Data is the foundation for many different business objectives, but without effective data management, companies run the risk of building up their operations and portfolios with unreliable data.
Data management is a broad technology market focused on optimizing data quality, organization, and security to help companies act on data across departments.
See below to learn all about the data management market:
A Closer Look at the Data Management Market
Also read: 5 Data Management Job Market Trends
The global market for data management reached a value of $73.1 billion in 2021 and is expected to grow to $150.6 billion by 2027, according to Expert Market Research.
Companies of all sizes and industries are implementing data management software, but there’s variation when it comes to deployment. Some companies, especially those with legacy technology that’s specialized to their industry, have chosen on-premises data management solutions.
Many other companies are electing to find cloud-based software-as-a-service (SaaS) data management solutions for more flexible scaling and remote capabilities. Cloud-based solutions will continue to grow their market share as more companies move their workloads to the cloud.
Learn more about Data Management Trends
The data management market is comprised of several different segments for handling and applying data, such as these key ones:
- Data architecture: The informed design behind how organizations set up and handle their data. Architecture features can be applied to data acquisition, storage, preparation, and other segments of data management.
- Data preparation and quality management: Transform raw data into a usable format and ensure data is legible and accurate for analysis.
- Metadata and master data management: Manage the contextual data and labels of data sets — making it easier to search for and identify assets — and ensure the quality, accuracy, and uniformity of master data sets.
- Data integration and business intelligence (BI): Emphasizes data quality, uniformity, and connections between platforms, so data can be integrated to obtain insights.
- Data visualization and analytics: Focus on illustrating data relationships, locations, and needs in a visual way, which is particularly important for data democratization efforts and the analysis of data.
- Governance, risk management, and compliance (GRC): The best practices and procedures for organizations to maintain their data security and adhere to regulatory requirements, locally, globally, and in their industry.
- Data warehousing and data storage: Data sets need storage with the right capacity and standards to maintain data quality, access, and security as well as support analysis.
- Database maintenance and management: Focuses on managing the quality of database storage and the usability of the tool for data manipulation.
Learn more: Data Storage in Multicloud: Best Practices
Time and cost savings
Data can bring major value to organizations but only when it is optimized for actual business processes. Storing data that you don’t need or know how to use incurs unnecessary costs and makes it more challenging to find the data you do need. With data management efforts, data gets streamlined, so companies can use less space to store it, and users can spend less time trying to find it.
Enhanced compliance and security
Data management platforms and best practices prioritize compliance and security. Whether you’re working in an industry with stringent regulatory guidelines or not, data management automates and simplifies data security and compliance processes.
New business insights and data democratization
Data management efforts work to translate raw data and unstructured data into more accessible formats through data visualization and supporting analytics. With these efforts, data scientists can more easily share their findings with a wider business audience.
Efficient data access
Data management allows for labeling, searchability, and notifications, so users can find important data at the right time.
More reliable and accurate data sets
Regardless of where your data is sourced from, there’s a chance for errors and inaccuracies to show up. Especially as data sets grow and include new business platforms and use cases, it can be difficult to rein in data. Data management helps companies by adding automation, safeguards, and processes to identify and root out unreliable data.
See how customers from different industries use data management software to optimize their teams’ data knowledge and performance:
“One of its most relevant attributes is its data cleansing function. … The matching, merging, and audit trail functions are very useful. The review and approval workflow is widely used by contributors within the organization, and Informatica MDM provides a complete modular data management solution designed to be flexible. The power of artificial intelligence and machine learning allows us to ensure that reliable data can be located, accessed, and used where and when it is needed.” -front-end development analyst in IT services, review of Informatica Intelligent Master Data Management Platform at Gartner Peer Insights
“Oracle Autonomous Data Warehouse has evolved over the last 2 years to include features that make it a first choice for our data warehouse vision. ADW has features such as elastic scaling, fast query performance, inherent security, and low administration needs that allow our data analytics team to focus on delivering valuable insights without worrying about performance issues or infrastructure maintenance.” -SVP of data strategy and analytics in the media and publishing industry, review of Oracle Autonomous Data Warehouse at Gartner Peer Insights
Other data management use cases: Data Management Use Cases at Nasdaq, Bausch & Lomb, Dallant, Financial Fabric, and Arabesque
Through the top data management vendors listed below, users can find on-premises and cloud solutions, tools that specialize in one core area of data management, and platform- and industry-specific offerings:
Read next: Top Data Management Platforms & Software