Data Governance allows the company to monitor the data it has, get more value from it and make important features of the data visible to users. It also provides the skills to manage these aspects. This is important not only because of errors and exclusions for existing data, but because new applications of data often need new features and thus new metadata to back them up.
According to the 2018 State of Data Governance Report, 98% of companies regard data governance as important. Moreover, 66% of the survey participants responded that understanding and governing business assets has become critical for their executives.
Below are some scenarios where data governance is applied:
Change in Regulatory Requirements
Change in regulations is undeniably one of the biggest drivers for data governance. On May 21, 2018, the European Union’s General Data Protection Regulation (GDPR) — the first attempt at a uniform approach to regulate the way enterprises use and store data — took effect.
Under the new law, Data Governance is indispensable under the new law, and failure to abide will leave companies responsible for huge penalties — up to €20 million or 4% of the firm’s global annual turnover.
Another major driver for data governance is improving user satisfaction. Organizations often have a choice when it comes to their strategy for data governance. It can be difficult and authoritative, or it can be flexible and empowering.
In a recent study, organizations determined to be “leaders” in data governance were characterized as those with powerful and clearly communicated policies for data governance, linked with the support of technology to help maintain oversight of their data. Not only are these leaders able to create a more effective data environment, but they also enjoy greater user satisfaction in several key areas, as shown in the graph below.
(Source: Aberdeen, September 2017)
Decision making is one of the key drivers of data governance. The success of data governance clearly presents itself as clear-cut data that is persistent throughout the business, accepted across departments, and used to pull the business in the dedicated direction. Ultimately, it improves the quality of the data.
By shifting data governance out of the IT silo, the individuals liable for business outcomes are part of its governance. This association makes data more detectable, contextual and insightful. The decision making process becomes more effective, as the momentum at which the data can be interpreted increases. Enterprises can also better understand and trust the data they are using to determine a system.
Reputation and Risk Management
Reputation Management is another important driver for Data Governance implementation. This is noticed time and time again with high profile data breaches exposing the likes of Uber, Yahoo and Equifax. All of them encountered costly PR fallouts. In the case of Equifax’s breach, as of November 2017, it had to pay $90 million.
One could argue that regulatory compliance and reputation management to be the same — both of them rely on data governance to help prevent or at least reduce damaging breaches. The difference, however, is generally evident in smaller businesses that believe they have less brand equity to maintain. They, as well as some of their bigger counterparts, have taken a conservative approach to data governance. But, GDPR now encourages a more proactive data governance across the board.
To manage the risk of data breaches, understand that data governance, at a central level, is about knowing where the data is, what is it supposed to be used for and who’s responsible for it. This consideration empowers organizations to target security spending on the fields of highest risk. Therefore, they can take a more cost efficient but thorough approach to risk management.
Big Data and Analytics
Big Data and Analytics are also considered to be key drivers for data governance. The need for data governance in these cases is largely compelled by the number of data companies that are now assigned with inspection.
In terms of volume, big data is self explanatory. According to the 2018 State of Data Governance Report, 22% of the respondents manage more than 10 petabytes of data. Research also indicates that 90% of the world’s data has been constructed in just the last two years.
The ‘Three Vs of Data’ (Volume, Velocity and Variety) are likely to be positively correlated, which implies that when one increases, so do the other two. Higher volumes of data indicate higher velocities of data which means that they must be processed faster for beneficial and valuable insights. However, it also means that an increase in data types (both structured and unstructured) makes processing more difficult. Hence, a solid data governance foundation ensures that data is more controllable and therefore more valuable.