Data Repository: Importance, Challenges, and Best Practices

By |2022-04-19T10:41:01+00:00November 7th, 2020|

With time, data is becoming more significant to business decision-making. This means you need platforms to gather, store, and analyze data. A data repository is a virtual storage entity that can help you consolidate and manage critical enterprise data.

In this blog, we’ll give a brief overview of a data repository, its common examples, and critical benefits. Next, we’ll also cover some main challenges and best practices associated with a data repository.

What is a Data Repository?

A data repository, often called a data archive or library, is a generic terminology that refers to a segmented data set used for reporting or analysis.

It’s a vast database infrastructure that gathers, manages, and stores varying data sets for analysis, distribution, and reporting.

What is a Shared Repository?

A shared repository is defined as a repository that can store revisions for multiple branches. Therefore, every branch will share one specific repository for its multiple revision storage.

Types of Data Repositories

Some common types of data repositories include:

Data Warehouse

A data warehouse is a large central data repository that gathers data from several sources or business segments. The stored data is generally used for reporting and analysis to help users make critical business decisions.

In a broader perspective, a data warehouse offers a consolidated view of either a physical or logical data repository gathered from numerous systems. The main objective of a data warehouse is to establish a connection between data from current systems. For example, product catalog data stored in one system and procurement orders for a client stored in another one.

Data Lake

A data lake is a unified data repository that allows you to store structured, semi-structured, and unstructured enterprise data at any scale. Data can be in raw form and used for different tasks like reporting, visualizations, advanced analytics, and machine learning.

Data Mart

A data mart is a subject-oriented data repository often a segregated section of a data warehouse. It holds a subset of data usually aligned with a specific business department, such as marketing, finance, or support.

Due to its smaller size, a data mart can fast-track business procedures as you can easily access relevant data within days instead of months. As it only includes the data pertinent to a specific area, a data mart is an economical way to acquire actionable insights swiftly.

Metadata Repositories

Metadata incorporates information about the structures that include the actual data. Metadata repositories contain information about the data model that store and share this data. They describe where the data source is, how it was collected, and what it signifies. It may define the arrangement of any data or subject deposited in any format.

For businesses, metadata repositories are essential in helping people understand administrative changes, as they contain detailed information about the data.

Data Cubes

Data cubes are data lists with multidimensions (usually three or more dimensions) stored as a table. They are used to describe the time sequence of an image’s data and help assess gathered data from a range of standpoints.

Each dimension of a data cube signifies specific database characteristics such as day-to-day, monthly or annual sales. The data within a data cube allows you to analyze all the information for almost any client, sales representative, products, and more. Consequently, a data cube can help you identify trends and scrutinize business performance.

Why Do You Need A Data Repository?

A data repository can help businesses fast-track decision-making by offering a consolidated space to store data critical to your operations. This segmentation enables easier data access and troubleshooting and streamlines reporting and analysis.

For instance, if you want to find out which of your workplaces incur the most cost, you can create an information repository for leases, energy expenses, amenities, security, and utilities, excluding employees or business function information. Storing this data in one place can make it easier for you to come to a decision.

Clinical Data Repository: Definition and Types

A clinical data repository (CDR) or clinical data warehouse (CDW) is defined as a real-time database that unifies data across multiple clinical sources to present a consolidated view of a patient’s details or records. Clinical data repositories aid the clinic staff to access data for one patient instead of identifying a huge number of patients with similarities or common characteristics.

The major data types of clinical data repositories are as follows:

  • Lab test results
  • Patient information, such as demographics
  • Discharge summaries
  • Transfer dates
  • Radiology images and reports
  • Pathology reports

Challenges Associated with a Data Repository

Although an information repository offers many benefits, it also includes several challenges that you must manage efficiently to alleviate possible data security risks.

Some challenges of maintaining data repositories include:

  • An increase in data sets can reduce your system’s speed. To rectify this problem, ensure that the database management system can scale with data expansion.
  • In case a system crashes, it can negatively impact your data. It’s best to maintain a backup of all the databases and restrict access to control the system risk.
  • Unauthorized operators can access sensitive data more quickly if stored in a single location than if it’s dispersed across numerous sources. On the contrary, implementing security protocols on a single data storage location is more accessible than multiple ones.

Best Practices to Create and Manage Data Repositories

When creating and maintaining software repositories, you have to make several hardware and software decisions. Therefore, it is best to involve all stakeholders during the development and usage phase of the data repositories. For example, in case of building a clinical data repository architecture, it is a good idea to involve doctors, data experts, analysts and data pipeline engineers in the initial planning stages.

Here are some of the best practices to help you make the most of this storage solution:

1. Select the Right Tool

Using ETL tools to create a data repository and transfer data can help ensure data quality is maintained during the process. But keep in mind that different data repository tools offer additional features to create, maintain, and control the repository. So, find a tool that provides the features that support your business requirements.

2. Limit the Scope Initially

It’s best to narrow down the scope of your information repository in the initial days. Accumulate smaller data sets and limit the number of subject areas. Gradually increase the complexity as the data operators get familiar with the system.

3. Automate as Much as Possible

Automating the process for loading and maintaining the data repository saves the user from manual efforts and reduces the chances of errors.

4. Prioritize Flexibility

The data repository should be scalable enough to accommodate evolving data types and increase volumes. So, make flexible plans that make allowance for alterations in technology.

Wrap Up

As more and more businesses adopt data repositories to store and administer their ever-increasing data, a secure approach becomes imperative for your company’s overall security. Creating comprehensive access rules to permit only authorized operators to access, change, or transfer data will help secure your enterprise data.

Astera Centerprise is an automated data integration tool that helps in data management with features such as data cleansing, profiling, and transformation all in one solution. Contact our team for a personalized demo.