Data can drive incredible value for modern businesses. Not only does it facilitate accurate and improved decision-making, but it can also enhance employee productivity, increase sales and customer satisfaction, and ultimately, protect the bottom line. The benefits are seemingly endless.
So much so that in a recent study by NewVantage Partners, 87.8% of CDOs (Chief Data Officers) and CDAOs (Chief Data and Analytics Officers) reported a rise in data investments in 2022, and 93.9% of organizations are planning to further increase their data investments throughout 2023.
Of course, this is great news, however, data must be optimized to generate real business value.
To optimize data, organizations must adapt and alter their data strategy to increase efficiency. This is achieved by speeding up the process of extracting, analyzing, and storing data. It’s throughout this improvement process that terms such as data management and data governance come into play.
While data management and data governance are cut from the same cloth, they have some distinctive differences. Many people often confuse these terms and many see them as interchangeable, but this simply isn’t true.
In this article, we’ll highlight the key differences and similarities between these two data terms as we see them.*
*Our viewpoint is based on experience and insights from DAMA BOK.
Data management encompasses all of the processes and practices that ensure a company’s data is properly stored and managed. This means everything from data security and backup to where and how data is stored is considered part of the data management process. These processes and protocols are then applied to the entire data lifecycle, i.e. it is applied to every stage data goes through, from creation, storage, and usage, to archival and destruction. This allows businesses to better monitor, track and manage data, regardless of where it sits within its lifecycle and where that data is stored.
The data management processes and protocols that a business puts in place will depend on a variety of factors, such as the business’s goals, the department or team member that holds that data – which is referred to as the data owner – and the purpose of that data. However, the data management process typically involves conducting the following tasks:
Data preparation: raw data is cleaned to analyze its accuracy, and it’s also where data quality comes into the mix. Data quality is a crucial aspect of data management as without accurate, up-to-date, and timely data, businesses run the risk of making ill-informed business decisions based on bad data, which can lead to significant financial losses
Data pipeline: the set of tools and processes used to automatically extract data from various sources into a target repository – this can be an on-premise system or a cloud-based data warehouse or storage solution – which reduces the risk of different data versions being used and shared across internal systems or, siloed data sources
Data catalog: think of this like an inventory for all company data, which is structured to support quick and easy accessibility and identification of data
Data warehouse: this is the central repository for all data that’s mentioned in the data pipeline section. The warehouse is fed via internal systems, and once it reaches this destination, it’s ready to be analyzed to inform business decision-making
Data architecture: this concerns how data is managed throughout its lifecycle, i.e. from initial collection through to transformation and ultimately, destruction or archiving. Data architecture sets the framework for data and how it flows through data storage systems
Data is perhaps a business’s most valuable resource, so managing it efficiently and effectively is a must for those that want to optimize data and better inform business decision-making.
What is Data Governance?
As mentioned before, data governance is a discipline that sits within data management. It is the set of practices, principles, and standards that an organization’s data adheres to in order to support effective data management.
Data governance has two primary functions:
Creates balance between data across various business functions, departments, and systems
Enforces data governance policies and procedures to ensure that data is utilized properly
Similar to data management, these two functions are applied to every stage of the data lifecycle, and as a result, support:
Data quality: ensures that data is accurate, complete, reliable, relevant, and timely
Data security: defines and labels data based on its risk level, and helps to maintain secure access between user interaction and security
Data stewardship: supports data fitness, i.e. is responsible for the quality and fitness for purpose of the organization’s data assets, including metadata
Data transparency: easily access and work with data in a secure manner, no matter where it or the user is located
So, why is governance important?
Data governance provides businesses with the tools necessary to establish organizational structures, which come in a variety of forms…
Roles and Responsibilities: establish clear roles and responsibilities for managing data within an organization. This includes defining who is responsible for data quality, data security, data access, and data privacy. By clearly defining these roles, it helps ensure that everyone understands their responsibilities and who ‘owns’ what data, minimizing confusion and avoiding effort duplication
Data Policies and Standards: implement data policies and standards that outline the rules for managing data, to help ensure that data is consistent, accurate, and secure and that it complies with relevant regulations. By doing so, data governance helps to create a structured approach to data management, which in turn helps to ensure data is used effectively and efficiently
Data Management Processes: develop data management processes that outline how data is collected, processed, stored, and shared within an organization. These processes help ensure that data is managed effectively throughout its lifecycle, from creation to disposal. Much like the data policies and standards above, data governance helps to create a structured approach to data management
Communication and Collaboration: encourage communication and collaboration between different teams and departments within an organization. This includes establishing data management committees, data stewards, and other governance structures that promote collaboration and communication between different teams. By ensuring that data is managed effectively across the organization, data quality can be improved and data-related risks reduced
Data Management vs. Data Governance
So, now that we’ve outlined what these two terms mean, it’s time to look at how they differ.
This is often the part that confuses some people. We’ve included a couple of real-world analogies below that should not only help to highlight how data management and data governance differ but also how they work together.
If data management is the engine, and data is the fuel, then data governance is the fuel filter, i.e. it is what ensures that the fuel (data) is clean.
If data governance is the blueprint of a building, then data management is the physical construction of that building.
In the world of data, this means:
If data management refers to the methods in which data is organized and stored, then data governance refers to the practices, policies, and standards that govern that data and its quality
If data management concerns how data is collected, organized, processed, maintained, and stored, then data governance concerns the practices and theories that make that a reality
If data management focuses on ensuring data quality and making data more valuable, then data governance focuses on the reliability and safety of that data
If data management is the logistical practice of ensuring data is organized properly, then data governance is the action of achieving data quality
If data management looks at the logistics of data and the technology needed to support data, then data governance is the philosophical focus on data in terms of the overall business strategy
These two practices work together to support an organization’s ability to make accurate data-driven business decisions. First, data governance helps to establish the policies, standards, and rules that surround the organization’s data, while data management then executes those policies, standards, and rules. Data management then organizes and extracts value from that data for better-informed decision-making.
The Benefits of Data Management and Data Governance
While data management and data governance both support accurate decision-making, we know that they perform different functions and therefore, provide varying organizational benefits.
Optimize Data Use with Data Management
Organizations use data management to optimize data by organizing, storing, protecting, and analyzing data to extract insights and make informed decisions – here are some ways that businesses use data management to optimize data:
Businesses collect data from various sources, including customer interactions, social media, market research, and other external and internal operations
Once collected, data is then stored in a central repository that can be accessed and analyzed by authorized users, this might be stored in a data warehouse, data lake, or a cloud-based storage system
Before analysis, data is cleaned and transformed to ensure accuracy and consistency to remove duplicates, correct errors, and standardize formats
Organizations use various data analysis techniques – including descriptive, predictive, and prescriptive analytics – to extract value from their data, to help them identify patterns, trends, and opportunities to improve their operations and decision-making processes
Data visualization tools help businesses present their findings in a clear and concise manner, enabling stakeholders to quickly understand and act on the insights derived from the data
Data must be protected from unauthorized access, theft, and other security threats so, security protocols, such as encryption, access controls, and firewalls are implemented to safeguard their data
Bring Meaning to Data with Data Governance
Once data management is set up, governance is the next logistical step.
Organizations use data governance to bring meaning to data, here are some ways businesses use data governance to improve the value of data, simplify internal data systems, ensure regulatory compliance, and resolve data issues before they can impact the business:
Improve the value of data: ensure that data is accurate, reliable, and relevant to the organization’s needs – by establishing data quality standards and monitoring data quality metrics, organizations can improve the value of their data and use it to make informed decisions
Simplify internal data systems: streamline internal data systems by eliminating duplicate data, ensuring consistent data definitions, standardizing data formats, simplifying data integration, and reducing the time and resources required to manage data
Ensure regulatory compliance: comply with regulatory requirements, such as data privacy laws by establishing policies for data handling, access, and retention, to minimize the risk of data breaches and protects against legal and financial consequences
Resolve data issues before they impact the business: identify and resolve data issues before they impact business operations by establishing data issue escalation processes and assigning ownership of data
Data governance is essential for organizations to bring meaning to their data. By establishing policies and procedures for managing data throughout its lifecycle, organizations stand to reap rewards, like those outlined above.
There we have it. The differences between data management and governance, and the varying functions that support wider, better-informed business decision-making.
The essential element to remember is that data management cannot work without data governance, and vice versa. While this is part of the problem, and why many people mistake the terms for being interchangeable, their purposes and the benefits they offer differ significantly.
While some organizations are well on their way to data management and governance success, others may need a helping hand. External support and solutions can help make sense of data and help to ensure maximum output.
Exmon provides organizations with simple and intuitive solutions for data management and data governance, to remove some of the data hassles. Reach out to learn more about Exmon’s data management and governance solutions and book a demonstration to discover how we can help you make improved business decisions.
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