Building an Effective Data Governance Program

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Who doesn’t love Faith?

Faith has been with your organization for as long as anyone can remember. She knows how to find data across many different groups, she’s been doing it forever. She’s got a directory inside her head built over decades of knowledge work that is critical to locating the right data for all kinds of projects and reporting needs.

Yet, as your organization becomes more and more data driven, Faith is no longer able to keep up with the demands. Now when you need to start a new analytics initiative or system integration, the request for data falls into a seemingly endless queue. Even worse, questions about exactly how data is sourced and secured are increasingly difficult to answer. You start to worry, what will happen when Faith retires?

And, here’s the rub. As your best employees turn over, they take critical knowledge of your data with them. New hires could take months or even years to catch up as they learn about one new system at a time.

Enter data governance which helps to manage that “tribal knowledge” into a framework where everyone has the opportunity to access it, interact with it, and use it. But building this framework is hard work, takes a lot of time, and no one rarely wants to do the extra work.

What is data governance

Most simply, data governance is the human aspect of managing data. Traditionally this means planning, monitoring, and enforcing processes against the management of data assets and other data related matters. These processes generally define authority, control, and decision-making powers. 

It can also be defined as a “system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what action with what information, under what circumstances, and using what method?”

According to the Data Governance Professional Organization, data governance is a discipline that provides policies, procedures, standards, roles, responsibilities, and accountabilities that ensure data is well-managed as an enterprise resource.

Common governance goals include improving customer experience, increasing revenue, achieving process efficiency, and regulatory compliance.

The state of data governance

The 2018 State of Data Governance report examined the data governance practices of more than 1,000 companies. Among the 118 North American respondents, only six percent of the organizations were prepared for the recent General Data Protection Regulation (GDPR).

Forty-six percent of respondents did not have a formal data governance strategy. Executive support continues to lag and IT often pays for the majority of an organization’s governance initiatives, because it’s not yet a business priority. Only four out 10 organizations have a separate budget for data governance. The survey also said organizations know about the importance of data governance, but it has yet to take hold.

Sixty eight percent of respondents say the CIO is driving the data governance process. Thirty nine percent of data architects drive data governance, 32 percent of the CEOs drive data governance, and 34 percent say the CEO is advocating for data governance.

Less than one-third of the organizations surveyed have fully implemented a data governance program. Forty two percent of the organizations said data governance is being built. Most of the organizations surveyed have completed the data discovery phase and are now developing policies and processes, business rules, data definitions and classifications.

Why create a data governance framework?

Putting the theory behind data governance into practice, a data governance framework supports the people, processes, and technology that lead to accurate, timely, and effective data within your organization. A data governance framework establishes a formal process for all stakeholders — business, IT, process owners, compliance, etc., to define a structured, agreed upon approach to managing data.

The right governance framework depends on organizational factors, including the size and scope of your existing data ecosystem; it’s not a “one size fits all.” Some organizations will start with an enterprise level data governance organization, complete with executive mappings and a full data governance council. Other data governance programs are incubated within analytics, marketing, or financial functions first before growing to an enterprise scale.

Regardless of where data governance is initiated or what technology supports it, the goals are similar.  You need to enable your organization to be able to access your data. They must know where to find it, how to understand it once they’ve found it, and trust what they' are looking at. Putting the right framework into place helps to ensure cultural buy-in from the users you are targeting, first. The framework will also help to track progress over time and enable collaboration, allowing you to bring people, processes, and technology together in pursuit of better data.

In our next blog, we will look closer at the components of a data governance framework.

Tags: healthcare, informatics, master data management, MDM

   

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