business intelligence

What is Business Intelligence?

Data is everywhere, and vast amounts of it are generated every day. Even the smallest businesses generate their share. Business intelligence helps people in business make use of that data—to improve efficiency and productivity, and make better business decisions.

Defining Business Intelligence

The term “business intelligence” was first used in the nineteenth century, but it didn’t become a major buzzword until the middle of the twentieth, when it was used to describe the relationships between facts, actions, and business goals. It’s only fairly recently that the term has been used more widely—perhaps in response to the ever-increasing amount of data and information that businesses generate in day-to-day and long-term operations.

Now, the term refers more specifically not to the information that businesses generate, but to the wide array of tools, systems, and methods that are used to gather, store, access, and analyse that information. Business intelligence has developed in the last few decades more or less out of necessity, as a way of turning vast amounts of data into accessible information that businesses can use to achieve their goals.

Why is Business Intelligence Important?

Like most things in life, business is increasingly complicated and compartmentalised. Even small and medium businesses are more complex than they used to be. Businesses of all sizes generate an exponentially larger volume of information than they used to. And simply put, more data means more complexity, and the need for increasingly sophisticated tools for the management of that data.

Business intelligence provides those tools. It helps people make better business decisions, by putting all of that data into its proper context. Wherever people need to make decisions, business intelligence can help, by contextualising the data they use to make those decisions. For instance, data intelligence can help people:

  • Identify market trends and consumer preferences.
  • Analyse customer behaviour.
  • Optimise production methods and operations.
  • Predict the likely success of a new product.
  • Track company performance.
  • Make comparisons with competing organisations.
  • Identify problems and their sources.

Intelligence Versus Analytics

The term business intelligence is often confused for business analytics. In fact, these two terms aren’t interchangeable, and analytics is actually a subset of business intelligence.

The most important difference between the two is that business intelligence is descriptive, while analytics is predictive. Business intelligence is all about what’s happening right now and how it relates what happened previously. Business analytics, on the other hand, is about what’s going to happen. Analytics uses data from the past and present to make predictions about what will happen in the future. Intelligence is about what happened today, this week, this month, while analytics predicts what will happen next month, next year, and beyond.

Challenges in Business Intelligence

In every business that uses business intelligence—and every business that wants to make better use of intelligence tools—there are barriers to making it work. It’s not necessarily easy to implement business intelligence tools, and many organisations find that they need to change certain processes and practices in order to ease the transition. Some common challenges include:

Low data quality

Poor quality or structure of data is one of the biggest barriers for many organisations. These issues make it more difficult to collate, organise, and search data, which can in turn reduce the accuracy and usefulness of any information generated using that data.

There are potential solutions to these issues; for instance, unstructured data can be improved with the addition of metadata that described the data and adds important context. However, businesses that are serious about using intelligence tools must often make significant changes to data-gathering and processing methods to make sure their data is more robust in the long term.

Poor adoption of BI practices

Another potential issue arises when business intelligence methods are adopted as a replacement for older, outdated practices. In some organisations this results in a poor level of user adoption, with employees more likely to return to legacy tools and methods.

To prevent these kinds of issues, it’s important that organisations offer user training, to ensure that everyone understands the new tools and how to use them. Communication is also key in this instance—if employees understand what business intelligence can do, and how it can help them do their jobs, they’ll be more likely to adopt new tools and methods.

Lack of planning in implementation

Implementing business intelligence isn’t just a one-off project. It’s best considered as a long-term process, in which an organisation continually improves and refines its practices. Having a concrete strategy for implementing—and using—business intelligence tools is critical to ensure that it’s worth the effort.

It’s also important that businesses determine how well those processes actually work for them, so that they can change and improve them over time. So, any organisation that’s considering implementing business intelligence tools should also look at developing processes that help them evaluate the effects of implementation.

Get in touch with ACUTEC today to see how business intelligence can help your business.