Recent developments in modern technology and computing power have allowed businesses to not only collect vast quantities of data but also store, analyze, and present data-driven findings in clear and impactful ways. It's a fairly recent development; before the explosion of growth in data science and information technology over the last few decades, large amounts of valuable raw data sat dormant. That's because there were no processes available for harvesting its information and integrating it into business activity.
The recent and rapid evolution of data science and the leveling-up for data management and visualization have revealed a rapidly developing field of business intelligence (BI) and analytics. These essential tools of modern business allow corporate executives and company managers to make more informed and effective decisions about current and future plans.
The technology-driven process of business intelligence is transformative for big businesses, but it can be especially crucial for startups. New businesses can use key performance indicators (KPIs) from strong data integration to determine details of their forming customer base: who they are, how they became customers, and how they can be best served by the company's current offerings. This type of BI application can even allow non-expert users to gain knowledge and analyze sales patterns and trends in customer behavior to help guide better decision-making.
The key to using all this internal and external data is in implementing appropriate and effective business intelligence tools to collect what is relevant, apply descriptive analytics to process patterns, and present the findings in easy-to-digest reports and graphics to guide business leaders in their decision-making process. Effective data visualization helps executives both understand and easily share performance metrics and detailed reports.
Leveraging business intelligence software can help build a strong foundation for launching a successful business plan and create workable strategies for strong business performance. Both new and existing companies can optimize BI systems tracking of current performance to inform planning for long-term business growth and stability.
This article addresses the question what is business intelligence and covers the following:
The term business intelligence describes a range of technologies and strategies that include data mining, business analytics, data tools and infrastructure, and data visualization, all of which help decision-makers reach informed data-driven decisions. Other functions of BI include online analytical processing (OLAP), benchmarking, and predictive and prescriptive analysis, all of which aim to identify and create new business opportunities by learning from external and internal data.
Using business intelligence solutions successfully means making better decisions, or, as Tableau.com explains: "In practice, you know you’ve got modern business intelligence when you have a comprehensive view of your organization’s data and use that data to drive change, eliminate inefficiencies, and quickly adapt to market or supply changes."
The evolution of modern business intelligence allows managers to set goals and quickly and effectively answer questions through the automated collection of raw data from operations and sales. This data is harvested and stored in data warehouses where it is accessible for analysis and future forecasting and available for continued analysis, resulting in time and cost-saving benefits for the company.
Microsoft outlines four steps that help define business intelligence, and how BI tools transform data into usable reporting and insights for decision-making:
Tableau.com defines business intelligence not so much as a specific "thing" but more as "an umbrella term that covers the processes and methods of collecting, storing, and analyzing data from business operations or activities to optimize performance." This allows for comprehensive analysis of current business through processes like data mining (using machine learning to discover trends in large datasets), reporting, performance metrics and benchmarking, descriptive analytics, querying, statistical analysis, and data preparation (compiling data sources for data analysis).
Advanced analytics typically have been conducted by teams of statisticians or data scientists who use processes like predictive analytics, big data analytics, and predictive modeling, while BI teams might stick to querying and more straightforward business data analysis. But recent gains in the industry allow for more people to access the information the BI tools can generate, and a new category of self-service business intelligence tools has begun to take shape. These BI solutions are shifting access from IT departments to nontechnical staff, providing intuitive and interactive business intelligence dashboards that transform data into easy-to-understand graphics.
However, there are drawbacks to self-service BI. Making your business users into informal data engineers creates risks of data security issues, inflated licensing or SaaS bills (if there's no centralized control of the tools), and the "too many chefs" syndrome created by so many actors interpreting the same data differently.
To see how business intelligence platforms work, consider these real-world examples from across many industries that benefit from BI software, including digital marketing, advertising, sales, call tracking, and healthcare.
For industries that involve rapid shipping of fresh produce, timing is everything. HelloFresh found that their digital marketing was "time-intensive, manual, and inefficient." The company was able to save 10 to 20 working hours per day by automating their reporting process, simultaneously improving customer service and retention with aggregate analyses of customer behaviors.
The fast-casual restaurant chain Chipotle was having difficulty gaining a unified view of its restaurants, but after modernizing its BI platforms it was able to increase the speed of report delivery from quarterly to monthly for specialized projects, saving thousands of work hours.
The recreational co-op REI solved problems with tracking membership retention rates by analyzing brick-and-mortar vs. online services data. Coca-Cola put customer relationship management data front and center for their sales and delivery team’s use with mobile dashboards, which maximized their operational efficiency.
There are even non-business BI applications. For example, Des Moines High School addresses dropout rates by using predictive analytics for attendance and historical data. The system forecasts significantly more accurately than the school's previous Excel-driven process, enabling teachers and counselors to implement effective preventative measures earlier.
Business analytics and business intelligence are related and share common data collection and analyzing tools, but shouldn't be conflated. They have different purposes and work on securing separate outcomes. Business analytics focuses on the future; it uses historic and current data to make predictions and recommendations to plan for what's next for a company. Business intelligence addresses current business processes and how to make them more efficient now. So, while business intelligence is descriptive—reporting on past and current business data for today's decisions—business analytics is predictive and prescriptive: calculating what will likely happen and what a business should be doing to work toward future success.
There are some excellent master's programs for both business analytics and business intelligence. Butler University offers a Master of Science in Data Analytics in two different concentrations: business analytics and healthcare analytics. Both stress the importance of ethical implications of data-driven business decision-makin and the qualities of written and visual storytelling for reporting results to different audiences.
At the Stevens Institute of Technology, students can pursue a Master of Science in Business Intelligence & Analytics (MSBI&A) as a single degree, utilizing deep learning, artificial intelligence, and predictive analytics in business decisions.
Understanding how to harness and use data in business is critical to success. The vast amount of data available for review holds real answers if you know how to harvest it. Data-driven business decisions are the way forward for identifying market trends, increasing efficiency and profit, tracking performance, optimizing operations, increasing sales, and making customers happy.
Modern business intelligence systems put the power of big data into the hands of employees outside of the IT department. Knowing how to utilize data and effectively illustrate and share it with others on an operations team can be a great lift and accelerator for your career.
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