BI is Dead. Long Live Advanced Analytics?

How companies utilize the vast amounts of data available to them can be a key factor in the success or failure of any firm today. With so much information available on customers, employees, competitors and the wider market, it's vital companies are able to sort through this and derive valuable insight to improve performance and boost revenue.

But old-fashioned ways of managing this - in particular traditional business intelligence (BI) approaches to data analysis - are no longer good enough as these tools can't keep pace with the volume of data that today's enterprises have available. They also find it difficult to manage the increasing variety of data sources, especially unstructured data, and can't give results quickly enough to meet the real-time demands of today's companies.

So if business intelligence is dead, what's replacing it? For many enterprises, the answer is advanced analytics.

What is advanced analytics?

Advanced analytics is somewhat of a catch-all term used to describe a variety of techniques and technologies to help businesses drill deeper into their data. They're used to discover patterns, solve challenging problems or make more accurate predictions that businesses can use to improve their decision making and react more quickly to a changing environment.

Technologies such as complex algorithms, artificial intelligence, machine learning and automation can all be employed as part of an advanced analytics solution. These work together to go beyond traditional BI solutions and deliver much more accurate and targeted answers to the key questions enterprises will have.

What's the difference between advanced analytics and business intelligence?

When considering advanced analytics vs business intelligence, it may seem at first glance that there are many similarities between the two practices, with advanced analytics merely being a modernization and refinement of existing ways of dealing with data. But this is far from the case.

Advanced analytics isn't just about more modern technologies. It's also a major shift in the way businesses think about their data, how they interact with it and what results they expect to see.

As a result, the differences between advanced analytics and BI can be broken down into a few key categories:

Overall purpose and insights

The main aim of BI is to provide information on what’s happened and why. This hindsight offers visibility into performance and enables users to draw inferences about the future, but it's still primarily backward-looking. Advanced analytics, on the other hand, offers foresight. It can anticipate future trends or problems and lets you move proactively to take advantage or head off issues before they arise. While BI answers the questions 'what happened?', advanced analytics lets you ask what will happen.

Methods of reporting and monitoring

Traditional BI involves the use of reporting and monitoring tools and provides a highly structured approach to data analytics, using the likes of dashboards and scorecards. Advanced analytics let you dive much deeper into structured and unstructured data, using data mining, statistical analysis, simulations, machine learning and decision trees, among others. It means more expertise is often needed to ask the right questions and interpret the results - which is why data scientists are in such high demand - but you can get much more detailed answers that look to solve key problems.

The utilization of data

The data utilized in these processes also differs. BI usually relies on structured data, though it can handle some unstructured data, whereas advanced analytics opens up every piece of information you possess, regardless of form or structure. This can include social media posts, images, videos, while analytics tools can also manage high-speed, high-volume, and complex data from a variety of sources.

The benefits of a strong BI solution

Advanced analytics can therefore set your business up much better for the future by allowing you to take a forward-thinking approach that’s less concerned about what’s happened in the past and more focused on what will happen in the future.

It also allows you to pose a range of questions and run simulations to test your ideas before putting them into practice. For example, it could provide information on the impact of changing your pricing structure, or whether increasing the amount of money you spend on marketing will offer a good ROI in the form of new customers.

On an operational level, it can help you identify which areas of the business are performing above or below average and pinpoint the reasons behind this. A BI platform can show you if a certain location is performing differently, for example, but you may be left to intuit the why for yourself. Advanced analytics, however, can look in much more detail at the hidden relationships between various factors to spot patterns and give you clear answers.

Advanced big data analytics can help almost every part of a business become more proactive and make better decisions. Whether it's improved targeting for sales and marketing campaigns, developing personalized customer experiences, managing supply chains or identifying potential new markets, these technologies and methods are vital to driving performance and keeping ahead of competitors who are using more traditional intelligence practices.