The bigger the business, the more data it has. UPS, for example, stores more than 16 petabytes of data as it tracks packages and customer requests. But any company of any size can improve its data.

That’s where data governance — the process of managing information and creating policies that ensure its accuracy — comes in. Enforcing data rules leads to better productivity, accurate assessments, and cost-effectiveness. Implementing proper policies ensures that an enterprise has clean data that everyone can understand, which wards off errors and confusion.

Then, companies can reach what Forrester calls “Data Governance 2.0,” which opens the potential for predictive analytics and behavioral marketing.

Triggers for Data Governance

In today’s enterprises, loftier Data Governance 2.0 and digital transformation efforts are top of mind for executives. However, these initiatives can’t even begin until data governance is solidly in place. The triggers that drive the need for data governance vary, and they include:

Lack of Context: A business may do a great job capturing and documenting rules, standards, and business processes, but if that’s done from only a technical perspective, it decreases the work’s meaning. To make impactful decisions, a businessperson must be able to easily access and understand the data in a self-service environment. He needs to know the business context of why the rule or process exists within his enterprise.

Tribal Knowledge: Sometimes, one person or a group keeps all the standards and rules for the company in his own personal database. Sometimes this is a complicated Excel spreadsheet only he can understand, and other times it’s kept completely within his own brain. If that person leaves for vacation for two weeks and doesn’t have access to his email or he moves on from the position, how will the company continue to ensure data trustworthiness? This could cause huge downstream effects including added expenses, significant delays, and bad business decisions.

Entry Confusion: Details as small as formatting can lead to costly mistakes. For example, one company didn’t have a protocol and documentation in place for entering suppliers’ names, one of which was IBM. Different data stewards entered “International Business Machines,” “IBM,” and “I.B.M.” as separate suppliers, even though they denoted the same business. The confusion could have cost millions if accounts payable had accidentally paid balances to all three versions of the same supplier.

Once a company has addressed those basic concerns, it can have higher aspirations for its data. Use these data governance best practices to do bigger and better things with yours.

1. Assign Accountability.

Responsibility is the most important aspect of data governance, and it can’t be achieved with a tool; it is a human problem. Users need to be held accountable for the information they manage and consume. When accountability isn’t assigned, there’s no guarantee that anyone will take responsibility for accuracy. It’s difficult to fix data entry processes if no one is willing to take ownership.

2. Establish Traceability.

Businesses should get into the habit of monitoring changes to their data. Whether data is added, altered, or removed, a governing tool needs to keep a record, and that record needs to be tracked and maintained for future reference.

3. Don’t Forget the Business Context.

Data stewards need to spend time working together with business and IT to create and maintain policies that are easily understood and accessible enterprise-wide. When the business resources can leverage data for better decisions, the whole company wins.

When a company doesn’t govern its data well, it can’t trust its information, and it can face difficulty in driving innovation and predicting the future without that trust. But governing your data well can set you apart from competitors and make your data an asset.