What is a Data Repository? (Definition, Examples, & Tools)
Learn about what a data repository is, the best practices for working with them, and more in Data Protection 101, our series on the fundamentals of information security.
Data is becoming more important to business decisions. That requires tools that can collect, store and help analyze data. A data repository is a tool that is common in scientific research but also useful for managing business data.
What is a Data Repository?
A data repository is also known as a data library or data archive. This is a general term to refer to a data set isolated to be mined for data reporting and analysis.
The data repository is a large database infrastructure — several databases — that collect, manage, and store data sets for data analysis, sharing and reporting.
Examples of Data Repositories
The term data repository can be used to describe several ways to collect and store data:
● A data warehouse is a large data repository that aggregates data usually from multiple sources or segments of a business, without the data being necessarily related.
● A data lake is a large data repository that stores unstructured data that is classified and tagged with metadata.
● Data marts are subsets of the data repository. These data marts are more targeted to what the data user needs and easier to use. Data marts also are more secure because they limit authorized users to isolated data sets. Those users cannot access all the data in the data repository.
● Metadata repositories store data about data and databases. The metadata explains where the data source, how it was captured, and what it represents.
● Data cubes are lists of data with three or more dimensions stored as a table — as you may find in a spreadsheet.
Benefits of Data Repositories
There is value to storing and analyzing data. Businesses can make decisions based upon more than anecdote and instinct. However, using data repositories as part of data management is another level of investment that can improve business decisions, such as:
● Isolation allows for easier and faster data reporting or analysis because the data is clustered together.
● Database administrators have easier time tracking problems because data repositories are compartmentalized
● Data is preserved and archived.
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Disadvantages of Data Repositories
There are several vulnerabilities that exist in data repositories that enterprises must manage effectively to mitigate potential data security risks, including:
● Growing data sets could slow down systems. Therefore, making sure database management systems can scale with data growth is necessary.
● A system crash could affect all the data. Backup the databases and isolate access applications so system risk is restrained.
● Unauthorized users can access all sensitive data more easily than if it was distributed across several locations.
Note: While putting all of one’s eggs (data) into one basket (data repository) sounds risky, there are mitigating factors. As difficult as it is to secure one source of data, distributing the data in several locations makes it that more difficult to secure. It’s also easier to backup a single data repository than to manage distributed backups.
These are valid risks but can be addressed when planning data repository management.
Best Practices for Working with Data Repositories
When creating and maintaining data repositories, there are many hardware and software decisions to make. Before you get there, establishing some data warehousing best practices will inform the technical decisions and keep the data repository useful:
● Enlist a high-level business champion to engage all stakeholders during the project development and during its use. This is not a developer but someone who can work across departments, engaging people who will use the data repository.
● The data repository will need to grow. Treat it as an ongoing system.
● Hire experts who can build and maintain the data repository that is needed.
● In the beginning, keep the scope of the data repository modest. Collect smaller sets of data and restrict the number of data subjects. Build upon the complexity as the data users learn the system and discover return on investment.
● Use Extract-Transformation-Load (ETL) tools to migrate data to the data repository. These tools ensure data quality in the transfer.
● Build your data warehouse first, then build the data marts.
● Decide how often the data warehouse will load new data. This often depends on the volume of data.
● Metadata is necessary for quality data analysis and reporting.
● Data users need to have access to education and support.
● The data repository will need to evolve. The types of data it collects and the uses for it will change. Therefore, having flexible plans will allow for changes in technology.
As more organizations adopt data repositories to store and manage their ever-growing volume of data, a secure approach is pertinent to an enterprise’s overall security posture. Adopting sound security practices, such as developing comprehensive access rules to allow only authorized users with a legitimate business need to access, modify, or transmit data, are crucial. Combined with a digital signature approach or multi-factor authentication, access rules go a long way in keeping sensitive data stored in a data repository secure. These and other security measures enable today’s enterprises to fully leverage large volumes of data without introducing unnecessary security risks.