Data Repository: Types, Challenges, and Best Practices
The importance of data is growing as everyone uses data to make decisions. Companies are focusing on analyzing large data sets, which help them to make winning business decisions. This data is stored in a tools known as a data repository and other popular names include data archive and data library. The data repository is just like the infrastructure of databases that collect, manage and store data sets accordingly.
Let us understand Data Repository Types?
- Type 1: Data Warehouse – A data warehouse is a large repository of data that brings data from several business segments or sources. The data stored is used for analysis and reporting that will enable the users to make sound business decisions.
- Type 2: Data Lake – A data lake stores a collection of raw data sets from external and internal data sources. The data can be structured, semi-structured, and unstructured.
- Type 3: Data Mart – It is a subset of a data warehouse that is focused on a particular subject, department, and business area. Datamart makes available specific data to a defined group of users that helps them to swiftly access the insights without spending much time searching in an entire data warehouse.
- Type 4: Data Cube – A Data cube is used to reflect the information that is to be retrieved from a huge set of complex data. Data includes many dimensions and helps to achieve the latest scenarios by establishing performance and trend analysis.
Why do we need a Data Repository?
- It is vital to organize and analyze the data that is coming from different sources
- To pinpoint trends, you need to assess several years of historical data
- Restructuring data and renaming fields and tables to make them more meaningful to the business users
- Before the business users access the data, the information that is stored in the data repository is more useful as it is already cleaned and optimized
- Data repositories ensure that all in the company are working with the single version of the truth i.e same data
Challenges associated with Data Repository
- It is vital to make sure that the database management system has the scalability feature with the data expansion, as any increase in the datasets can reduce the system’s speed
- It’s best to maintain a backup of all your databases, as in case of any systems crash, it may negatively impact your data
- There might be the possibility of accessing sensitive data by unauthorized operators as the data is stored in a single location. It is very challenging to implement security protocols on multiple storage locations
Best Practices for working with Data Repositories
- Data repository is a continuous process and needs to grow
- Focus on hiring experts who can build and maintain the data repository
- In the initial stages, collect small sets of the data and focus on restricting several subjects
- Use ETL (Extract-Transformation-Load) tools that ensure data quality when migrating the data to the data repository
- Building a data warehouse first is important before building the data marts
- Focus and decide how many times the data warehouse will load new data as it depends on the volume of data
- Metadata is required for the quality data ,reporting and analysis
- Data users should have access to training and support
- The data repository continues to evolve and the types of data it collects for the usage will change. Hence, having flexible plans will help with any changes in technology
To explore more, you can request a demo of Amurta’s Data Insights Platform by just filling out an inquiry form. To know more information and for any queries please feel free to contact us at +1 888 840 0098 and you can email us at sales@amurta.com, we will be happy to assist you.
