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Data Warehouse Overview - History, Functions and Features

History of Data Warehouse

The famous author of several Data Warehouse books, William H. Inmon first coined the concept of Data Warehouse (DW) in 1990. Inmon defined data warehouse as ‘a subject-oriented, integrated, time-variant and non-volatile collection of data.’ Extremely useful for Data Analysts, this data helps them to take business decisions and other data-related decisions in the organization.

For instance, in the world of E-commerce, a data warehouse includes and maintains data about products, costumer’s login credentials, addresses, buying behavior details, checkout details and other technical and non-technical information. In a warehouse, all the information is combined and consolidated in different tables such that companies can extract and analyze that data in an integrated manner.
Over the years, many applications have been designed to store huge datasets. A data warehouse is specially designed to perform business intelligence activities and enable professionals and employees to comprehend and improve the organization’s overall performance.

Functions of Data Warehouse

  • Maintaining past and present records
  • Helping organizations to take effective business decisions with precise data analysis.

It provides the multidimensional view of consolidated data in a warehouse. Additionally, the data warehouse environment supports ETL (Extraction, Transform and Load) solutions, data mining capabilities, statistical analysis, reporting and Online Analytical Processing (OLAP) Tools, which help in interactive and efficient data analysis in a multifaceted view.

It is important to note that the DW operates with data extraction from multiple sources- internally built systems, third-party business groups, purchased applications and others. Many operations like production, transactions, sales and marketing, human resourcing are included from these source locations. With the buzz of big data and E-commerce, DW involves dealing with terabytes and petabytes of consumers and products data generated from each website click.
What happens next once all data is stored and arranged in databases? Here comes the term ‘data mining’ into action. It is the process of analyzing data to produce meaningful information and provide answers to the queries asked. Data mining uses various analytic tools to create summary reports, which are helpful in taking business decisions. The use of data mining in business applications leads to Business Analytics and Business Intelligence.

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Key Features of Data Warehouse

  • Subject-oriented: Data warehousing gives you an option of building your warehouse including the data as and what you want to extract and analyze. Thus, a subject matter expert can answer relevant questions from the da For example, a sales executive for an online website can develop a subject-oriented database including the data fields he wants to query. The salesperson can then excerpt data writing different queries like, “How many customers purchased Database Architect Course today?”
  • Integrated: Similar to the concept of subject-orientation, data warehouses supports consistency by arranging data from diverse sources in a uniform and rational forma It should not allow any conflicts in field names and other units of measure. Having accomplished this steadiness, we can refer it to be an integrated data warehouse.
  • Nonvolatile: As the name suggests, nonvolatile DW refers to the no data change once created. It is an important and relevant feature since the aim of developing a warehouse if to evaluate what has occurred until then.
  • Time-variant: DW believes in adopting and adapting to the changing trends and thus, allows inclusion of novel business patterns and also identifies what’s trending in business relationships, involving large volumes of data.

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About the Author

Data Analyst & Machine Learning Associate

As a Data Analyst & Machine Learning Associate, Nishtha uses a combination of her analytical skills and machine learning knowledge to interpret complicated datasets. She is a passionate storyteller who transforms crucial findings into gripping tales that further influence data-driven decision-making in the business frontier.