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Data Warehouse: Concept, Types & Uses

by Mangal Tiwari
Data Warehouse: Concept, Types and Uses

A data warehouse provides a place for organizations to integrate data from various sources. Users can create reports or perform ad-hoc queries without affecting operational system performance. A data warehouse increases data consistency and improves access of end-users to enterprise-wide data. It can improve computing costs and increase the productivity of businesses.

What is a data warehouse?

A data warehouse is the repository of all the electronically stored data of an organization coming from its operational systems. It provides a single, homogeneous database to support organizational decision-making and forecasting. The data is organized in such a way that it facilitates scheduled reporting and ad-hoc queries.

Main types of data warehouses

The three main types of data warehouses are the Enterprise Data Warehouse, Operational Data Store and a Data Mart. They each play a role in data management.

The Enterprise Data Warehouse (EDW) is a central database that offers access to aggregated data spanning the entire organization. The data is labeled and categorized for easy access. It’s possible to run complex queries and obtain insights for assessing risks early and driving decisions.

The Operational Data Store (ODS) refreshes in real-time and stores data specific to a chosen activity.

The Data Mart (DM) is a part of the data warehouse and is the central data repository for individual departments within the organization. Information that passes through it is stored automatically and organized for use at a later stage. Data Marts add another level of customization to a data warehouse, streamlining information to the teams that need it most. Department leaders can make decisions faster and capitalize on time-sensitive marketing opportunities.

Characteristics of a data warehouse

The data warehouse provides a concise view of a specific subject such as sales, marketing or customer service. It integrates data from a number of sources to help with effective analysis. Data contains a time element and offers historical information – once it is in the data warehouse, it can’t be changed or updated. Being non-volatile means that previous data is not erased when new data is entered. The only data operations performed in a data warehouse are data loading and data access.

Difference between a data warehouse and a traditional database

A database was built to store current transactions. Users get fast access to specific transactions related to ongoing business processes that help them to make strategic decisions and improve their productivity. Workflow automation can also increase productivity.

A data warehouse is an analytical database that layers on top of transactional databases to facilitate analytics. A data warehouse is different from a traditional database as it stores large quantities of historical data and enables complex queries across it. It stores files and folders in an organized way so the data is readily available. Using a data warehouse means decision-makers in a business don’t have to rely on poor quality, incomplete data or hunches and risk delivering inaccurate results.

 Data warehouse architecture

The architecture of the data warehouse is complex as it contains commutative and historical data from many different sources. Single-tier architecture minimizes the amount of data storage to remove redundancy and isn’t often used.

The two-tier architecture separates the physically available sources and the data warehouse. This model can have connectivity issues due to network limitations and can’t support a large number of end-users.

Three-tier architecture is the most widely used. The bottom tier is usually a relational database and back-end tools clean, transform and load data into the layer. This means that information can be reviewed, evaluated, and deleted or stored if it is useful and relevant.

The middle application tier acts as the mediator between the end-user and the database. The top tier is the front-end client layer, where the tools like query tools or reporting tools and API connect to get data.

Data warehouse examples

The structure of data warehouses makes analytical queries easy and no advanced knowledge is necessary. New cloud-based tools are enabling enterprises to set up data warehouses with more scalability, storage, and less upfront investment.

In many businesses, a data warehouse may integrate customer information from a website, mailing lists or point-of-sale systems. It may integrate employee information, including demographic data, time cards and salary information. By combining all the data in one place, it is possible to analyze customers or employees in a more comprehensive way.

The retail industry: In the retail sector, businesses will use a data warehouse for business intelligence and forecasting. It enables them to keep track of items and promotional deals and to analyze consumer buying trends. Data mining to look for patterns can result in higher sales and more profits.

The investment and insurance sector: Analyzing customer and market trends are the primary uses in this sector. Forex and stock markets are two areas where data warehouses play a crucial role with a focus on real-time streaming to avoid losses.

The healthcare sector: In this sector, the data warehouse enables the generation of treatment reports, forecasting of outcomes and sharing of data with research labs, insurance providers etc.

Businesses in the fitness, nutrition and weight-loss industries use insights from data to better target and personalize offerings to their customers. Revamping their analytics architecture can help them reduce data acquisition time and improve database accuracy for marketing campaigns. Producing better health outcomes for their clients ultimately leads to better business performance.

A final word

Organizations that move beyond simple databases and use data warehouses have an advantage when it comes to marketing, pricing, forecasting, and much more. They don’t have to wonder whether data is consistent or accessible, which ensures more data integrity and quality when it comes to making sound decisions.

Finding the right warehousing solution to suit specific business needs can make a difference to profitability. As data warehousing moves to the cloud, businesses do not require a big upfront investment and don’t need to go through a difficult deployment process. Cloud warehousing offers advantages such as flexibility, accessibility and lowers the barriers to entry.

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