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In Data Management Last updated: July 31, 2023
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Data is a critical asset that can improve operations, efficiency, customer experience, and decision-making.

Towards this, businesses and organizations are generating, collecting, and storing huge volumes of data from different sources. However, as the data volumes increase, extracting the most useful information can be challenging, especially when the information is disorganized and scattered across different locations. 

One way to overcome these challenges is to store data in a suitable data repository. This provides a unified data source containing information that is filtered, searchable, and ready for analysis and reporting. 

Analyzing data in a data repository

In this, we will define the data repository and learn its benefits, the different types, and best practices.

What Is a Data Repository?

A black data repository icon featuring arrows emanating from a barrel.

A data repository is a library or archive that contains data to support analysis and reporting functions in research or business operations. In practice, a data repository is a general term that refers to the centralized location where data is stored. It can refer to a single storage device or a set of databases spanning across different devices. 

In a typical operation, organizations may collect disparate data from point-of-sale, CRM, ERP, spreadsheets, and other sources. They then move it into a data repository where it is sorted, cleaned, validated, formatted, organized, and stored. 

Usually, organizations may isolate and store specific types of data in the repository for analytical or reporting purposes. And since this is long-term storage, they can reuse it several times to perform different types of analysis. 

A typical data repository has three main layers.

  • Data sources layer
  • Data Processing Layer or warehouse
  • The target application layer, such as consists of users, analysts, and reporting

Why Do You Need a Data Repository?

Data is available from customer touchpoints, the internet, research, marketing, applications, and many other sources. However, it is usually in raw format, and organizations require appropriate tools to extract useful information to help them achieve their objectives. A good practice is to create a data repository to organize the data and make it available for analysis and other applications. 

The repository enables authorized users to easily and quickly access, retrieve and manage data using search, query, and other tools. Consequently, users and businesses can perform analysis, research, sharing, and reporting. And this enables them to streamline operations and make better data-driven decisions.

Supposing you want to establish which department in your organization incurs the most operational costs. You can create a data repository for the leases, security, energy costs, utilities, and other expenses. Keeping the data in a centralized place helps you to analyze and identify the department with the most expenses, hence making more informed and focused decisions when you want to cut costs.

Although data repositories are commonly used by research and scientific institutions, it is also applicable to general organizations and businesses.

Benefits of Data Repositories

Today, the majority of organizations are using data repositories as a means to manage and utilize their data more efficiently. The data repository concept has continued to gain popularity due to benefits such as easy information access, management, analysis, and reporting. 

Other advantages include:

  • Providing better visibility: Saving data in a central, reliable place makes it accessible anytime. In contrast, keeping the data in unshared applications or local silos means it is only available to an individual or a few people. This reduces its visibility and usability. Consequently, teams may take longer and use additional resources to access the data.
  • Easy access to useful data: Data in digital form is easy to search and access. Adding metadata to the data in the repository enables users to understand and use it much better.
  • Easy to secure data and comply with standards: It is much easier to protect data in a central location, unlike when scattered across different places. Additionally, a data repository makes it easy and less costly to comply with various regulatory standards.
  • Reusable data: The data repository contains a wide variety of data for analysis and reporting. Analysts and researchers can use the same data to generate different types of reports.
  • Provides useful insights: Using appropriate tools on data repositories allows you to get a multi-dimensional view of the data as opposed to analyzing information in different locations. 

Types of Data Repositories

Data repository is a general term that refers to the information archive. However, there are different repositories based on the target application or objective. And below are the four main types of data repositories.

#1. Data Warehouse

A flow diagram depicting the data repository in a Google Cloud Platform.

The data warehouse is one of the largest data repository types. In this category, businesses may collect data from several sources and in different formats. A typical data warehouse stores large volumes of data from different sources. Its structure enables organizations to easily organize the data, analyze and make reports. And this enables teams to make better data-driven decisions.

Information in a data warehouse may cover several subjects and is usually cleaned, filtered, and defined for a particular use.

#2. Data Mart

YouTube video

A data mart is a segregated section of a data warehouse. The subject-oriented data repository stores a subset of data focusing on a specific business function or department, such as finance, support, purchasing, or marketing.

Typically, a data mart is smaller in size. This helps speed up business processes by allowing access to the relevant data within a shorter period. These provide a cost-effective means to quickly gain actionable insights. 

#3. Data Lake

Diagram, data lake.

A data lake is a large archive containing data in any form. This includes unstructured, semi-structured, and structured data. It uses metadata to categorize and label the data, which is largely unstructured. A data lake provides total control and better data governance than a data warehouse.

#4. Data Cubes

Data cubes are multi-dimensional data repositories that focus more on complex data not supported by the other types. These have three or more dimensions, each representing a specific characteristic such as daily, monthly, or annual costs or sales. Data lakes enable researchers to assess data from various standpoints.  

Also read: Data Lake vs. Data Warehouse: What are the Differences?

Best Practices for Designing and Maintaining Data Repositories

A typical data repository has tools to store, manage and secure the information. It has features such as access control, indexing, compression, reporting, encryption, and more. 

When designing and creating a data repository, you need to consider several hardware and software factors in addition to working with data pipeline engineers, data analysts, and other experts. Depending on the domain, you must involve industry experts. For example, if creating a clinical data repository, you will work with doctors and other medical professionals. 

An effective data management strategy includes the following:

✅ Organizing files

✅ Secure storage and proper access controls

✅ Version and documentation control

✅ Supports collaboration

✅ Clear policies on reuse and sharing 

✅ Archiving and preserving the data for future reference or use.

While the steps to design, create and manage a data repository may differ from one industry or organization to the other, below are some best practices.

Limit the Scope at the Initial Stages

In the beginning, it is best practice to use a smaller scope of the data repository. One strategy is to use a smaller number of subject areas and data sets and increase the scope gradually. 

Choose the Right Tools

Tools are crucial in creating, storing, sharing, analyzing, and managing data repositories. As such, the data quality and analysis will depend on the tools you use. Since there are different types of tools with varying capabilities, ensure that your choice meets your needs. 

Automate as Many Processes as Possible

If possible, automate the load and maintenance tasks to improve efficiency, reduce time wastage and risk of errors. 

Design a Flexible and Scalable Repository

To accommodate increased data volumes, evolving data types, and formats, it is best practice to design and create a scalable repository. Such a system will serve the current needs and scale to support increased data types and volumes in the future. Also, it should be flexible to work with different tools and emerging technologies.

Protect Data at All Times

Ensure data integrity and security since any discrepancies, compromises, or theft can lead to inaccurate analysis results and bad decisions. Set proper access rules and give authorized users only the permissions they need to perform their duties. Additionally, encrypt the data at rest and in transit. Consider other measures like multi-factor authentication to add an extra protection layer.

Use Standard Data Models

Data modeling helps to convert data into valuable information that researchers and business leaders can understand better. Usually, information in a data repository is reusable.

Organizations can use the same data to extract useful information in different areas. Data has many contexts based on how it is used in different processes and analytic applications. As such, an organization may use several data models to cater to different analytical needs.

Indexing Data

Creating indexes on the data repository tables improves query performance and should be standard practice. It improves the query speed by providing an organized lookup table based on certain attributes and with entries that point to specific data locations.

Indexing on data repositories may vary depending on the usage. It can be light or extensive, depending on the usage. Ideally, the indexing strategy should focus on speeding up the ETL processes. One best practice when transforming the data is to ensure that the index provides the necessary information without missing useful data and being unnecessarily large. 

It is also important to balance the tradeoff between improved query performance of the data repository and the associated overheads and maintenance costs of the indexing.

Also read: Best ETL Tools for SMBs to Use.

Examples of Data Repositories

Data repositories fall under different categories:

  1. Institutional Repositories (IRs) for researcher’s institutions, such as Texas Data Repository by Texas A&M University Libraries.
  2. Disciplinary or domain-specific repositories (DRs): These are domain-specific and operated by a consortium of researchers or a professional organization, such as the Registry of Research Data Repositories (re3data) by DataCite, and the  Directory of Open Access Repositories (OpenDOAR), consisting of several academic open access repositories. 
  3. Open or general-purpose repositories, such as  DryadFigshare, and Harvard Dataverse

Use Cases of Data Repositories

Fintech, healthcare, e-commerce, supply chain, and other industries can benefit by using data repositories. By fully utilizing the large amounts of data they collect and generate, they can get better insights to optimize their services and deliver better and faster services.

Clinical Research

A woman in a lab coat is analyzing a data repository on her computer screen.

Clinical research is a data-intensive field. Getting the most out of the data helps to drive the healthcare industry in the right direction. Analyzing big data enables scientists and other professionals to dig deep into clinical trials and gain insights that help improve healthcare and save lives.

Financial Services

A data repository with the word financial services written on a piece of paper.

The financial services industry can benefit by analyzing large amounts of data they have. The analysis provides them with insights that they can use to improve services, efficiency, and revenues. Some of the areas financial institutions can use data repositories include: 

  • To generate financial reports by analyzing the data from a centralized location.
  • Enables AI-powered automated decision-making.

Final Words

Data is an essential asset in decision-making. However, organizations storing large volumes of data need the right solutions to gather, store, manage, and analyze the data. 

Towards this, a data repository provides a solution to consolidate and manage critical data. The repositories enable organizations to analyze data, gain insights, and make better data-driven decisions.

A data repository provides centralized storage of different types of information but in a logical way that makes it easy to access, search, analyze, and manage. It also helps organizations to secure, share, maintain, and ensure data integrity and quality and comply with regulatory standards.

Next, check out the best data management tools for medium to big business.

  • Amos Kingatua
    Amos Kingatua is an ICT consultant and technical writer who assists businesses to set up, secure, and efficiently run a wide range of in-house and virtual data centers, IT systems and networks.
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