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A Guide to Building a Successful Data Governance Team

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Who needs a data governance team in 2021? Pretty much everybody, considering how data, usability, and accountability follow us everywhere we go, we all need some level of clarity and assurance.

Guide to Building a Successful Data Governance Team

However, while at home, your data governance team might be just you looking over your shoulder; things may be more complex for an entire organization. More importantly, what is data governance, and how best can you build an efficient data governance team to run a successful program.

Data Governance Defined

Data governance refers to the collection of best practices needed for data owners or an executive sponsor in the acquisition, utilization, and overall management of an organization’s data.

A data governance team puts together the right people responsible and capable enough to be entrusted with all of a company’s data assets and data management efforts.

Most often than not, this team comprises a data steward who carries the oversight responsibility, a data architect who designs and manages the system, and some other data governance professionals who ought to ensure that a company’s data governance activities churn maximum value.

This guide, albeit non-exhaustive, can be of good essence in building a functional data governance team.

Building a Successful Data Governance Team

1. Carefully Assess Your Data Needs.

There might not be one way of building a data governance team but beginning with these questions may be a bold first step and a substantial foundation: which data are we looking to govern? How and why is my organization looking to govern these data sets?

Answers to these questions can be crucial in determining the people, processes, and technology required for your organization’s data governance efforts.

2. Map Out a Clear Structure.

Typically, many companies are quick to pull out an organizational hierarchy chart anytime the topic of structure comes up. Mapping out a data governance structure goes beyond portfolios and positions.

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Instead, it encompasses efficient role casting and how best a company’s data governance tool, data strategy, databases, metadata, and general information technology can best realize data governance goals.

3. Hire the Right People and Strategize

With your data needs in mind and an efficient structure that cuts across people, processes, and technology for a company’s data governance efforts; a company may now constitute a team.

At this stage, structure and data needs can be used as indicators in hiring, acquiring data management technology, and creating the organization’s data governance strategy.

A data governance strategy most often may determine the direction an organization seeks, which will become the blueprint on which its data governance roadmap would be built, considering specific activities, budget, targets, and timelines.

4. Execute Tasks and Evaluate Results.

With a team and an approved strategy in place, the new data governance team may very well be on its way to success. The approved strategy may likely include:

  • The organization’s data governance policy.
  • Governance initiatives.
  • Other core elements of the data governance framework.
  • Best practices for data protection and overall data quality.

However, for corporations, a successful data governance team does not end with constituting a data governance office.

There ought to be an incremental approach shared by the team and all the various departments and business functions to ensure that data governance translates to a satisfactory business outcome for a corporate body.

It goes beyond all the data governance deliverables and technicalities. It must involve every team member and other stakeholders to uphold a culture tied to accepted data standards and regulatory requirements.

Otherwise, poor data management may continue to plague the data integrity at not just the general user level but also the enterprise level.

An effective team is one that knows to place data users at the core center of all processes and general data governance efforts.

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