Data is a vital asset to businesses. Insights gathered from data taken from multiple sources can help companies and organizations greatly improve their decision-making. Data warehouses are a handy tool for this. In this article, we will make sense of the data warehouse for business intelligence.
A data warehouse is a central repository connected to several data streams, such as transactional and relational databases. It offers the technical infrastructure needed to store and aggregate large amounts of structured data from one or more sources and help analyze large volumes across the enterprise.
The workflow of a typical data warehouse involves three steps. First, an Extract Transform Load (ETL) process processes the data, integrating data from multiple systems and sources where required and ingesting it in the right format into the data warehouse with business intelligence.
Next, the data is passed along to Data Marts, which hold the data for specific divisions, departments, or other organizational business units, making sense of a data warehouse.
After this, Business Intelligence (BI) tools help those who need it to access the data — complete with live dashboards, periodic reports, and “slice and dice” multi-dimensional data analysis.
A brief history of data warehouses
IBM researchers Barry Devlin and Paul Murphy first suggested the idea of data warehousing in 1988. Their concept was for a nonvolatile, subject-oriented, integrated, time-variant data collection tool that could support management decisions, whether reporting or carrying out “what-if” simulations.
This meant a structured collection of intelligently timestamped data from multiple sources that could be used as a stable, ongoing record of current and historical information.
Computers and business data requirements have advanced significantly since then. As Devlin wrote in an article commemorating 30 years since the concept of data warehousing was coined, at the time, a typical personal computer boasted a 20MB hard drive and monitored with a resolution of just 640×480 pixels.
Today’s world of data collection has also come an almost unimaginably long way. We now live in a world of big data, with more data and entirely new sources of potentially valuable data not previously available.
Data warehouses must be able to absorb this (often semi-structured) data, scrub it to make it fit for purpose, combine it with other datasets, and then make it accessible to businesses in a genuinely useful way.
Not the same as a database or data lake
When it comes to setting up the right data storage systems for an organization, it’s important to know what a data warehouse is and what it isn’t.
It is not a database. A database is a tool for recording data. Simultaneously, a data warehouse is an information system for storing historical data from single or multiple sources to carry out fast analytics.
It also isn’t a data lake, another widely used data storage method. Unlike a data warehouse, data lakes store data without a particular structure. As its name suggests, a data lake is a big raw data pool. But a pool that does not have a purpose yet. Setting up and loading data into a data lake requires considerably less expertise than a data warehouse. Still, extracting insights and making queries can be more challenging because they have not been ready with a specific purpose in mind.
A data lake is more likely to be data scientists with special tools. While business professionals will find the processed data in a data warehouse more palatable. A user can utilize a data warehouse with only knowledge of the topic. Rather than requiring expertise in dealing with unprocessed data, making sense of the warehouse.
Protecting: Making Sense of the Data Warehouse
Increasingly, companies have learned that data is often their most valuable asset. For this reason, it’s crucial that they protect their data as best as they can, whether on-premises, in the cloud, or in a hybrid environment.
Fortunately, there are multiple steps organizations can take to do this. One crucial one involves proper data masking and encryption, meaning that sensitive data would render unreadable. To a bad actor if it does not exist any longer somehow.
However, in the same way, a person wants to protect their house. Rather than worrying about how to tackle a burglar once they’re inside. The goal is to stop bad actors from gaining access, to begin with.
To achieve this, consider the use of a database firewall. Database activity monitoring and user rights management tools, among other preventive measures. In all cases, the purpose is to monitor data warehouses. So, block threats that arise while generating real-time alerts if anything suspicious happens.
Data warehouses have been a game-changer for many businesses. Ensure that you employ the right measures to protect them so that you can continue to benefit from them.
