What is data management ?
Data is essential to how a business operates and functions. Businesses must make sense of data and find a connection to the hype generated by the diverse systems and technologies that underpin today’s highly relevant global economies. In this regard, the data takes the center stage. Data on its own is useless – companies need an effective data management, governance, and strategy model to leverage all forms of data for practical and efficient use across supply chains, employee networks, customer and partner ecosystems…and much.
The data management process includes a wide range of tasks and procedures, such as:
- Data collection, processing, validation and storage
- Integrate different types of data from different sources, including both structured and unstructured data
- Ensure a high level of data availability and disaster recovery
- Manage how data is used and accessed by people and apps
- Protect and secure data and ensure data privacy
Why is data management important ?
Every application, analytics solution, and algorithm used in business (the rules and associated processes that allow computers to solve problems and complete tasks) depends on seamless access to data. In essence, a data management system helps ensure data security, availability, and accuracy. But the benefits of data management don’t end there.
Data management components and systems
Data management systems are built on data management platforms and include a set of components and processes that work together to help you extract value from your data. It can include database management systems, data warehouses, lakes, data integration tools, analytics, and more.
Master Data Management (MDM)
Master Data Management is a system for creating a trusted master reference (one copy of data) for all critical business data, such as product data, customer data, asset data, financing data, and more. MDM helps ensure that companies do not use multiple versions of data in different parts of the business, including operations, operations, analytics, and reporting. The three core pillars of effective comprehensive data management include: data integration, data management, and data quality management.
Big data management
New types of databases and tools have been developed to manage big data – the huge volumes of structured, unstructured and semi-structured data that are exhausting businesses today. In addition to highly efficient processing technologies and cloud-based facilities to handle volume and velocity, new approaches have been created to interpret and manage data diversity. In order for data management tools to understand and use different types of non-structured data, for example, new preprocessing processes are used to identify and classify data items to facilitate storage and retrieval.
Data integration is the practice of ingesting, transforming, compiling, and providing data, where and when it is needed. This integration happens on-premises and beyond—at the level of partners as well as third-party data sources and use cases—to meet the data consumption requirements of all applications and business processes. Technologies include batch/batch traffic, extract, transform, load (ETL), change data capture, data replication, data virtualization, stream data integration, data formatting, and more.
Business intelligence and analytics
Most, if not all, data management systems include basic data retrieval and reporting tools, many of which are integrated or bundled with powerful applications for retrieval, analysis, and reporting. Reporting and analytics apps are also available from third party developers and will almost certainly be included in the app bundle as a standard feature or as an optional add-on for more advanced functionality.
The strength of today’s data management systems lies, in large part, in the custom retrieval tools that allow users with minimal training to create their own on-screen data retrievals and print reports with surprising flexibility in formatting, calculations, arrangement, and summaries. Additionally, professionals can use these same tools or more sophisticated analytics toolkits to do more in the way of calculations, comparisons, higher mathematics, and coordination. New analytics applications are able to link traditional databases, data warehouses, and data warehouses to allow big data to be combined with business application data for better forecasting, analysis, and planning.
Source: SAP insights