Data Management | What It Is & Why It Matters
Depending on your perspective, digital transformation is either a necessary evil to stay competitive or a terrific opportunity for growth and improvement. Whether you’ve reluctantly agreed or enthusiastically welcomed it, the choice to pursue digital transformation leads inevitably to a focus on data.
Data is the centrepiece of all successful digital transformation efforts, and “data management” is often touted as a key component of your strategy. That’s true — data management is important.
The problem is that using data management as a catch-all term for everything data-related is too vague and leads to confusion and a long list of questions: What is data management? Why is it important? What’s involved in data management? Why do I need a data management strategy? Is data management the same as data governance? How does data management fit into the bigger picture?
Here at Anmut, we encounter these kinds of questions all the time — and we’re always happy to answer them. To help you understand and extract value from your data, we’ve put together this introduction to data management so you can find the answers you need.
What is data management and why is it important?
Data management deals with the technical aspects of your data such as processing, storage, organisation, and maintenance. The goal of data management is to ensure that your data is accurate, available, secure, and discoverable. In other words, data management is the process of preparing and keeping your data ready to support the needs of your business.
Think of your overall data strategy as a building. In this analogy, data management is a critical part of the foundation. It’s one of the most basic parts of the bigger data picture, but it’s also one of the most important.
All of your applications and business processes depend on data to run productively. Your employees require data to do their jobs, and your leadership relies on it to provide insight and guidance for the future of your business.
That’s why data management is so important. With a strong data management foundation, your data is always ready and available for use. You and your team can dive into the work of extracting value from your data without worrying about technical details such as how the data is stored or whether you can find what you need.
In contrast, bad data management has serious consequences such as wasted time, data loss, and decreased productivity, which lead to lost opportunities and ultimately to lower returns for your business.
Data Management Functions & Activities
Now that we’ve defined data management and established its importance for your business, let’s look at some basic data management functions and their link to the data lifecycle. Each data management activity requires distinct skills and expertise and is a necessary part of a strong data management program. Data management is closely linked with other capabilities such as data architecture, data quality management, and data governance. As you’ll notice from the descriptions below, the capabilities overlap and complement each other throughout the data lifecycle.
Data Architecture & Storage
Phase of Data Lifecycle: Collect
Designing, installing and maintaining databases, document repositories, and other data storage systems to fit your business needs is a key data management function. This work can be incredibly detailed and includes designing different types and tiers of storage based on input from business functions.
Data architecture involves developing the blueprints to meet the data needs of your business enterprise, while data management focuses on building and managing the system and databases that support your business needs.
Data Performance, Security & Maintenance
Phase of Data Lifecycle: Use, Dispose/Retain
Once the data architecture has been deployed, the focus shifts to maintenance. Performance and security are two of the most essential aspects of good data management. Teams must make sure that responses to data queries are consistently fast and accurate and that all systems stay secure. This data management function also includes activities such as installing updates and performing data backups.
Data Integration & Modeling
Phase of Data Lifecycle: Analyse
Valuable data often comes from different sources. To be useful for your business, data must be integrated and made available for different applications and complex analysis. Successful data integration relies on detailed data modelling to map data to workflows and organise the data in a way that honours the data relationships important to your business. Together, data integration and modelling comprise one of the most important data management functions.
Phase of Data Lifecycle: Collate
Raw data must be processed and prepared for analysis. Important activities include assessing data quality and data condition, making sure the data is stored in the appropriate repository, and updating metadata.
Data processing and preparation ensures that the standards defined as part of your data quality management program are applied to your dataset.
Master Data Management
Phase of Data Lifecycle: Specify, Evaluate
Master data management addresses the need for a common set of reference data to link data across different data sets and repositories. Examples include product reference numbers, customer IDs, or site identifiers.
Why you need a data management strategy
Think about the most successful companies in the world today. Perhaps names like Amazon, Salesforce, and Apple come to mind. These businesses are very different, but they have one thing in common — their success is powered by their excellence at extracting value from the data they collect.
Every company has the potential to collect and access enormous amounts of data. Across industries, the differentiator between companies who are leaders and those who lag behind is how well they use the data at their disposal.
Making the most of your data starts with good data management and good data management is impossible without a strong data management strategy.
As we mentioned earlier, the goal of data management is not simply to store data securely or to maintain backup data copies. Those things are important, but only in the larger context of making sure your data is ready to support your business needs.
To accomplish this goal, you need to first understand how your business uses data and what you’re trying to accomplish by managing it. You need to understand what types of data are most valuable and where to focus your time, money, and efforts.
Putting together a data management strategy ensures that you have a good grasp of who needs the data, why they need it, and what you need to do to deliver it effectively. Your strategy influences the data you collect, how you prepare it, the metadata you need, how you store it, your performance and security goals, the technology you adopt, and every other aspect of data management.
Without a data management strategy, it’s easy to misalign your well-intentioned efforts with the real needs of the business and end up spending time and money on data management activities that don’t add value to your organisation.
With a data management strategy, you can be confident that your efforts will support the most critical needs of your business and enable future growth and productivity.
A look at the big picture: Introducing data asset management
While it’s necessary for digital transformation success, developing a data management strategy is difficult. To start, there are often a lot of very messy legacy data systems involved. Plus, you have to get the business to cooperate with you so you can determine data value and figure out where to start.
To help solve this problem, we would like to introduce the concept of data asset management. Data asset management is an approach to data that involves taking concrete steps to treat data as a tangible corporate asset instead of an intangible business input. It’s the best way to manage your data and the approach we use with all our clients.
Data asset management allows you to view and manage data as part of the big picture for your business rather than as an IT project. Treating data as an asset allows you to determine your data’s value, set goals for your data, monitor progress towards your goals, make decisions about data management projects, and even measure the ROI of your data investments, just as you would for any other asset such as an oil field, factory, or product line.
Putting it all together: Data management vs data governance vs data asset management
The world of data is complicated. It’s easy to confuse terms and end up with a muddled picture of what you should be doing. We’ve mentioned three terms here — data management, data governance, and data asset management. To clear up any lingering confusion, we’ll quickly define each term and explain how they fit together.
Data management involves the technical aspects of storing, maintaining, securing, and delivering the data in a way that supports business processes, applications, and goals.
Data governance focuses on the rules and processes around how data is accessed, used, and handled in compliance with both outside regulations and internal best practices.
Data asset management is a value-focused approach to data based on the idea that treating data as an asset is the best way to create value for an organisation.
Data asset management enables you to see the big picture of your data’s value and understand the best way to manage its collection, storage, integration, distribution, use, and disposal. Data management allows you to build the technical foundation to support what you want to achieve, and data governance helps you develop the processes you need to make it all work.
When it comes to managing your data, you don’t have to choose from data management vs data governance vs data asset management. Instead, all three fit together as part of a complete approach. We offer an array of products and services designed to help you adopt and implement robust data asset management — including data government and data management.
Beyond Data Management — Taking the Next Steps
As a data professional or business executive, you undoubtedly understand that data is vital for both successful digital transformation and the future of your business. The next step is figuring out the strategic and monetary value of your data assets so you can plan the best way to manage and use them.
That seems like a simple goal, but achieving it is difficult and complex. Fortunately, you don’t have to do it alone. At Anmut, data is the core of our business. We exist to help companies like yours discover and unlock the value of data. Contact us to learn more about our unique approach to data and how we can help you.
See More Resources
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A Guide To Data Condition
A Guide To Data Quality
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