Business intelligence (BI), in the simplest terms, is the technology-driven process of analyzing business information and making it actionable. BI helps leaders make informed business decisions. It provides a historical, current and predictive view of operations.
While the practice of business intelligence has been around for decades, machine learning is making BI more effective. The symbiotic relationship between machine learning and business intelligence leads to more informed data-driven strategies and tactics being developed.
Below are some ways machine learning is making business intelligence more actionable and effective.
Connect the Dots
Humans are capable of cross-referencing data from various parts of their organization. But that becomes wildly more complicated and inefficient when it’s time to collect and aggregate data from every division of the company.
One of the quickest ways machine learning makes business intelligence more effective is in its ability to link data from every division of a company and rapidly generating a snapshot of all aspects of operations. Machine learning can automate tedious repetitive tasks such as identifying and analyzing copious amounts of data. It leaves more time and effort for analysts to make the data actionable.
360 Degree View
Being able to quickly compile and associate data points from every division of an organization has many benefits, but one of the greatest benefits for businesses is creating a 360 view of the customer. What does this mean? A “360 view” of a customer tells business leaders the whole story about a customer’s relationship with a company. Incorporating machine learning into a database of 360 customer profiles can help to very efficiently give a climate report about customer needs, business needs, and overall more-informed business intelligence.
Before anyone jumps to conclusions: machine learning is definitely not a crystal ball. However, when it comes to wading through and processing huge amounts of data, machine learning is the best tool available. Using past internal data and external data, machine learning can inform a business intelligence strategy to optimize business operations, estimate future sales, or even predict the most affordable time of the week for an executive to leave for a trip.
Recent advances in the relationship between business intelligence and machine learning has been pushing these kinds of predictions to the next level. Forecasting and predicting can now be processed and delivered in a much more accessible way.
Machine learning that integrates natural language processing (NLP) and natural language generation (NLG) makes it easier for managers to spend their time making decisions, rather than spending a day interpreting a complicated set of graphs and figures. How? NLG can help explain myriad graphs and charts by providing automatically generated explanatory text to end users, making their comprehensive needs greatly lessened. Some managers prefer to analyze figures themselves. For those who don’t (or even those without an analytics background), NLG and machine learning provide an opportunity to get a quick, succinct summary of the data being presented and allow the manager to make a decision and move on with their day.
One of the topics within machine learning that has been a bit more cumbersome is the existence and implementation of bots as employees. Most will jump to the conclusion that bots are being used to replace employees, when, in most cases, the bots are being tasked with tedious minutia that workers are often incapable of completing or do not warrant their time. Artificial intelligence bots are able to complete these tasks to supplement employee efforts, not replace them. This makes for a much more efficient workplace with a more streamlined business intelligence system.
Catch Anomalies Quicker
In addition to being able to quickly cross-reference data from many branches of an organization, machine learning can also analyze data as it is collected. Having real-time analysis gives the opportunity for machine learning to catch outlier data points as they are happening and quickly assign an expert to determine the cause of the outlier. This can be particularly useful in issues of cybersecurity.
As we’ve discussed before, artificial intelligence is the future of cybersecurity, and on the most basic level, that all starts with catching and flagging threats quicker. Incorporating machine learning into business intelligence allows the opportunity to quickly feed outlier information to experts for a solution, identify what caused the issue in the first place, but also to use that information to inform future decisions and protocol.
The future of business intelligence is hard to predict because business intelligence evolves every day and also varies greatly based on organization size, type, and mission. However, some of the upcoming trends in artificial intelligence may lend a helpful lens for looking at the future of machine learning in business intelligence. No matter what the future holds, it’s clear in the present that machine learning and business intelligence have a unique (and extremely beneficial) symbiosis that’s allowing business leaders to create more informed strategies and help their businesses run more efficiently.