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What is data management?

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Data management is the process of gathering, storing, and using data. It allows you to know what data you have, where it is located, who owns it, who can see it, and how it is accessed. Data management empowers organizations to securely and cost effectively deploy critical systems and applications and engage in strategic decision-making.  

A sound data management strategy determines an organization’s ability to scale and adapt to changing business processes and needs, giving teams the information and confidence to act faster and smarter.

Data management systems help organizations provide information to the right people at the right time. With the appropriate controls and implementation, data management workflows deliver the analytical insights needed to make better decisions. Data management is a necessary measure to ensure that your business-critical information is secure, accessible, and scalable. Your data management process should:

  • Generate and curate data across your infrastructure.
  • Store and scale data in the cloud and/or on-premises.
  • Establish high availability
  • Plan for disaster recovery.
  • Secure and control access to data, wherever and however possible.
  • Audit and destroy data to meet compliance requirements
  • Inspire the creation of intelligent apps through data services.

Data governance is a critical piece of any data management solution. While data management encompasses the creation, curation, and output of an organization’s data, data governance regulates the usage and security of data in accordance with an organization’s internal standards and policies, as well as any relevant external regulations.

Data management includes many architectural components that organizations need to consider as they address their enterprise data needs. These aspects of data management turn data into a strategic asset. 

  • Data storage collects and retains digital information—the bits and bytes behind applications, network protocols, documents, media, address books, user preferences, and more.
  • Data preparation gets raw data ready for analysis, fixing errors and consolidating different sources.
  • Data catalogs categorize metadata to help users easily find, understand, and use the data that’s important to them.
  • Data warehouses store data in a structured model designed for reporting. 
  • ETLs (Extract, Transform, Load) extract data from a database, transform it into a new format, and load it into a data warehouse.
  • Data pipelines automatically transfer and process incoming data from one system to another in real time.
  • Data lakes store large and varied sets of unstructured data in its native format, letting you keep an unrefined view of your data. 
  • Data architecture formally defines how data will be collected, stored, transported, and consumed.
  • Data modeling outlines how data moves through a business or application.
  • Data mesh decentralizes analytical data to make it more accessible and available across teams and locations.
  • Data grids leverage an organization’s computers collectively to complete large tasks. 
  • Data federation collects data from multiple sources and prepares them to function together.

Database management systems (DBMS) are data-keeping systems used to automate or oversee these types of data management. Relational DBMS relies on the SQL programming language to structure and connect data, while NoSQL databases are better suited for unstructured data.

Data is valuable only if it can be protected, processed, and acted upon. Leveraging your data is rewarding but complex. As data fuels enterprises in larger quantities and at faster speeds, there are challenges to prepare for. 

  • Volume. Your data is flowing in at bigger sizes and different formats, making it easy to lose sight of what you have and where it is. 
  • Data integration. As data becomes more complex, it becomes harder to consolidate data from different sources efficiently and strategically.  
  • Silos. Data that is not integrated cannot work together, resulting in untapped value and wasted resources. 
  • Storing and processing data. IT teams must determine where data should go and how it should be processed in order to have maximum impact. 
  • Costs. Data processing and storage adds cost whether you manage it on-premise or in the cloud. It’s important to evaluate these costs alongside business goals and the value of your data. 
  • Compliance. Noncompliance with industry and data privacy standards may result in fines, data security breaches, loss of certification, or other damage to your business. 
  • Data gravity. Data has the power to draw in applications and services in accordance with its mass. Large datasets and the components they attract become harder to move over time. 

Big data is data that is either too large or too complex for traditional data-processing methods to handle. Big data management organizes and administers this data to offer real-time information that you can use to improve your business.

Big data classification and analysis locates critical information quickly from a variety of sources. While it can be difficult to integrate, clean, and govern large datasets, establishing a strong architecture and tactful data strategy can help you scale efficiently, meet business goals and gather quality data analytics. Big data requires a management platform that supports integration and automation.

Data lifecycle management (DLM) is the people, tools and processes that control and govern data throughout its lifetime, from inception to deletion. This includes capturing, storing, sharing, archiving, and destroying data.

Your DLM strategy should keep information secure, accurate, and accessible as well as comply with regulatory requirements like the General Data Protection Regulation (GDPR). DLM products often automate this process, separating data into tiers based on governance policies and migrating data between tiers accordingly.

Master data is an enterprise’s core data essential for business operations. It provides a foundation for business transactions and allows an organization to compare data consistently across systems. Think customers, products, and locations –– these are some of the entities that compose master data. 

Master data management (MDM) is the process of maintaining master data. A unified MDM strategy prevents the critical data from becoming separated and siloed across systems. It also prevents errors from compounding by keeping a single source of truth. 

MDM systems should provide an overview of an enterprise’s master data across different streams as well as real-time data visualization and security features.

Data management platforms perform many key functions of data management such as locating and resolving errors, dividing resources, and optimizing systems for performance –– automating many of these functions to cut costs and increase efficiency. It’s important to keep up with data management best practices when using these platforms. 

  • Evaluate the data you have. It’s important that IT teams, data scientists, and business executives understand the data you’re generating and why it is valuable. 
  • Align your data with your business goals. Don’t keep data you don’t need. Knowing the data that will impact the business keeps your systems streamlined, simplifies maintenance, and helps you locate the data that matters.
  • Optimize your database. Make sure your database can scale and perform as you draw from different data sources. Many databases offer advanced algorithms and machine learning and artificial intelligence capabilities to help you make informed business decisions from your data. 
  • Maintain high data quality. Keep data accurate and up to date with regular quality checks, from routine updates to spelling and formatting fixes. 
  • Govern your data and make sure the right people can access it. Put teams, policies and systems in place to ensure the integrity of your data –– how it’s being used, stored and viewed. 
  • Focus on security and compliance. Train your teams and protect your systems to comply with regulations and keep your business intelligence and data safe.  

According to IDC, Red Hat Enterprise Linux is a popular choice for customers’ business critical deployments thanks to its stability, security and performance, providing consistency across all footprints in your infrastructure — on-premises, virtualized, in the cloud, and at the edge. With a centralized home for your data management solution, you can remain agile and meet your transformation and innovation goals as they evolve.

Red Hat Enterprise Linux is a performance-driven, cost-effective platform for Microsoft SQL Server that lets you quickly process large volumes of data and meet growing operational and analytical demands. It provides a scalable foundation and a consistent application experience, whether deployed across bare-metal, virtual machine, container, or hybrid cloud environments. Included analytics capabilities identify threats to security, performance, availability, and stability and provide remediation guidance to avoid issues, outages, and unplanned downtime. Red Hat Enterprise Linux is Microsoft’s reference platform for SQL Server on Linux and RHEL 8 delivers record-breaking SQL Server performance. 

Red Hat OpenShift® Data Science is a managed cloud service for data scientists and developers of intelligent applications. It provides a fully supported sandbox in which to rapidly develop, train, and test machine learning (ML) models in the public cloud before deploying in production. 

Red Hat has developed a new add-on service, Red Hat OpenShift Database Access, that makes it easier for administrators to provision and manage access to multiple 3rd party database services. OpenShift Database Access also helps developers to connect their applications to database services with a consistent and predictable experience, without bothering administrators and needing to learn the nuances of database service provider’s access requirements.

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