Gartner defines Business Intelligence (BI) as, “An umbrella term that includes the applications, infrastructure, tools, and best practices that enable access to and analysis of information to improve and optimize decision and performance.” BI encompasses a broad set of terms and tools that are used to make information about a business easily accessible in order to allow the user to make informed business decisions, cut costs, and identify inefficient processes.  Often, BI is used interchangeably with Data Analytics or to describe a process which includes data preparation, analysis, and visualization.

With a robust BI tool, employees and executives alike can analyze data themselves rather than waiting for IT to run complex reports.  This democratization of information access allows users to use hard data to back up decisions that previously could have been based merely on gut feelings and anecdotes.

The most common reasons that businesses implement BI tools are:

  • Performance measuring
  • Quantitative analytics
  • Company reporting
  • Knowledge management

However before decisions can be made, data must be collected in a single platform for easy access, analyzed to find meaning and communicated effectively to key decision-makers.

Data Warehousing

A Data Warehouse is a central repository of integrated data from one or more disparate sources.  Data warehousing is often referred to as ETL (extract, transform, and load).  The purpose of a data warehouse is to gather data from different sources, cleanse it, and store it on a single common platform for the use in enterprise-wide data analysis, reporting, or integrating data into other systems.

A data warehouse is different than a standard operational database because data warehouses are designed to give a long-range view of data over time.  Conversely, standard operational databases are optimized to maintain a strict accuracy of data in the moment by rapidly updating real-time data.  Data warehouses trade-off transaction volume in favor of data aggregation.

There is a clear need for a data warehouse when analytic requirements run afoul of the ongoing performance of operational databases.  In addition to the benefit of analyzing data from multiple sources and negotiating differences in storage schema using the ETL process, data warehouses also do the analytic work which leaves the transactional database free to focus on transactions.

Data Analytics

There is a clear overlap of data analytics and business intelligence and the two terms are often used interchangeably.  However, generally speaking, analytics is the data science process of asking questions behind the decision-making power of BI. Basically, data analytics is the use of data in an actionable way, while BI is the action made possible through analysis of data analytics.  Analytics tools are utilized to try and forecast what will happen in the future, whereas BI tools translate those forecasts and predictions into common language.

Data analysis is defined as processing data, whether from a single or multiple sources, using statistical and mathematical tools in order to generate insights.  Data analysis is a heuristic activity, where the analyst gains insight by scanning through all of the data.  Analytics is about applying a mechanical or algorithmic process to derive the insights like correlations between data sets.

Basically, data analytics is how you get to BI.  It is the process of answering questions that allow business users to accurately make predictions about the future.  Only then, can informed decisions be made with BI tools.

Data Visualizations

Data Visualizations or Visual Analytics have been a hot topic of late.  The reason for this type of increased interest is to meet the onslaught of big data head-on.  With so much data available, it can be a daunting task even for the most seasoned data analysis professional to make sense of it all.  While data analytics focus on analyzing business data, data visualizations report data in an easily consumable visual representation.

Data visualizations serve two purposes: To make data easier to understand (data analysis) and to make data easier to communicate.  The premise of data analytics hinges on the ability to put actionable data in the hands of business users quickly.  Visualizing data has made the process of data analysis quicker and also made BI accessible to a broader audience, empowering more decision-makers to make better decisions at a faster pace.

The brain is 60,000 times faster at identifying and processing visuals than text or numbers. Data visualization tools present the opportunity to transform jumbled collections of data into a story.  The imagery of data visualizations, whether it be geographical maps, heat mapping, or traditional charts, allows the human brain to pick out correlations and patterns that wouldn’t be immediately apparent looking at a table full of numbers.

There has been a societal shift to visual in the last few decades across industries.  Whether it’s the popularization of emojis, visual platforms like Instagram and YouTube or just the general fact that tweets including pictures receive twice the engagement of those that are entirely text-based.  Many modern BI tools have either adapted to Big Data by adopting data visualization capabilities in addition to their traditional offerings or have based their entire service around the ability to accurately represent data visually.

What Is Business Intelligence?

BI is the umbrella term that encompasses the other terms described here.  It is data-driven decision-making, including the generation, aggregation, analysis, and visualization of data to inform business strategies and decision-making.  A complete end-to-end BI tool will communicate data relationships by understanding the underlying data, validating the data, applying a meaningful analytic to the data and then visualizing the data so that businesses can proceed to use data for actionable insights and improvements.

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