Opinion: Data Governance - Definitions and Business Tips

The amount of data we create every day is astounding. Combining computer and smart device use with products in the world of the internet of things (IoT), we produce nearly three quintillion bytes of data within a 24-hour period. Much of this is fabricated by businesses across the globe.
The creation of this enterprise data is not done in a vacuum. In fact, businesses that rely on the validity of their data set policies to ensure its both consistent and trustworthy. The process used to enact this protection is called data governance.
How governance works
The best way to answer the question, "What is data governance?" is to create an awareness. Rather than describing the data transfer process you need to consider the who, what, where, when, why, and how of the data itself. Doing this helps the business extract value from the collected information to improve performance.
Developing a structure for data governance
In most cases, businesses that rely heavily on data will have some form of governance, though it might not be officially organized. To secure the gathering and delivery of information a governing council should be established. In turn, they would create a plan to execute protection procedures.

One way to do this is the establishment of a data governance framework. The framework defines several factors related to how data is recorded, delivered, and retained. For instance, it can include:
  • The number of components the framework covers.
  • How governance will be aligned between departments.
  • Who has decision rights and stewardship when it comes to policy changes.
  • What the data quality requirements will be.
  • The reporting methods used to track the data and its governance.
Presenting this information in a written form ensures a few things. First, it offers security to enterprises that receive the data. Second, it increases its value since the steps taken to strengthen is validity are available for review. Third, the business can make changes to the framework when necessary, so the data remains useful through new technology developments.
Mature data
In addition to building a governance framework, businesses need to understand the concept of a data maturity model. Here, how the data is managed goes beyond what presently takes place. It delves into concepts of what might be added to the framework later.
The maturity model can be shown as a graph. When first starting out, governance may be at the immature level as leaders are educated and strategies are aligned with various technology policies. An effective maturity model reveals additional factors to be added to the governance framework and it is further refined.
The outcome
In the end, a governance program and framework must to the following for the business' data:
  • Set the data's trustworthiness for all departments within the enterprise.
  • Meet regulatory and audit requirements through annual review of the company's procedures.
  • Establish data ownership and responsibilities among other custodians.
  • Determine how to monetize the data to increase profits without going against established guidelines.
  • Build on forward movement to ensure a constant commitment to data validity.
Creating a governance program for a business is a long-term task. However, breaking down the components to create positive outcomes will result in powerful protections and revenue building.

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