Data is an important asset for modern business. And data is only going to become more important as companies evolve. In fact, according to 451 Research, part of S&P Global Market Intelligence, “81% [of companies surveyed] said data will be more important to their organization a year from now.”1
This increasing valuation of data is also causing many organizations to take stock of their data management solutions, causing what 451 Research calls a “renaissance in interest -and often budget.”1 And while data management doesn’t always garner the same attention as data and analytics, it does serve as a foundational element for companies looking to become more data driven.
What is Data Management?
Data management is the practice of curating and cleaning, securing and storing, and enriching and maintaining an organization’s data so the business can use it to drive better decisions. Data management solutions are made up of a number of components, among which are data prep, data pipelines, and data warehouses to data governance, data architecture, and data security.
But in addition to many of these well-known components, we believe there are a few additional things companies should add to their data management stack.
Many companies are struggling with dirty data – data that’s incomplete, incorrect, inconsistent, or out-of-date. And when you surface this data directly within your BI and analytics tools, business users can find themselves making bad decisions.
Data mastering helps solve for dirty data by employing machine learning to consolidate, clean, and categorize your internal and external data. And while data mastering is machine learning-driven, it keeps humans in the loop, providing them opportunities to provide feedback that makes the algorithms even better over time.
A data product is what helps to make your data consumable by your business users so they can make confident decisions. It’s a layer that sits between your front-end data preparation tools and your messy back-end source systems and resolves the business entities that matter to your company. This business context layer allows you to transform the data into business topic areas that matter to your decision makers.
Companies today are realizing that, while they have a lot of data internally, the best version of that data is more likely to live outside of their organization. That’s why companies invest in data enrichment.
Data enrichment is the process of enhancing existing, internal datasets with information that is generated from additional data sources. These sources could include data about organizations, people, or parts or data that could be used for sales and marketing, product analytics, risk management, and more. It’s an important part of data management, especially as data volume continues to grow.
As data grows in volume and complexity, and as companies enrich their data with third party sources, it’s inevitable that dirty data will make its way into the data pipeline. While there are many ways companies can tackle data quality, data mastering being one of them, another way is through data observability.
Data observability enables data engineers to monitor the data pipeline for dirty data and make data quality changes on-the-fly, preventing dirty data from wreaking havoc on decision making.
Why is Data Management Important?
There are many reasons why data management is important to modern business. First and foremost, data management helps drive business value. It provides the foundation for businesses to manage their data as a trustworthy asset that the business can rely on for decision making.
Second, data management done right enables your organization to scale as the volume and variety of your data grows. It helps you to ensure that your data, whether internal or external, remains of the highest quality. And it helps to increase collaboration across the business because everyone is working off the same version of the truth.
Finally, data management provides the structure and support to enable data engineers and data scientists to focus on higher-value work. Instead of spending their days fixing data problems, they can focus on improving data pipelines. Not only will data management allow them to put their skills to the right work, it will also help prevent burnout from fighting data fires.
Getting data management right is a critical step in becoming data driven. Finding the right solutions for each part of the stack is key, and we believe that taking a best-of-breed approach will help to ensure that you have the right data management solutions for your business.
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1 – Source: 451 Research, part of S&P Global Market Intelligence, Data, AI & Analytics Market Monitor: Data Management