Data Marts (With Definition, Types, Users, and Benefits)

By Indeed Editorial Team

Published April 25, 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Data marts are one way for firms to arrange their data to make it more accessible. Data organization is critical to corporate operations because access to specific data sets can help projects operate more quickly, strengthen client relationships, and increase income. Understanding what a data mart is and who typically uses it can help you determine whether this technology is useful in your career. In this article, we explain what data marts are, discuss their types and benefits, explain who uses them, discover how to implement and use this powerful data organization tool, and share best practices.

What are data marts?

Data marts, also known as data stores, are repositories of data you can dedicate to a single subject or aspect of a business. To generate usable data collections, data stores may prioritize factors such as specificity, accessibility, marketing, sales, finance, or staff performance. Businesses use this data to optimize operations and better understand their internal processes.

Related: How to Learn Data Science (A Complete Guide)

Different types of data marts

The three distinct forms of this data repository are:

Dependent data stores

You can frequently derive dependent data stores from an organization's current data warehouse. The data stores depend on warehouse data feeds and functions as a functional component of the entire warehouse. Dependent data stores only access the warehouse's information sets when the team requires specific data. You can create dependent data stores by clustering specific data sets and then extracting a subset of the data for analysis as needed.

Hybrid data stores

A hybrid data mart is a data warehouse that connects independent and dependent data stores, using both an internal and external data warehouse. The hybrid paradigm combines the agility of independent data stores with the dependability of the organization's data warehouse. By defining a data set as a dependent of the data warehouse and connecting it to external sources, you can construct a hybrid data store. This enables the team to gather data both internally and externally.

Independent data store

Independent data stores function independently of a data warehouse. Smaller businesses or those unwilling to invest significant money in developing a complete data warehouse frequently use these marts. Independent systems provide the business with the data it requires expanding beyond budget constraints because they can be more adaptable and accessible because they're not dependent on a data warehouse.

Benefits of data marts

The following are some data store advantages you can consider:

  • Centralized data: You can access information from a single source because data stores help centralize specific data sets. This helps prevent data inconsistencies and minimizes errors.

  • Scalable data management: Data stores increase data sets' scalability or their capacity to grow as business demands alter. You can meet a business's data requirements by growing data in a data store.

  • Fast implementation: You can quickly and easily implement data stores because they're more focused than data warehouses. This can help a corporation save time and money.

  • Quick data access: Data stores simplify teams to review large data sets and easily access specific data. This can significantly speed up the data collecting process and save both time and money.

  • Better decision-making: With access to more timely and reliable data sets, teams can make more informed decisions. This can cause increased overall efficiency and cost savings.

  • Low cost: Establishing a data store might be substantially less expensive than establishing a full-scale data warehouse for the organization. You can reinvest the savings in other areas of the firm.

Related: How to Learn Data Entry and Available Career Options

Who uses data stores?

In the following fields, data stores can be very useful:

  • Finance: Finance data stores assist organizations in categorizing their money and storing distinct kinds of financial data. This is especially beneficial in financial planning.

  • Sales: Sales teams frequently use data repositories to gather data for specific sales approaches or to organize promotional periods. For instance, sales staff may collect data to prepare for the holiday season.

  • Marketing: Data stores enable marketing professionals easy access to the data for analytics. Marketing professionals build effective marketing campaigns by analyzing consumer, product, and analytics data.

How to use data stores

The following steps outline establishing and using data stores:

1. Design your data store strategy

When developing your data store strategy, ask yourself whether you're constructing a future data warehouse, using an existing warehouse, or developing independent data stores. Determine the type of data you wish to collect and keep and the purposes for which the business can use the data. Determine whether they require modifications to the existing data warehouse system to accommodate the installation of specific data stores and the associated expenses. You might scale the data mart as needed.

2. Construct the data store architecture

Construct the architecture of your data store following the specifications defined in the previous phase. Choose a database for your data store and change to your existing data warehouse structures. You can provide access to make data more available and easier to use by team members. Determine that data store users access the data they require via the system permissions and connectivity.

3. Populate the data store architecture

Data stores design populating entails performing the data flow between your data warehouse and external sources. This populates the data store with the data you may need and enables you to debug any issues that may arise. You can select which phrases make data available, clean and normalize data, and which indices to use to increase the data store accessibility.

4. Access your data stores

Access populated data stores by creating queries for specific data sets and ensuring the data store retrieves them. This is an excellent time to address specific concerns and ensure the data stores operation. You can investigate how the data store integrates with your existing data warehouse, how users feel about the design and functionality, and what future enhancements you might make. Documenting the deployment step can be an effective way to keep track of issues, successes, and user input to make future improvements.

Related: How to Become a Data Architect (With Essential Skills)

How to implement data stores

The following sections outline the steps involved in implementing data stores:

1. Design

This is the initial phase of the data store installation. It encompasses all tasks, from the request for a data store to getting the required information. Finally, you can develop the logical and physical components of the data store. The design phase entails the following activities:

  • Compilation of business and technical needs and identification of data sources

  • Choosing the most relevant subset of data

  • Creating the data mart's logical and physical structure

2. Construct

This phase of implementation is the second stage, and it entails the development of the physical database and logical frameworks. After designing the database schema items in a previous phase, you can follow this step, which entails the implementation of the physical database. You need a relational database management system to create a data mart.

3. Populate

The third phase involves populating the data store with data. You can accomplish these population-related operations using an Extract Transform Load (ETL). The filling stage entails the following activities:

  • From source to target data mapping

  • Source data extraction

  • Data cleansing and transformation operations

  • Data loading into the data mart

  • Metadata creation and storage

4. Access

Access is the fourth phase, and it entails putting the data to use by querying it, making reports and charts, and publishing them. You can use either the command line or the graphical user interface to access the data store. The end-user submits queries to the database and returns the results. The accessing step is to accomplish:

  • Establishing a meta-layer for converting database structures and object names to business terms. This enables non-technical people to access the data store quickly.

  • Creating and maintaining database schemas

  • Configuring APIs and interfaces as necessary

5. Manage

This is the last step in implementing a data store. You can administer a data store via the graphical user interface or the command line. This step encompasses management responsibilities, such as:

  • Ongoing management of user access

  • Optimizing system is necessary to get the desired performance increase

  • Adding new data to the data mart and maintaining it

  • Developing recovery scenarios and ensuring system availability in the event of a failure

Related: Data Entry Skills and How to Improve Them

Best practices for implementing data stores

The following are the best practices to follow while implementing a data store:

  • Always arrange the data store's source by department.

  • Always measure the data store implementation cycle in weeks, as opposed to months or years.

  • It's critical to incorporate all stakeholders during the planning and design phases, as data store implementation might have challenges.

  • Costs associated with data store hardware/software, networking, and implementation are to be allocated precisely in your plan.

  • Even if you develop the data store and the database on the same hardware, they may require separate software to execute user queries. You can also access additional processing power and disk storage requirements to ensure a quick response time for the user.

  • Always locate the data store independently of the data warehouse. That is why it's critical that they have the networking capacity to handle the data volumes required for data transfer to the data store.

  • Costs associated with implementation can account for the time required to complete the data store loading procedure.


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