How data governance works: Introduction

Suppliers as diverse as Google Cloud and Oracle all hope to sell you data governance, but it turns out data governance isn’t something you can buy. Sure, there are products that are meant to help businesses audit and secure sensitive data stored in databases and elsewhere, but data governance is more about people and processes than work. technology and tools.

Many businesses strive to achieve success in data governance, that is one of the reasons Gartner’s Survey found that 90% of data governance projects fail. For organizations that find the human side of data governance, significant benefits await.

Data governance sets clear standards for data handling while improving the quality and consistency of data within your company. This guide will help you understand what makes good data governance.

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What is data governance?

Data governance determines how an organization manages its data throughout the data’s lifecycle, from acquisition to disposal, as well as the various uses in between. While data governance involves tool usage, it’s much more than that: It also involves processes that everyone must follow to ensure security, availability, and integrity. of data.

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This human-centered approach is also an indication of what data governance is all about. Traditionally, data governance has been a highly centralized function focused on controlling data rather than enabling innovation. No more.

Due to “different levels of uncertainty in today’s world”, Saul Judah, Vice President of Analytics at Gartner, argues, Data management needs to grasp “Speed ​​and agility,” has rendered “traditional approaches to data governance… obsolete.”

Thus, modern data governance tends to be driven by the principles of linking data to a business case and flexibly responding to business needs.

Why is data governance important?

While data governance has traditionally been a way to protect and secure a company’s data assets, thus contributing to compliance goals, it is arguably the least compelling purpose for governance. data today. It’s not that security isn’t important; instead, it’s a question of how to increase the value of data, especially as companies increasingly rely on data to drive machine learning and other new initiatives.

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The higher an organization’s confidence in the availability and consistency of its data, the more it can put its data to use in innovation.

Why is data governance important for all businesses?

The benefits of improved data governance are not exclusive to Silicon Valley upstarts using data for legacy incumbents. With the increasing importance of data to every business, the benefits of strong data governance extend to all companies looking to stay competitive in their respective industries.

While not every company has strict data privacy needs, nearly all organizations benefit from improved data quality, lower data management costs, and more predictable data access across the organization.

Who is responsible for data governance?

For organizations that make effective use of data management, there is no single person responsible for data management policies and procedures. Instead, the responsibility falls mainly on a few key roles:

Steering committee or data governance program group

This committee typically includes senior management, who sets the overall goals and guides the organization’s data governance strategy.

Data owner

As the name implies, these individuals are responsible for data in a given domain to ensure responsible use across lines of business. This functionality will often reside within IT and manage the infrastructure needed to protect the data in their possession.

Data manager

Data curators are subject matter experts who are responsible for regular data management. These managers are on the front lines to preserve data quality. They report to data owners on ongoing data usage, especially on large projects and corporate initiatives.

All employees

There are different permutations for each of these roles, but it’s important to point out that data governance tends to fail when it’s seen as “someone else’s job.” The most successful organizations make data governance an integral part of the role of all employees.

Data governance models

Traditionally, businesses have favored top-down data governance models. Unfortunately, these models have largely failed. Gartner found thatBy 2025, 80% of organizations will fail to scale their digital business as they continue to use outdated data governance methods.

In addition to the top-down approach, there is also a bottom-up approach to data governance that businesses are increasingly adopting. Data Governance Institute explained a bottom-up approach to data governance is “central” driven and “participating” by experts in the organization, where different business units or groups come together in a congress. data co-management to define how to collaborate in data governance.

Choosing a data governance model for your business

It can be tempting to choose a model and impose it on an organization, but the reality is that most organizations need an adaptive, integrated approach that fits their culture as well as the people and existing process. Choosing a data governance model involves balancing four key steps, by Laurence Goasduff of Gartner:

1. Define a clear set of adaptive data governance principles: This involves deciding what principles can and should fit into the existing culture of the organization.
2. Establish accountability decisions across organizational areas: It is important to create clear responsibilities for each team and the roles and how they play a key role in the larger data governance goals of the organization.
3. Adopt an adaptive management style that suits your business scenario: Tailoring the governance model to your business ensures the right tools, procedures, and monitoring expectations are in place for business goals.
4. Maintain adaptive governance by building on your governance model: At the operational level, data governance focuses on applying change management principles and tactics to all data-driven work so that operations match data governance expectations .

By making data governance relevant to an organization instead of trying to force it to conform to data governance, businesses maximize their chances of success and minimize the chances that individuals will be successful. The individual or business unit chooses to escape the burdens imposed by an overly authoritarian approach.

Disclosure: I work for MongoDB but the views expressed here are mine.

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