, Vermont, Maine, New York, Washington, Oklahoma and Utah also are leading the way with some type of consumer privacy regulation. Several other bills are on the legislative docket in Alabama, Arizona, Florida, Connecticut and Kentucky, all of which follow a similar format to the CCPA.
Stop Wasting Time
In addition to drivers like digital transformation and compliance, it’s really important to look at the effect of poor data on enterprise efficiency/productivity.
Respondents to McKinsey’s 2019 Global Data Transformation Survey reported that an average of 30 percent of their total enterprise time was spent on non-value-added tasks because of poor data quality and availability.
Wasted time is also an unfortunate reality for many data stewards, who spend 80 percent of their time finding, cleaning and reorganizing huge amounts of data, and only 20 percent of their time on actual data analysis.
According to erwin’s 2020 report, about 70 percent of respondents – a combination of roles from data architects to executive managers – said they spent an average of 10 or more hours per week on data-related activities.
The Benefits of erwin Data Intelligence
erwin Data Intelligence by Quest supports enterprise data governance, digital transformation and any effort that relies on data for favorable outcomes.
The software suite combines data catalog and data literacy capabilities for greater awareness of and access to available data assets, guidance on their use, and guardrails to ensure data policies and best practices are followed.
erwin Data Intelligence automatically harvests, transforms and feeds metadata from a wide array of data sources, operational processes, business applications and data models into a central catalog. Then it is accessible and understandable via role-based, contextual views so stakeholders can make strategic decisions based on accurate insights.
Webinar: The Value of Data Governance & How to Quantify It
Watch the latest webinar in this series, “The Value of Data Governance & How to Quantify It.” Mr. Pörschmann will discuss how justifying a data governance program requires building a solid business case in which you can prove its value.Watch Now