Click to learn more about author Olivia Hinkle.

The amount of data in the world today is truly staggering. In fact, the World Economic Forum estimates that by 2025 463 exabytes of data will be created globally each day. Data is no longer just a byproduct of doing business—it’s the lifeblood of everything they do.

Businesses rely on high quality data to operate and succeed. A robust CRM populated with clean, accurate data allows companies to build better client relationships, deliver seamless customer experiences, and create more effective sales and marketing campaigns. It can help to uncover valuable insights and inspire the development of new products and services. But the key to all this success is ensuring that data is available to the people who need it, when they need it, in a structure they can use. That’s where Data Governance comes in.

Data Governance Defined

In simple terms, Data Governance is comprised of the people, processes, and technology an organization employs to manage its use of data. It requires establishing a standard for data that fits the individual organization’s needs and processes, as well as a plan for implementing, enforcing, and supporting that standard. While security and governance are different, a complete governance policy should include a security review. This kind of review ensures that the right people have proper access to the right stuff, which includes compliance with regulations and storage.

An efficient and effective Data Governance policy needs to cover a lot of ground. At a minimum, for each set of data, it should define:

• Where the data is stored;
• Who has access to the data;
• How the data is structured;
• How key terms and entities within the data are defined;
• What the organization expects in terms of Data Quality;
• What the organization wants to do (or be able to do) with the data; and
• What needs to happen in order for the data to meet these goals.

Clearly, answering these questions is no small task. So, what can organizations do to establish and implement an effective governance policy? Here are three important things to keep in mind:

1. Involve the Right People

At first glance, it may seem like Data Governance is a job for IT. But in truth, Data Governance goes far beyond the scope of an IT team’s capabilities. IT is great at the technical aspects of data management, but they typically aren’t close enough to the organization’s business needs and day-to-day operations to develop a comprehensive and effective strategy for putting data in the hands of the people who need it.

Instead, enterprise businesses will often create a team to develop and oversee their Data Governance program, which includes representatives from various departments across the organization. Any team that is a producer, collector, user, protector, or owner of data should be involved.

Depending on the organizational structure, teams to consider include sales, marketing, tech support, professional services, product development, legal, compliance, finance, management, and more—in addition to IT. In this way, it’s possible to ensure that the many perspectives and priorities within a large enterprise are fairly represented and create Data Governance policies and procedures that address the needs of departments company wide.

2. Don’t Get Lost in the Process

The goal of Data Management—and Data Governance in particular—should be to simplify data access and usage as much as possible by creating logical, meaningful standards and processes that are easy for data users to follow. Processes equal control, and control is absolutely critical when it comes to data.

Unfortunately, the exercise of creating those standards, processes, and policies can be quite complex. It’s important to remember that Data Governance is a tool to improve business outcomes—not a hurdle to overcome. To avoid going down a process-building rabbit hole and ensure your Data Governance efforts come to fruition, follow these important tips:

• Look for quick wins first, then build to more ambitious goals over time.
• Prioritize. First get the right people in place, then build the right processes, and once that’s done, define your technology needs.
• Set clear goals and measure your progress. If you can’t measure it, you can’t manage it.
• Clearly identify roles and responsibilities so everyone knows why they’re involved and what’s expected of them.
• Simplify processes and automate whenever possible.
• Remember, effective Data Governance is an ongoing exercise. There’s no start and end date.

Tailor Technology to Meet Your Needs

The search for appropriate technology solutions becomes far less daunting once you have a clear vision of what you want from your data and how your users are going to interact with it. Once established, your Data Governance policy acts as a roadmap for finding the right technology.

As you begin evaluating potential solutions, you may find that parts of your data policies and processes need to be tweaked. This is normal — tailor your processes and technologies to mesh with one another, while still meeting your stated goals. Your needs may also change over time, as evolution is inevitable.

It’s unlikely you’ll find one tool that meets all of your needs, which is why your technology stack could include the strategic implementation of a core CRM solution, along with third-party tools and integrations. At a high level, here are some key functionalities to look for:

1. Data Import: Get data from a spreadsheet or other source into your CRM quickly and easily, in the format you specify. Manipulate, update, export, and delete records without wasted time or effort.
2. Data Verification: Ensure clean contact data by keeping your CRM free of errors like invalid email addresses, phone numbers, and physical addresses.
3. Deduplication: Remove and prevent duplicate records based on your customized definition of “duplicate.” Standardize and modify data with flexible rule creation for both records and individual data fields.
4. Data Reporting and Analytics: Understand the quality of your CRM data through customizable dashboards and alerts that show how your data affects pipeline management, campaign performance, and customer retention.
5. Data Operations: Boost Data Management productivity and operational efficiency by integrating data into configurable views that allow you to see only the data that’s relevant to the task at hand.
6. Data Maintenance: Easily find and remove junk data with specialized filters that address common data entry issues.
7. Data Security: Set and configure your customized rules for data access and administration. (Who can see what? Who can change what?)

The Bottom Line

Just as emerging technologies like autonomous vehicles rely on accurate data for optimal performance, businesses rely on quality data and effective Data Governance to operate and succeed. Once organizations recognize this inherent value in their data, they can begin to take the steps required to maximize return on their data investments. Putting the right people, processes, and technologies in place will ensure that data assets are available where and when they’re needed.