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What is data governance and why does it matter?

Unlock true value of your data with a carefully crafted data governance framework

What is data governance?

According to The Data Governance Institute, “Data Governance is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods”.1 Simply put, data governance is a collection of processes, roles, policies, standards, and metrics that ensures the effective and efficient use of information, empowering an organization to achieve its goals.

We are living in an age of data where around “5 quintillion bytes of data is produced every day”.2 As organizations are transforming digitally, IT leaders are recognizing data as their most critical asset and adopting data-driven strategies to fuel digital transformation. In other words, organizations are using data for informed decision making to deliver better business outcomes, boost revenue and gain a competitive advantage.

However, boosting revenues and getting an advantage takes time, data quality often requires improvement, and many companies struggle to make the transformation into truly data driven enterprises. Data duplication, inaccuracies, inconsistency, incompleteness, and obsolescence can all affect data quality, with “poor data quality costing the U.S. economy a whopping $3.1 trillion every year”.3 In addition to dealing with poor quality data, many companies lack the optimal data infrastructure, such as a data governance framework that provides a fundamental imperative for the modern data-driven business, and they struggle with using data silos.

How is data governance different from data management?

It’s not uncommon for the terms “data governance” and “data management” to be used interchangeably. Although both are an integral part of a data-driven organization, they are separate entities.

Techopedia defines data management as “an organization's management of information and data for secure and structured access and storage”. Data management involves the collecting, organizing, protecting, and storing of data to analyze for business decision making. Data governance helps address the policies, procedures and standards around that data to help produce superior quality, security and compliance across the organization.

What is a data governance framework?

In the “Definitive Guide to Data Governance”, the author argues that “data governance is not optional”5 and that “an effective data governance strategy provides so many crucial benefits to your organization that it’s hard to live without one”.Data governance offers plenty of other measurable benefits to your enterprise in addition to helping it understand the value of its data, manage data risks and streamline data management processes and costs.

The author expands that data governance offers the following benefits:

  • A common understanding of data
  • Improved data quality
  • A data map
  • A 360 degree view of customers and business entities
  • Consistent compliance
  • Improved data management
  • Improved accessibility5
A common understanding of data and improved data quality

According to the “Definitive Guide…” author, “data governance offers a consistent view of, and common terminology for, data, while individual business units retain appropriate flexibility”.5 It also “creates a plan that ensures data accuracy, completeness, and consistency”.5 This improved data quality helps enterprises to understand, trust and access data that matters, reducing misuse of data and enabling them to make better and accurate decisions with confidence.

A data map and a 360 degree view

The “Definitive Guide…” author notes data governance helps you to “understand the location of all data related to critical entities”. This capability is required for data integration, and functions similar to “a GPS [by representing] a physical landscape and [helping] people find their way”,5 improving accessibility and business outcomes.

By establishing a data governance framework, an enterprise is essentially given “’a single version of the truth’ for critical business entities [and] can then create an appropriate level of consistency across entities and business activities”.5

Consistent compliance and improved data management

Compliance is a tricky, thorny subject that companies worldwide need to deal with. Data protection laws are constantly changing and evolving with their corresponding technology. In addition to risking data loss or damage from things like cyber attacks, adhering to regulatory frameworks and data protection standards helps to minimize loss and damage, along with any damage to an enterprise’s finances and reputation.

The data driven world is often highly automated, and data governance instills “a human dimension”5 into it, creating “codes of conduct and best practices [and helping] the concerns and needs beyond traditional data and technology areas [get] addressed consistently”.5 With robust data governance, companies can adhere to such demands of government regulations and protect their mission-critical data.

Improved accessibility

The “Definitive Guide…” author states that “a data governance framework ensures data is trusted, well-documented, and easy to find [and] that it is kept secure, compliant, and confidential”.5

What are the roles for best maintaining data governance?

There are a variety of roles that you’ll benefit from having on your data management team. Although each team can be customized to best fit the needs of their enterprise, “your team should include specific skills and expertise to understand both compliance regulations and data management [for covering] the full data spectrum [and implementing your] data strategy”.5  As technology evolves and compliance regulations changes with it, these roles may also change.

At time of writing, these roles help with supporting a collaborative data management framework:

  • Chief data officer
  • Data protection officer
  • Data architects
  • Data stewards
  • Data engineers and developers
  • Data scientists
  • Business analysts
  • Business users/data curators/data custodians5
Chief Data Officer (CDO)

The maestros of the data strategy, CDOs are in charge of “defining, deploying, and tracking the data strategy with the help of the data governance team”.5  CDOs evangelize the merits of data, hoping to convince executives that “data is considered a valuable business asset at the executive level, [encouraging the] executive committee [to invest in] data compliance, [helping] to minimize risks, and extract value out of data flows to maximize revenue”.5

Data protection officers (DPO)

The veritable data enforcement agents, DPOs help an enterprise maintain their “data compliance standards as defined by the relevant authorities [including regulations such as GDPR or CCPA]”5 and oversee how data is processed, ensuring that it’s protected.

Data architects

Data architects are responsible for ensuring the integrity and longevity of your enterprise’s “data house” and for “annotating, enriching, [and] certifying data”.5 Data architects lay “the foundations of the data as an asset to make it meaningful and business-driven for the whole company”.5

Data Stewards

As the quality assurance agents of data integrity, data stewards ensure that “their datasets meet data quality standards [per] the data governance team [and often] work with information stewards who are their partners in deploying data integrity in their specific geo/division/department”.5

Data engineers and developers

Data engineers and developers frequently leverage technical data environments for processing data flows and are in charge of “designing, deploying, and maintaining the architecture to process complex flows within the organization”.5  Similar to data stewards, data engineers and developers will help “ensure that the content has gone through quality checks”.5 Today many data engineers strive “to give autonomy to the whole data community [and maintain] control of access, authorization, and product administrations”.5

Data scientists

Data scientists tend to live up to their title. Able to extract value from data pipelines and produce valuable insights, data scientists help “solve complicated data problems using mathematics, statistics, and computer science [and] statistics, data mining, and predictive analytics [and] programming”.5

Business analyst

While data scientists produce valuable insights from data and help with complicated data problems, business analysts look at “trends and opportunities, identify and calculate risks”5 and make predictions based off this information. Like data scientists, business analysts extract value from data pipelines. What separates business analysts from data scientists is that the business analysts take the trusted insights and then “present them in [and easily] digested way through comprehensive dashboards”.5

Business users/data curators/data custodians

The talend author notes that “business users/data curators/data custodians are generalists who come from every department. Their skills and capability level may vary [but] they are also eager to get value from data with modern and simple self-service tools”.5

Data governance and your enterprise

Data governance is more than just data protection and control. It is the strategy that allows you to make the most out of your enterprise’s data and drive business results. Elements such as creating a culture that values the importance of data governance and providing realistic and outcome-oriented use cases, engaging the right people to take governance initiatives and upskilling them with proper tools and technologies all help your enterprise to embrace modern data governance policies and processes and make the most of them.

Resources
  1. Definitions of Data Governance, The Data Governance Institute, 2022.
  2. Data Governance Market - Growth, Trends, COVID-19 Impact, and Forecasts (2022 - 2027), Mordor Intelligence, 2022.
  3. Bad Data Costs the U.S. $3 Trillion Per Year, Thomas C. Redman, Harvard Business Review, 22 September 2016.
  4. Data management, Techopedia, 17 April 2017.
  5. Definitive Guide to Data Governance, talend, [NA1]
  6. Data ownership, Techopedia, 2022.