It’s increasingly important for businesses to have a clear view of all their data to stay competitive, which is where business intelligence (BI) tools come in. After all, nearly 50% of all businesses already use BI tools, and projections show continued growth in coming years.
But for those who haven’t adopted a tool yet, or are simply looking to learn more, it can be difficult to understand exactly what BI is. We created this complete guide to educate people on what BI is, how it works, and more.
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Business intelligence combines business analytics, data mining, data visualization, data tools and infrastructure, and best practices to help organizations make more data-driven decisions. 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. Modern BI solutions prioritize flexible self-service analysis, governed data on trusted platforms, empowered business users, and speed to insight.
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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, capital letters and all, originally emerged in the 1960s as a system of sharing information across organizations. The term Business Intelligence was coined in 1989, alongside computer models for decision making. These programs developed further, turning data into insights before becoming a specific offering from BI teams with IT-reliant service solutions. This article will serve as an introduction to BI and is the tip of the iceberg.
Businesses and organizations have questions and goals. To answer these questions and track performance against these goals, they gather the necessary data, analyze it, and determine which actions to take to reach their goals.
On the technical side, raw data is collected from business systems. Data is processed and then stored in data warehouses, the cloud, applications, and files. Once it’s stored, users can access the data, starting the analysis process to answer business questions.
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BI platforms also offer data visualization tools, which convert data into charts or graphs, as well as presenting to any key stakeholders or decision-makers.
Much more than a specific “thing, ” business intelligence is an umbrella term that covers the processes and methods of collecting, storing, and analyzing data from business operations or activities to optimize performance. All of these things come together to create a comprehensive view of a business to help people make better, actionable decisions. Over the past few years, business intelligence has evolved to include more processes and activities to help improve performance. These processes include:
Business intelligence includes data analytics and business analytics but uses them only as parts of the whole process. BI helps users draw conclusions from data analysis. Data scientists dig into the specifics of data, using advanced statistics and predictive analytics to discover patterns and forecast future patterns.
What Is Predictive Analytics? Transforming Data Into Future Insights
Data analytics asks, “Why did this happen and what can happen next?” Business intelligence takes those models and algorithms and breaks the results down into actionable language. According to Gartner's IT glossary, “business analytics includes data mining, predictive analytics, applied analytics, and statistics.” In short, organizations conduct business analytics as part of their larger business intelligence strategy.
BI is designed to answer specific queries and provide at-a-glance analysis for decisions or planning. However, companies can use the processes of analytics to continually improve follow-up questions and iteration. Business analytics shouldn’t be a linear process because answering one question will likely lead to follow-up questions and iteration. Rather, think of the process as a cycle of data access, discovery, exploration, and information sharing. This is called the cycle of analytics, a modern term explaining how businesses use analytics to react to changing questions and expectations.
Historically, business intelligence tools were based on a traditional business intelligence model. This was a top-down approach where business intelligence was driven by the IT organization and most, if not all, analytics questions were answered through static reports. This meant that if someone had a follow-up question about the report they received, their request would go to the bottom of the reporting queue and they would have to start the process over again. This led to slow, frustrating reporting cycles, and people weren’t able to leverage current data to make decisions.
How Businesses Benefit From A Data Driven Culture
Traditional business intelligence is still a common approach for regular reporting and answering static queries. However, modern business intelligence is interactive and approachable. While IT departments are still an important part of managing access to data, multiple levels of users can customize dashboards and create reports on little notice. With the proper software, users are empowered to visualize data and answer their own questions.
BI is more than just software—it’s a way to keep a holistic and real-time view of all your relevant business data. Implementing BI offers a myriad of benefits, from better analysis to an increase in competitive advantage. Some of the top business intelligence benefits include:
Many disparate industries have adopted enterprise BI ahead of the curve, including healthcare, information technology, and education. All organizations can use data to transform operations. With as much information as is in this article and available online, it can be difficult to understand the exact capabilities of BI. Real-world examples can help, which is why we build case studies out of our clients’ success stories.
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For example, financial services firm Charles Schwab used business intelligence to see a comprehensive view of all its branches across the United States to understand performance metrics and identify areas of opportunity. Access to a central business intelligence platform allowed Schwab to bring its branch data into one view. Now branch managers can identify clients that may have a change in investment needs. And leadership can track if a region's performance is above or below average and click in to see the branches that are driving that region's performance. This leads to more opportunities for optimization along with better customer service for clients.
Another example is the meal-kit service HelloFresh which automated its reporting processes because its digital marketing team spent far too much time on it each month. With the help of , HelloFresh saved 10 to 20 working hours per day for the team, and made it possible for them to create much more segmented and targeted marketing campaigns.
A BI strategy is your blueprint for success. You’ll need to decide how data is used, gather key roles, and define responsibilities in the initial phases. It may sound simple at a high level; however, starting with business goals is your key to success.
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There are three major types of BI analysis, which cover many different needs and uses. These are predictive analytics, descriptive analytics, and prescriptive analytics.
Predictive analytics takes historical and real-time data and models future outcomes for planning purposes. Descriptive analytics is the process of identifying trends and relationships in data using historical and current data. And prescriptive analytics takes all the relevant data to answer the question, “what should my business do?”
We’ve covered a lot of the pros of BI. But as with any major business decision, implementing BI comes with some difficulties and disadvantages, particularly in the implementation stage.
Top 10 Business Intelligence Trends To Look Out For In 2023
Many self-service business intelligence tools and platforms streamline the analysis process. This makes it easier for people to see and understand their data without the technical know-how to dig into the data themselves. There are many BI platforms available for ad hoc reporting, data visualization, and creating customized dashboards for multiple levels of users. We have outlined our recommendations for evaluating modern BI platforms so you can choose the right one for your organization. One of the more common ways to present business intelligence is through data visualization.
The key to successfully implementing BI is to choose the right platform for the job. In choosing your tool, it’s best to keep in mind what key features will be most helpful for your business. Some key features of BI tools include:
Arguably one of the most useful tools in BI are dashboards, which allow complex data to be aggregated and viewed all in one place. These dashboards can have different purposes, such as for complex analysis or stakeholder buy-in. The challenge is building the best dashboard for your needs.
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As the data atmosphere grows and the gathering, storage, and analysis of data becomes more complex, it’s important to consider the relationship between BI and big data. Big data has become a bit of a buzzword in the industry lately, so what exactly is it? Well, data experts define it by “the four Vs”: Volume, velocity, value, and variety. These four define big data and set it apart. In particular, volume is what people normally point to as the main defining factor, as the amount of data is ever-increasing and relatively easy to store for long periods of time.
As you can imagine, this is important for BI as businesses create more and more data by the year, and BI platforms have to keep up with the increasing demands made on them. A good platform will grow with increasing demands. But if not maintained, dashboards and data sources may fall behind as big data
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