How business intelligence works
There are four keys steps that business intelligence follows to transform raw data into easy-to-digest insights for everyone in the organisation to use. The first three—data collection, analysis, and visualisation—set the stage for the final decision-making step. Before using BI, businesses had to do much of their analysis manually, but BI tools automate many of the processes and save companies time and effort.
Step 1: Collect and transform data from multiple sources
Business intelligence tools typically use the extract, transform, and load (ETL) method to aggregate structured and unstructured data from multiple sources. This data is then transformed and remodelled before being stored in a central location, so applications can easily analyse and query it as one comprehensive data set.
Step 2: Uncover trends and inconsistencies
Data mining, or data discovery, typically uses automation to quickly analyse data to find patterns and outliers which provide insight into the current state of business. BI tools often feature several types of data modelling and analytics—including exploratory, descriptive, statistical, and predictive—that further explore data, predict trends, and make recommendations.
Step 3: Use data visualisation to present findings
Business intelligence reporting uses data visualisations to make findings easier to understand and share. Reporting methods include interactive data dashboards, charts, graphs, and maps that help users see what’s going on in the business right now.
Step 4: Take action on insights in real time
Viewing current and historical data in context with business activities gives companies the ability to quickly move from insights to action. Business intelligence enables real time adjustments and long-term strategic changes that eliminate inefficiencies, adapt to market shifts, correct supply problems, and solve customer issues.
Why companies benefit from using business intelligence tools
Because business intelligence tools speed up information analysis and performance evaluation, they’re valuable in helping companies reduce inefficiencies, flag potential problems, find new revenue streams, and identify areas of future growth.
Some of the specific benefits that businesses experience when using BI include:
- Increased efficiency of operational processes.
- Insight into customer behaviour and shopping patterns.
- Accurate tracking of sales, marketing, and financial performance.
- Clear benchmarks based on historical and current data.
- Instant alerts about data anomalies and customer issues.
- Analyses that can be shared in real-time across departments.
In the past, business intelligence tools were primarily used by data analysts and IT users. Now, self-service BI platforms make business intelligence available to everyone from executives to operations teams.
Here’s how business intelligence improves the way work is done in six key areas:
Access all your customer information in one place, so you can direct resources to key areas that will positively impact customer engagement and support.
Sales and marketing
Gain visibility into sales and marketing performance, consumer behaviour, and buying trends which ensures future marketing initiatives are effective and drive revenue.
Improve operations by automating routine analytics tasks, refining processes, reducing inefficiencies, and increasing productivity.
Use custom dashboards to get a holistic view of the company’s financial health, study historical data, calculate risk, and predict trends.
Automate data analysis and reporting to improve stock management, accelerate fulfilment, and help anticipate buying trends.
Security and compliance
Centralise data to improve accuracy and transparency, making it easier to uncover errors, security issues, and reduce compliance risks.
When evaluating business intelligence tools, look for a product that’s secure, compliant, globally available, and trusted. It should also have features that make BI insights accessible to your entire organisation—such as data visualisation, shared dashboards, artificial intelligence, and machine learning.