In recent years, the need to safeguard data for workflow and security purposes has skyrocketed. Databases solved the problems of data organization and structuring for a small to medium amount of data. However, larger data with multiple datasets require a more organized system of storage. The need to store this large and more complex information birthed the concept of Data Warehouses.

What is a Data Warehouse?

According to Oracle, A “data warehouse is a type of data management system that is designed to enable and support business intelligence (BI) activities, especially analytics”. It acts as a central repository for information, channeled in from multiple sources. The data coming into a Data Warehouse could be structured or unstructured. However, the data is processed and transformed for easy navigation and location of information by the users. In simple terms, a data warehouse unifies information sent by multiple sources into a single comprehensive database.

How does a Data Warehouse function?

Data warehouses do not produce, rather they serve as a unified database for all input data from multiple heterogeneous sources. Once data is sent into a data warehouse, it becomes impossible to make adjustments or alter the details. Data warehouses function by analyzing and querying all historical data derived from transactional sources.

Implementing a Data Warehouse

Data is delicate; hence, data warehousing implementation has to be strategic and efficient to prevent loss of vital information. Improper data warehouse implementation can infect data quality and ease of access. The best way to implement a data warehouse is to employ the strategy below;

  1. Enterprise review: Here the current state of the business is reviewed, to identify facts and attributes of the current data input system.
  2. Phased Delivery: The implementation of a data warehouse should be based on phases. Through the phasing, each section and area of the architecture is catered for properly. Related entities of the business are integrated with each other in this phase.
  3. Testing: Before incorporation, all sections of the data warehouse should be piloted to avoid discrepancies.

Tiers in Data Warehousing

According to Investopedia, four steps define the development of a data warehouse; data extraction, data cleaning, data conversion, and data consolidation.

  • The data extraction process involves the collection and compilation of large information for various data sources.
  • Data cleaning describes the process of combing through the compiled data for inconsistencies that may affect the overall quality of data.
  • Data conversion describes the operation of converting the input data into a compatible warehouse format. The final format hinges on the implemented warehouse architecture.
  • Data consolidation is the last phase in the development of a data warehouse. It is an operation initiated to sort and summarize the input data to facilitate use and co-ordination.

Why businesses implement data warehouses

There are many reasons for deploying and effectively using a data warehouse. According to (Almeida, 2017), “From an IT perspective, separating the analytical processes in a data warehouse from the operational processes in the production applications and transactions can enhance the performance of both areas. From a business perspective, a data warehouse platform can deliver a practical way to view the past without affecting the daily operations of the business”. Also, another reason why businesses implement a data warehouse is;

  • Improved data consistency

The unified format employed by the comprehensive databases in a data warehouse ensures that all irregularities in data are eliminated.

Scaling with a data warehouse

Your Business data is crucial to the overall workflow. Implementing a data warehouse would take data safety a further notch. However, proper implementation is also key to the process. With a data warehouse, your transactional systems, operational databases, and other data sources now have a unified housing, making it easy to recall, structure, and organize your business data.

As a business executive with enormous data and records, developing a data warehouse structure should be a priority as it poises your business for quick decision making you more competitive and profitable.

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