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Data governance (DG) is one of the fastest growing disciplines, yet when it comes to defining data governance many organizations struggle.

Dataversity says DG is “the practices and processes which help to ensure the formal management of data assets within an organization.” These practices and processes can vary, depending on an organization’s needs. Therefore, when defining data governance for your organization, it’s important to consider the factors driving its adoption.

The General Data Protection Regulation (GDPR) has contributed significantly to data governance’s escalating prominence. In fact, erwin’s 2018 State of Data Governance Report found that 60% of organizations consider regulatory compliance to be their biggest driver of data governance.

Other significant drivers include improving customer trust/satisfaction and encouraging better decision-making, but they trail behind regulatory compliance at 49% and 45% respectively. Reputation management (30%), analytics (27%) and Big Data (21%) also are factors.

But data governance’s adoption is of little benefit without understanding how DG should be applied within these contexts. This is arguably one of the issues that’s held data governance back in the past.

With no set definition, and the historical practice of isolating data governance within IT, organizations often have had different ideas of what data governance is, even between departments. With this inter-departmental disconnect, it’s not hard to imagine why data governance has historically left a lot to be desired.

However, with the mandate for DG within GDPR, organizations must work on defining data governance organization-wide to manage its successful implementation, or face GDPR’s penalties.

Defining Data Governance: Desired Outcomes

A great place to start when defining an organization-wide DG initiative is to consider the desired business outcomes. This approach ensures that all parties involved have a common goal.

Past examples of Data Governance 1.0 were mainly concerned with cataloging data to support search and discovery. The nature of this approach, coupled with the fact that DG initiatives were typically siloed within IT departments without input from the wider business, meant the practice often struggled to add value.

Without input from the wider business, the data cataloging process suffered from a lack of context. By neglecting to include the organization’s primary data citizens – those that manage and or leverage data on a day-to-day basis for analysis and insight – organizational data was often plagued by duplications, inconsistencies and poor quality.

The nature of modern data-driven business means that such data citizens are spread throughout the organization. Furthermore, many of the key data citizens (think value-adding approaches to data use such as data-driven marketing) aren’t actively involved with IT departments.

Because of this, Data Governance 1.0 initiatives fizzled out at discouraging frequencies.

This is, of course, problematic for organizations that identify regulatory compliance as a driver of data governance. Considering the nature of data-driven business – with new data being constantly captured, stored and leveraged – meeting compliance standards can’t be viewed as a one-time fix, so data governance can’t be de-prioritized and left to fizzle out.

Even those businesses that manage to maintain the level of input data governance needs on an indefinite basis, will find the Data Governance 1.0 approach wanting. In terms of regulatory compliance, the lack of context associated with data governance 1.0, and the inaccuracies it leads to mean that potentially serious data governance issues could go unfounded and result in repercussions for non-compliance.

We recommend organizations look beyond just data cataloging and compliance as desired outcomes when implementing DG. In the data-driven business landscape, data governance finds its true potential as a value-added initiative.

Organizations that identify the desired business outcome of data governance as a value-added initiative should also consider data governance 1.0’s shortcomings and any organizations that hasn’t identified value-adding as a business outcome, should ask themselves, “why?”

Many of the biggest market disruptors of the 21^st Century have been digital savvy start-ups with robust data strategies – think Airbnb, Amazon and Netflix. Without high data governance standards, such companies would not have the level of trust in their data to confidently action such digital-first strategies, making them difficult to manage.

Therefore, in the data-driven business era, organizations should consider a Data Governance 2.0 strategy, with DG becoming an organization-wide, strategic initiative that de-silos the practice from the confines of IT.

This collaborative take on data governance intrinsically involves data’s biggest beneficiaries and users in the governance process, meaning functions like data cataloging benefit from greater context, accuracy and consistency.

It also means that organizations can have greater trust in their data and be more assured of meeting the standards set for regulatory compliance. It means that organizations can better respond to customer needs through more accurate methods of profiling and analysis, improving rates of satisfaction. And it means that organizations are less likely to suffer data breaches and their associated damages.

Defining Data Governance: The Enterprise Data Governance Experience (EDGE)

The EDGE is the erwin approach to Data Governance 2.0, empowering an organization to:

Manage any data, anywhere (Any²)
Instil a culture of collaboration and organizational empowerment
Introduce an integrated ecosystem for data management that draws from one central repository and ensures data (including real-time changes) is consistent throughout the organization
Have visibility across domains by breaking down silos between business and IT and introducing a common data vocabulary
Have regulatory peace of mind through mitigation of a wide range of risks, from GDPR to cybersecurity.

To learn more about implementing data governance, click here.

As the applications for data have grown, so too have the data governance use cases. And the legacy, IT-only approach to data governance, Data Governance 1.0, has made way for the collaborative, enterprise-wide Data Governance 2.0.

In addition to increasing data applications, Data Governance 1.0’s decline is being hastened by recurrent failings in its implementation. Leaving it to IT, with no input from the wider business, ignores the desired business outcomes and the opportunities to contribute to and speed their accomplishment. Lack of input from the departments that use the data also causes data quality and completeness to suffer.

So Data Governance 1.0 was destined to fail in yielding a significant return. But changing regulatory requirements and mega-disruptors effectively leveraging data has spawned new interest in making data governance work.

The 2018 State of Data Governance Report indicates that 98% of organizations consider data governance important. Furthermore, 66% of respondents say that understanding and governing enterprise assets has become more or very important for their executives.

Below, we consider the primary data governance use cases and drivers as outlined in this report.

The Top 5 Data Governance Use Cases

1. Changing Regulatory Requirements

Changing regulations are undoubtedly the biggest driver for data governance. The European Union’s General Data Protection Regulation (GDPR) will soon take effect, and it’s the first attempt at a near-global, uniform approach to regulating the way organizations use and store data.

Data governance is mandatory under the new law, and failure to comply will leave organizations liable for huge fines – up to €20 million or 4% of the company’s global annual turnover. For context, GDPR fines could wipe off two percentage points of revenue from Google parent company, Alphabet.

Although 60% of the organizations surveyed for the State of DG Report indicate that regulatory compliance is the key driver for implementing data governance, only 6% of enterprises are prepared for GDPR with less than four months to go.

But data governance use cases go beyond just compliance.

2. Customer Satisfaction

Another primary driver for data governance is improving customer satisfaction, with 49% of our survey respondents citing it.

A Data Governance 2.0 approach is paramount to this use case and should be strong justification to secure C-level buy-in. In fact, the correlation between effective data governance and customer satisfaction is clear. A 2017 report from Aberdeen Group shows that the user-base of organizations with more effective data governance programs are far happier with:

The business’ ability to share data (66% – Data Governance Leaders vs. 21% Data Governance followers)
Data systems’ ease of use (64% vs. 24%)
Speed of information delivery (61% vs. 18%)

3. Decision-Making

Another data governance use case as indicated by the State of DG Report is improved decision-making. Forty-five percent of respondents identify it as the third key driver, and for good reason.

Data governance success manifests itself as well-defined data that is consistent throughout the business, understood across departments, and used to pull the business in the desired direction. It also improves the quality of the data.

By moving data governance out of its IT silo, the employees responsible for business outcomes are part of its governance. This collaboration makes data both more discoverable, more insightful and more contextual.

The decision-making process becomes more efficient, as the velocity at which data can be interpreted increases. The organization can also better interpret and trust the information it is using to determine course.

4. Reputation Management

In the survey behind the State of DG Report, 30% of respondents name reputation management as a driver for DG’s implementation.

We’ve seen it time and time again with high-profile data breaches inflicting the likes of Equifax, Uber and Yahoo. All were met with costly PR fallout. For example, Equifax’s breach had a price tag of $90 million, as of November 2017.

So the discrepancy between the 60% who cite regulatory compliance as a key driver and the 30% who cite reputation management as DG drivers is interesting. One could argue they are the same; both call for data governance to help prevent or at least limit damaging breaches.

The difference might come down to smaller businesses that believe they have less brand equity to maintain. They, as well as some of their larger counterparts, have taken a reactionary approach to data governance. But GDPR should now encourage more proactive data governance across the board.

In terms of data governance use cases for managing the risk of data breaches, consider that data governance, at a fundamental level, is about knowing where your data is, who’s responsible for it, and what it is supposed to be used for.

This understanding enables organizations to focus security spending on the areas of highest risk. Thus, they can take a more cost-effective but thorough approach to risk management.

5. Analytics and Big Data

Analytics and Big Data also were identified as key drivers for data governance among 27% and 20% of respondents, respectively.

The need for data governance in these cases is largely driven by the amount of data businesses are now tasked with overseeing. In terms of volume, Big Data speaks for itself. Twenty-two percent of respondents in the State of DG Report manage more than 10 petabytes of data, which lines up closely with those who identify Big Data as a key driver.

However, the amount of data the average organization without a Big Data strategy consumes, stores and processes has climbed considerably in recent years.

Research indicates that 90% of the world’s data has been created just in the last two years. Globally, we generate 2.5 quintillion bytes a day. Other studies equate data’s value to that of oil, so clearly there’s a lot of value to be found.

However, the “three Vs of data” (volume, velocity, variety) tend to be positively correlated. When one increases, so do the other two. Higher volumes of data mean higher velocities of data that must be processed faster for worthwhile, valuable insights. It also means an increase in the data types – both structured and unstructured – which makes processing more difficult.

A Strong DG Foundation

A strong data governance foundation ensures data is more manageable, and therefore more valuable.

With Data Governance 2.0, data governance use cases shift from reactionary to proactive with a clear focus on business outcomes.

Although new regulations can be seen as bureaucratic and cumbersome, GDPR actually presents organizations with great opportunity – at least for those that choose to take the evolved Data Governance 2.0 path. They will benefit from an outcome-focused DG initiative that adds value beyond just regulatory compliance.

To learn more, download the complete State of Data Governance Report.