Data for Breakfast Around the World
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Data may be the key to success for the modern enterprise, but managing the quality of that data has never been easy. And in today’s environment, where data has become overwhelming, it’s more difficult than ever. That’s where data management and data governance come in. While they are often confused as being one and the same, they actually serve different (albeit compatible) functions.
In this article, we’ll explain the scope and purpose of each discipline and clarify how they work in tandem to improve operational efficiency, enhance data quality and improve compliance rates.
In a nutshell, data governance is the business discipline that establishes the policies, processes and standards for data access, compliance and security within an organization. It also outlines the roles and responsibilities of those who will access the data to ensure that it is used responsibly.
Governance is important because every organization has information it needs to manage and protect, whether it's confidential financial transactions, sensitive medical records, intellectual property, proprietary processes or personal information about employees and customers. Increasingly, this data falls under regulatory guidelines that specify detailed rules about how that data is managed and which levy significant penalties for failure to comply. Ensuring that this data is accurate, up-to-date and well-managed throughout its lifecycle are also essential components of a data governance strategy.
Data management involves the day-to-day tools and processes for capturing, storing, delivering and utilizing data to support the business activities of an organization.
Data management is an essential function in today’s business world. Organizations must navigate an increasingly complex data landscape, with access to more data sources and types of data than ever before. Capturing and storing all that data is only the first hurdle; it then needs to be transformed into high-quality, usable data that is accessible and secure. With the proper implementation of data management tools, organizations can:
Collect, process and validate data
Integrate structured and unstructured data
Ensure the quality of data adheres to business standards
Provide access to data
Determine data storage needs
Ensure data privacy and compliance
Manage the lifecycle of data
Ensure availability and disaster recovery
While data governance and data management are related, they have different goals and users. This table breaks down the fundamental differences between the two disciplines.
Data Governance |
Data Management |
|
What it is |
A strategic framework established by an organization to ensure that their data is accurate, secure, compliant and used responsibly. |
Day-to-day tools and processes used by an organization throughout the data lifecycle that follow the organization’s data governance guidelines. |
What it does |
Defines the policies and standards for data access, compliance and security. |
Oversees the tools and processes for capturing, storing and delivering data. |
Questions it answers |
|
|
Who owns it |
Business leaders, including data owners, data stewards, cross-functional governance committees, data custodians and reporters. |
Data team, including IT professionals and engineers. |
While data governance and data management are distinct disciplines, they actually overlap quite a bit. Understanding these similarities can help your organization align its strategies and optimize its data stewardship. Here are four key similarities:
Through the strategic direction of data governance and the operational execution of data management, both disciplines ensure data is accurate and relevant.
Both disciplines ensure data is protected and compliant by establishing policies and tools to track and classify regulated data.
Data governance and data management both assign roles and responsibilities for data owners, stewards and custodians to improve data clarity and accountability.
Although they have different functions, both disciplines have tools and policies for data handling, with data governance relying more on documentation and data management relying more on tactical tools.
To derive the most value out of data, it’s imperative that data management and data governance work in concert with one another. If you think of this collaboration like an orchestra, data management would be the musicians, playing their instruments, following scores and making sure they’re hitting the right notes. Data governance would be the conductor, providing the artistic vision, setting the tempo and unifying the orchestra. They need each other to create beautiful music — otherwise it risks becoming disjointed noise.
Data governance serves as a foundation for effective data management by providing structure, oversight and strategic direction for anyone who interacts with data. Here are some of the key ways that governance bolsters data management:
It establishes the policies that guide data quality, security and privacy.
It defines who is responsible for managing specific datasets.
It provides frameworks for compliance with regulations.
It ensures consistent metadata management across multiple systems.
It facilitates auditability and accountability in data handling.
Like any core function, data governance is a significant business investment. To evaluate the effectiveness of your data governance initiatives, you’ll want to track metrics along the following lines:
Improving data quality is one of the main goals of data governance, so it’s important to conduct regular assessments and audits to make sure your data is accurate, consistent and timely.
Data governance tools will provide regular compliance reports; these should be reviewed frequently to uncover any troublesome data breaches or regulatory violations.
To ensure proper data access and security, it’s necessary to monitor user access controls and stress-test data encryption to ensure both are working as they should.
To determine the impact your data governance initiatives are having on business outcomes, you’ll want to track metrics such as data utilization rates and user adoption of data governance tools and processes.
Conduct interviews and surveys to determine stakeholder satisfaction levels with your data governance tools and policies.
As much as possible, try to measure the time and money saved as a result of improved data thanks to the data governance initiatives. This includes quantifying the reduction of processing times and manual efforts around data and measuring savings related to increased efficiencies.
In today’s data-driven economy, businesses need both data governance and data management to stay competitive. But it’s not enough to just have both disciplines — they need to work in lockstep with each other to transform raw data into meaningful insights.
Data management oversees the operational tools and systems used to collect, process, distribute and store data. Data governance, on the other hand, is concerned with creating the strategic policies and accountability structure necessary to ensure that data is accessed and used securely, responsibly and in compliance with any relevant regulations. Essentially, data management deals with how data is handled, while data governance is concerned with who, what, when and why. Without both disciplines working together, your organization runs the risk of capturing low-quality data that leads to meaningless insights. Worse, it could leave you exposed to damaging — and potentially very costly — data breaches or compliance violations.
The collaboration between data governance and data management, or lack thereof, can make or break your business. As the world increasingly adopts new digital technologies such as artificial intelligence, the need for organizations to effectively process large volumes of data quickly and securely will only grow. Only the businesses that can transform data into meaningful insights quickly and consistently will succeed. Additionally, as we navigate a digital landscape that is rife with cyberattacks, a strong collaboration between data governance and data management can bolster data security and protect your business against data breaches.
Data governance concerns itself with establishing strategic policies and standards for how an organization should handle its data assets. This includes establishing rules to ensure security and compliance, as well as outlining a clear hierarchy of who can access the data and what they can do with it.
Data quality, on the other hand, speaks to the accuracy, reliability and consistency of the data itself. When it comes to transforming data into value for the business, quality is paramount; without high-quality data, organizations lack the reliable insights necessary for smart decision making.
Data analytics is the process of extracting information from data — including insights, patterns and trends — to optimize operations, understand customer behaviors, predict market outcomes and more.
In contrast, data governance establishes the policies and standards for how the business should handle its data assets. It provides the foundation for the success and accuracy of data analytics. For example, data governance defines data quality standards to ensure the data being analyzed is accurate, reliable and relevant. Data governance also ensures that any sensitive data used in analytics is accessed with the appropriate security measures and that it adheres to compliance guidelines.
Data management programming refers to the use of software tools and systems to collect, process, store and deliver data throughout its lifecycle. Various data management programming tools include database management systems, APIs and the use of code written in SQL, Python or the like, all of which comes together to make data more accurate, consistent and accessible and to make data governance possible.
Data governance initiatives run the risk of failure if they do not align with overall company strategy or lack reasonable and measurable goals. Most importantly, they can’t succeed without the support of a strong data management strategy.
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