With the passage of time, data is becoming more significant to business decision-making. This means you need platforms that can gather, store, and analyze data. A data repository is one such virtual storage entity that can help you consolidate and manage critical enterprise data.
In this blog, we’ll give a brief overview of what is a data repository, its common examples, and key 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 huge 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 repository for its multiple revision storage.
Types of Data Repositories
Some common types of data repositories include:
A data warehouse is a large data repository that brings together 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.
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.
A data mart is a subject-oriented data repository that’s 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 relevant to a specific area, a data mart is an economical way to acquire actionable insights swiftly.
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 source of data 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 are lists of data with multidimensions (usually 3 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 characteristics of the database such as day-to-day, monthly or annual sales. The data contained within a data cube allows you to analyze all the information for almost any or all clients, sales representatives, 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 a data 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 repository aids the clinic staff to access data for one patient instead of identifying a huge number of patients with similarities or common characteristics.
The common 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 a data repository offers a plethora of 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, make sure 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 easily 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 easier than on multiple ones.
Best Practices to Create and Manage Data Repositories
When creating and maintaining data repositories, you have to make several hardware and software decisions. Therefore, it’s best to involve all stakeholders during the development and usage phase of data repositories.
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 to it can help ensure data quality is maintained during the process. But keep in mind that different data repository tools offer different features to create, maintain, and control the data repository. So, finding a tool that offers the features that support your business requirements is essential.
2. Limit the Scope Initially
It’s best to narrow down the scope of your data 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 increasing data volumes. So, make flexible plans that make allowance for alterations in technology.
As more and more businesses are adopting data repositories to store and administer their ever-increasing data, a secure approach becomes imperative for your company’s overall security. Consider creating comprehensive access rules to permit only authorized operators with a genuine business need to access, change, or transfer data. This will help secure your enterprise data.