What is Database Management?

Database management is a series of actions and responsibilities that help meet an organization's needs during the data life cycle. This role can be the responsibility of a single database administrator (DBA) or shared across a larger data team. A DBA may be responsible for one database or data repository, or they may have a wider responsibility for data across the organization.

What are the Goals of Database Management?

Organizations have diverse data needs, although there are some common threads. The main goal of data management is to find reliable, cost-effective solutions for all of these requirements.


The DBA ensures that all systems within their purview are always at optimal performance. This can include any cloud or on-premise RDBMS systems, as well as any data repositories such as data warehouses and data lakes. Even minor performance issues can have a cumulative effect that slows down both the systems and the people who depend on the data.


Data analytics is now a core function in almost every business. For effective analytics, the organization will need their data sources integrated, cleansed, and (if required for compliance) obfuscated. A DBA will help the organization leverage the value of its existing data while ensuring that future data projects are as analytics-friendly as possible.


The organization needs the right data to be available at the right time. Database management involves ensuring maximum uptime by responding quickly to issues. It also means scheduling data updates to happen at the right time, so that users and processes have the most recent version when they are working.


Every person and process within the organization can access data that's relevant to them, but not any other data. Access types are: read-only, read-write, or admin. Database management involves granting permissions when a party requires them and revoking those privileges when they are no longer needed.


An organization's data requirements will generally scale upwards, partly due to growth, and historical data will accumulate over time. Database management involves thinking about these issues ahead of time and implementing solutions that anticipate the organization's future needs.  

Backup and Recovery

Every organization has to plan for data loss. DBAs will schedule backups to reflect the data's volatility while also ensuring that the backup process doesn't impact the available resources. The DBA also needs to plan for fast disaster recovery, which may involve getting production systems back online using a saved backup.


DBAs work with both production databases and long-term storage repositories. These can be in the form of relational databases in data warehouses or unstructured data in a data lake. The DBA will help make decisions about appropriate repository structures, building data pipelines with ETL, and maintaining the integrity of production systems.


Database security mostly focuses on the data itself, which means working to secure the data infrastructure, including the network. DBAs may also assist with physical security and employee protocols to reduce social engineering threats. They may also participate in decisions about which applications can access particular databases, or what data to expose via API.


Databases can raise compliance issues, especially if they contain personal information. DBAs will understand all relevant compliance issues and work to keep the organization within all applicable rules.

What are the Pillars of Database Management?

Database management is one of the practical applications of data governance. While data governance is ultimately about high-level policy, database management involves putting those policies into action in a live environment.

DBAs will work with the data governance team to make decisions that align with the organization's policy. This means that database management touches on many of the pillars of data governance. As per the DAMA-DMBOK functional framework, these pillars include:

  • Data architecture: Designing an appropriate structure for data solutions, including RDBMS instances and data repositories. A data architect will often perform this task. However, the DBA will need to understand the underlying architecture of their systems.
  • Data modeling: Performing analysis of potential use cases to try and anticipate the data needs of the future, resulting in a data structure that is suitable for all instances.
  • Data storage: Building physical structures for storing data. This can include on-premise production systems and cloud-based repositories.
  • Data security: Applying adequate safety measures for each data structure. DBAs will also make sure that all measures comply with the organization's overarching security policy.
  • Data integration: Managing data integration processes so that data quality is never compromised. Extract, Transform, Load is one such process, and DBAs may play a role in implementing automated data pipelines.
  • Documents and content: Managing unstructured data such as files, documents, images, and audio recordings, especially when stored in a repository such as a data lake. Indexing these files so that the users and processes can access them when they need them.
  • Master data management: Improving data quality by maintaining master data records, which describe key business entities such as customers, products, and locations. Also, working to protect the integrity of this crucial data.
  • Analytics and business intelligence: Co-ordinating with analytics teams to provide suitable sources of data for further study. From a practical point of view, this often overlaps with data integration because analysts require a clean, unified data source.
  • Metadata: Managing the metadata that describes the contents of the databases. This can include file catalogs for unstructured data or the lineage metadata that tracks the history of relational databases.
  • Data quality: Performing routine quality checks to verify that all data meets the organization's standards. The QA team may perform these checks, who will then raise issues with the appropriate stakeholder.

In a small organization, the DBA may have to make plans and decisions that take all of these elements into account. In larger companies, the DBA might focus on the day-to-day running of an existing database. In all instances, the main purpose of database management is to ensure that the company sees the maximum value from its data.

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Glossary of Terms

A guide to the nomenclature of data integration technology.