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What is a Data Mart?

Defining data marts

A data mart is a simple form of data warehouse focused on a single subject or line of business. With a data mart, teams can access data and gain insights faster, because they don’t have to spend time searching within a more complex data warehouse or manually aggregating data from different sources.

Why create a data mart?

A data mart provides easier access to data required by a specific team or line of business within your organization. For example, if your marketing team is looking for data to help improve campaign performance during the holiday season, sifting through and combining data scattered across multiple systems could prove costly in terms of time, accuracy, and ultimately, money.

Teams forced to locate data from various sources most often rely on spreadsheets to share this data and collaborate. This usually results in human errors, confusion, complex reconciliations, and multiple sources of truth—the so-called “spreadsheet nightmare.” Data marts have become popular as a centralized place where the necessary data is collected and organized before reports, dashboards, and visualizations are created.

The difference between data marts, data lakes, and data warehouses

Data marts, data lakes, and data warehouses serve different purposes and needs.

A data warehouse is a data management system designed to support business intelligence and analytics for an entire organization. Data warehouses often contain large amounts of data, including historical data. The data within a data warehouse usually is derived from a wide range of sources, such as application log files and transactional applications. A data warehouse stores structured data, whose purpose is usually well-defined.

A data lake allows organizations to store large amounts of structured and unstructured data (for example, from social media or clickstream data), and to immediately make it available for real-time analytics, data science, and machine learning use cases. With a data lake, data is ingested in its original form, without alteration.

The key difference between a data lake and a data warehouse is that data lakes store vast amounts of raw data, without a predefined structure. Organizations do not need to know in advance how the data will be used.

A data mart is a simple form of a data warehouse that is focused on a single subject or line of business, such as sales, finance, or marketing. Given their focus, data marts draw data from fewer sources than data warehouses. Data mart sources can include internal operational systems, a central data warehouse, and external data.

The benefits of a data mart

A data mart dedicated to a team or specific line of business offers several benefits:

  • A single source of truth. The centralized nature of a data mart helps ensure that everyone in a department or organization makes decisions based on the same data. This is a major benefit, because the data and the predictions based on that data can be trusted, and stakeholders can focus on making decisions and taking action, as opposed to arguing about the data itself
  • Quicker access to data. Specific business teams and users can rapidly access the subset of data they need from the enterprise data warehouse and combine it with data from various other sources. Once the connections to their desired data sources are established, they can get live data from a data mart whenever needed without having to go to IT to obtain periodic extracts. Business and IT teams both gain improved productivity as a result
  • Faster insights leading to faster decision making. While a data warehouse enables enterprise-level decision-making, a data mart allows data analytics at the department level. Analysts can focus on specific challenges and opportunities in areas such as finance and HR and move more rapidly from data to insights, which enables them to make better and faster decisions
  • Simpler and faster implementation. Setting up an enterprise data warehouse to cater to the needs of your entire organization can require significant time and effort. A data mart, in contrast, is focused on serving the needs of specific business teams, requiring access to fewer data sets. It therefore is much simpler and faster to implement
  • Creating agile and scalable data management. Data marts provide an agile data management system that works in tandem with business needs, including being able to use information gathered in past projects to help with current tasks. Teams can update and change their data mart based on new and evolving analytics project
  • Transient analysis. Some data analytics projects are short-lived—for example, completing a specific analysis of online sales for a two-week promotion prior to a team meeting. Teams can rapidly set up a data mart to accomplish such a project

Moving data marts to the cloud

Business teams are striving to become more agile and data-driven to guide strategy and improve day-to-day decision-making, yet they typically struggle to turn an ever-growing mountain of data into insights. CFOs spend on average 2.24 hours per day sifting through spreadsheets. Although business teams usually turn to IT for help, IT teams may have a hard time keeping up with business users’ demands for increased access to more disparate data sources, larger volumes of data, and faster query times.

Setting up data marts can also be a concern for IT teams already burdened with a heavy workload, because they need to manage those data marts on an ongoing basis and ensure data security. Moving data marts to the cloud helps alleviate concerns of both business and IT teams by moving administration and security tasks to the cloud service provider which decreases the need for manual intervention and lowers operational costs.

How Oracle Autonomous Data Warehouse powers cloud data marts

Oracle provides a complete and self-service solution that allows business teams to get the deep, trustworthy, data-driven insights they need to make quick decisions.

Business teams can quickly combine all necessary data across different sources and formats, including spatial and graph, in a converged database to drive secure collaboration around a single source of truth provided by data marts. Analysts can easily leverage self-service data tools and embedded machine learning—with zero coding required—to accelerate data loading, transformation, and preparation, automatically find patterns and trends, make predictions, and gain insights based on data with transparent lineage.

Governed and secure, Oracle’s solution makes it possible for IT to reduce risks. IT teams additionally can rely on a simple, reliable, and repeatable approach for all data analytics requests from business departments, greatly improving productivity.

Oracle Autonomous Data Warehouse intelligently automates provisioning, configuring, securing, tuning, scaling, patching, backing up, and repairing. This eliminates nearly all the manual and complex tasks that can introduce human error. Built-in data tools enable simple, self-service data loading, data transformation, business modeling, and automatic insights for data marts. DBAs can shift their efforts from routine database administration to new application designs, and helping business departments achieve their goals. Business users in finance, HR, and marketing can be empowered with secure data access and consistently high query performance for any number of concurrent users, even at peak times. Autonomous Data Warehouse automatically scales according to workload needs, without any downtime.

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