Data Governance

Data Governance: Roles, Policies, and Challenges

April 25, 2021

Topics: Elementary 8 minute read Cloud Data Sense

What is Data Governance?

Data governance is a field that includes organizational policies and processes that control how data is managed in the organization. The goal of data governance is to ensure that data is managed, secure, accurate, audited, and documented (in terms of where and what data the organization holds).

Data governance means that companies take measures to ensure they have high-quality data at every stage of its lifecycle.

Effective data management strategies and processes can improve business results and drive growth. Most organizations are collecting large amounts of data, both internally produced and acquired from third parties. Effective data governance is essential for effective use of the data, risk management and cost reduction.

In this article, you will learn:


Data Management vs Data Governance

Data governance is part and parcel of data management at an organization.

Data governance involves establishing and monitoring policies and procedures, related to how data is accessed and processed. The purpose is to ensure data is of high quality, and is adequately protected, throughout its lifecycle.

The data governance process manages data integrity, security and ease of use, in line with internal data standards and policies. The governance team and data stewards encourage improvement, and ensure data policies are enforced.

Data management is responsible for implementing the architecture and processes to achieve the goals of data governance.

Who is Responsible For Data Governance?

Here are several roles that are often involved in the data governance process:

  • Chief data officer (CDO)—a senior executive responsible for overseeing the data governance program. CDO responsibilities typically include securing approval and funding for the program, hiring key staff members, as well as leading the program.
  • Data governance manager—a program manager leads the data governance team. This may or may not be the CDO. If there is no CDO, for example, organizations might decide to hire a manager to serve as the lead.
  • Data governance committee—composed of business executives and relevant parties. This committee is responsible for making policies and standards, which are then enforced by the data governance manager and team. The committee is also responsible for resolving disputes and amending policies as needed.
  • Data stewards—typically team members specialized in certain domains and data assets. A data steward can have knowledge of IT, business, or both. Responsibilities may include overseeing data sets, ensuring that policies are properly implemented and compliance is achieved. 

There are other roles that might be involved in the process, including data modelers, data quality analysts, data engineers, and data architects. To ensure compliance across all departments, other business roles and departments should receive proper training.

What is a Data Governance Policy?

A data governance strategy is a set of guidelines, which ensure that data assets are consistently managed and correctly used. These guidelines typically include individual policies regarding data quality, access, security, privacy, as well as roles and responsibilities for implementing these policies and monitoring compliance.

A data governance strategy should clarify the principles, practices, and standards that the organization deems necessary to have high quality data and protect data assets. This process is the responsibility of a group called the Data Governance Committee, which consists primarily of executives and data owners.

Policy documents developed by this team should clearly define the data governance structure that executives, managers and production line workers must follow in their day-to-day routines.

Here are some important basic data policies that any organization should establish.

  • Data governance structure policy—defining the overarching structure of data management at the organization, the Data Governance Council and other roles and responsibilities
  • Data access policy—defining which employees or third parties should have access to which types of information. The policy should be broadly applied to all business units and all types of data.
  • Data usage policy—ensuring data is not put to unethical or otherwise undesirable use. This involves setting restrictions for data use that will avoid legal concerns, compliance violations, or privacy issues.
  • Data integrity policy—ensuring data can be relied on. Includes measures to protect validity, accuracy, and reliability of data so it can be used confidently in decision making.
  • Data classification policy—defining how data is classified into levels of sensitivity, for example public data, internal data, classified data and restricted data.

Learn more in our detailed guide to data governance policies (coming soon)

Data Governance Challenges

Data governance is a complex process at any organization. Here are some of the key challenges facing Data Governance Councils and data stewards.

Legacy Data Systems

Many organizations have old data systems, which are inflexible and difficult to manage, and hinder the free flow of data throughout the enterprise. This makes it difficult to share, organize, and update information.

Data that is isolated in separate silos, stale, or poorly organized, can make it difficult to establish data governance activities such as tracking data records, categorizing data, and applying detailed security models.

Related content: read our guide to data migration strategy 

Data Visibility

Data governance requires businesses to achieve data transparency. It must be clear which types of data exist in the organization, where it is stored, who can access it, and how it is used. However, legacy systems often obscure the answers to these questions. Data management processes must be implemented to establish strategies and methods for accessing, consolidating, storing, transmitting and preparing data for analysis.

Related content: read our guide to data discovery (coming soon)

Unsecure Data

As the quantity and variety of internal and external data sources grows, so does the likelihood of data breaches. Like data management, data security depends on traceability. IT teams need to be able to track the source, location and users of the data, how it is used, when it is no longer useful and processes used to delete it.

Data governance establishes rules and procedures to prevent potential leakage of sensitive business and customer data, and prevent data abuse. However, traditional data platforms create isolated information silos that are difficult to visualize and trace. Without an integrated data store, invisible, untraceable data results in security risks.

Lack of Control Over Data

Many businesses are required to comply with regulations such as GDPR (General Data Protection Regulation), California Consumer Privacy Act (CCPA), Health Insurance Portability and Accountability Act (HIPAA), and industry standards like PCI DSS.

All these regulations require organizations to have a data governance structure that describes data traceability from source to retirement, provide logs recording data access and how, where and when the data is used. Governance enables businesses to control their data and prevent misuse of sensitive information. It does this in a way that can be audited and demonstrated to an external compliance body.

Data Governance with NetApp Cloud Data Sense® Cloud Data Sense is the data privacy and governance service for data stored in the cloud and on premises. Cloud Data Sense leverages cognitive computing to deliver always-on privacy and data governance controls across your hybrid data sources.

By discovering, mapping and identifying personal and sensitive information, Cloud Data Sense automates the most challenging data privacy and governance tasks introduced by modern day data regulations such as the GDPR and the CCPA, PCI and many others.

Learn more about NetApp Cloud Dats Sense

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Read more: Data Governance vs Data Management: Key Differences and How They Work Together (coming soon)

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