Business Intelligence (BI) combines business analytics, data mining, data visualisation, data tools and infrastructure and best practices to help organisation to make more data-driven decisions.
Ultimately you know you’ve got modern business intelligence, when you have a comprehensive view of your organisation’s data, and use that data to drive change and eliminate inefficiencies, and quickly adapt to market or supply changes.
It’s important to note that this is a very modern definition of BI – and BI has had a strangled history as a buzzword. Traditional Business Intelligence originally emerged in the 1960’s as a system of sharing information across organisations. It developed further in the 1980’s alongside computer models for service solutions.
Modern BI solutions prioritise flexible self-service analysis, governed data on trusted platforms, empowered business users and speed to insight.
Much more than a specific thing, Business Intelligence is rather an umbrella term that covers the processes and methods of collecting, storing and analysing data from business operations or activities to optimise performance.
Our people have gained working knowledge and experience over the years in these umbrella processes being as follows;
Using databases, statistics and machine learning to uncover trends in large datasets.
Sharing data analysis with stakeholders so they draw conclusions and make business decisions.
Benchmarking: Comparing current performance data to historical data, to track performance against goals, typically using customised dash-boards.
Using preliminary data analysis to find out what happened-good or bad within the business.
Asking the data specific questions, BI pulling the answers from datasets.
Taking the results from descriptive analytics and further integrating that data using statistics such as how this trend happened and why.
Turning data analysis into visual representations such as charts, graphs and histograms to easily consume data.
Exploring data through visual storytelling to communicate insights on the fly and stay in the flow of analysis.
Compiling multiple data sources, identifying the dimensions and measurements, and preparing it for data analysis.