A Data Governance Strategy that Works

Data management is critical to the success of any business endeavor larger than a lemonade stand. A business must be able to supply its people with the data assets they need to carry out their day-to-day tasks successfully. Whether they’re positioned in accounting, production, logistics, or any other area of an organization, all staff members rely on one thing: effective data on which to base business decisions.

Effective is a key word here. Ensuring that you have reliably high-quality data is crucial, but it must also be available to the people who need it. But data hides in strange places, it’s formatted in many different ways, and its usefulness often isn’t immediately apparent. Like any other raw resource, data needs to be discovered, collected, and refined before it can be effective.

Data management entails:

  • Discovering the data you organization has collected
  • Ensuring its accuracy, integrity and availability
  • Storing it securely

The goals of data governance

To get real value out of your data, you have to be able to identify and find the data you need, and understand its usefulness to your organization. These are the ultimate goals of a data governance strategy: to make high-quality, relevant data accessible and useful, while ensuring it’s stored securely and available to only the right people.

In practice, good data governance makes it easy for people in the organization to store the data that they create or collect as part of their work in a secure fashion. It also supplies the appropriate people with data that is relevant, accurate and of real value.

Mining for gold: Data discovery

To deliver effective data governance, your data governance initiative must take into account the needs of everyone who creates, collects or stores data within the organization. That way, it is possible to design a functional cataloging and storage process.

Even if every department is generating good, useful data and storing it in the appropriate locations, your organization will have “undiscovered” data in unpredictable places.

Why is this? Advancements in data processes have led to somewhat hazy boundaries for enterprises everywhere. A typical business has data both on the network and in the cloud—and if they’re using distributed or edge computing, they’ll likely find more data “out there” than they knew existed. Discovering all of this data is the first major step toward a successful data governance program.

Finding all the data you have, including data you had no prior knowledge of, is somewhat like mining for gold, and the result is often just as valuable. Data discovery enables you to find, categorize and classify data, which in turn enables you to understand how valuable that data truly is—and how at risk it might be.

Security through data governance

With all that data lurking in all of those different places, it can be hard to know how much of it might be vulnerable. Getting a more complete picture of data flows and repositories is a critical first step in identifying data that might be at risk for theft or misuse. It could be stored on platforms or in file formats that make it more susceptible to security breaches, either internal or external.

Ensuring secure data entails:

  • Discovering all of the data stored by your organization
  • Classifying and labeling (tagging) the data
  • Determining its value
  • Mapping where it’s stored
  • Ensuring it’s secure and accessible only to the right people

Developing a successful data governance strategy

A successful data governance strategy has multiple components. The data governance strategy team should set both long-term and short-term goals. These goals will help drive decisions about procedures and processes to meet the organization’s immediate and future needs.

It is also important to determine how the overall success of the developing data governance strategy will be measured. Defining metrics to measure whether specific criteria have been met, and communicating these metrics and criteria to all involved parties, helps ensure that teams are all working on common goals.

Assembling a data governance team or an office of data governance is also a key step. Once goals and success metrics are defined, people must be assigned responsibility for achieving them. A complete team should include representatives from management, IT pros who are charged with stewardship of the organization’s data, department liaisons where appropriate, and any other key figures or stakeholders in the company who are involved in handling data.

The team should outline who can view and distribute different types of data. This is also a good time to define the approach to data collection and the standards for data protection, and determine which channels will be used to obtain the data. This helps ensure the reliability, consistency and accuracy of the information.

The team should also determine how and to whom each category of data will be distributed. A governance framework must take into account sensitive data that shouldn’t be shared throughout the organization. This requires a process that categorizes data as it’s obtained and ensures appropriate access controls are in place. It is important to determine which channels will be used to distribute each type of data.

Needles, haystacks, and how to tell the difference

Clearly, developing an effective data governance strategy highlights isn’t a trivial task. The discovery process alone is almost unimaginably complex using manual methods. But ensuring the data is properly sorted, analyzed and classified can be an even bigger challenge.

Therefore, implementation of automated data discovery and classification technology across enterprise data is critical. Enterprises need solutions that eliminate the extensive effort and risk of errors inherent in manual processes if they are to keep their data secure, ensure it is complete and accurate, and simplify data management. The ideal tool will be an integration of both discovery and classification of data across the entire organization. When your organization’s data is discovered, classified and tagged, you can ensure it is stored in a way that ensures its security, utility, and compliance with internal controls and regulatory mandates.