Is AI changing business intelligence?

Published August 14, 2019   |   

Businesses are relying on business intelligence more than ever, but how are the recent innovations from AI impacting this sector? As a technology-driven process business intelligence could garner much autonomy from AI if put to use properly.

Of course, business intelligence serves a much higher purpose than handing out numbers to users. Instead, it works to guide high-level management and end-users. Primarily it should guide people through making business decisions. Some have a concern that AI may hinder that decision making by creating such a narrow view of big data analysis. For now, though it seems like AI is helping analytics become more relevant.

Different aspects and function of analytics

Early on in business intelligence, everything was about huge reports that often left more questions than answers. As we are all developing in our ability to work with and understand data, we are moving past simple questions.

Much in the way that broadband and connectivity speed has changed, business intelligence has also changed. Now you can check your speed to see if everything is up to par. ISPs have given their customers the ability to react to information that they can access. While behind the scenes, these ISPs and nearly every other big business are working with proactive analytics.

Business intelligence has changed massively over the years. However, as users gained more access to information and took a reactive approach, companies knew they would have to become more proactive.

Descriptive Analytics

The original form of business intelligence was descriptive analytics. It provides summaries of data and gives vital information regarding what may have happened. Essentially, descriptive analytics describes the environment, source, and state of the data. That doesn’t mean that this type of analytics is not useful. It allows companies to mark their lessons learned and to better adapt to changes in the future.

Predictive Analytics

After descriptive analytics came predictive, which works to predict insights accurately. This form of analytics uses algorithms and statistics-based formulas. Companies often turn to predictive analysis as a form of forecasting future events. The foundation relies on probability and the thought process that external factors will remain unchanged. While that’s not exactly realistic for most businesses, it can help tremendously within a department.

Prescriptive Analytics

Just like a doctor might write you a prescription, your business intelligence software will do the same. Prescriptive analytics works on many levels. First, it analyzes the data. Second, it determines likely pitfalls and issues that arise from trends. Third, it provides advice.

Prescriptive analytics was the first time that business intelligence relied on AI for predictions and remediation. These types of analytics will help high-level management identify critical issues in an extensive value chain process while working with massive volumes of data.

Retail is well-known as the industry that uses predictive analytics most often. However, healthcare is catching up on this trend and beginning to use predictive analytics to respond to different demands throughout the year and in different regions.

The massive jump between predictive and prescriptive analytics changes in many industries. This type of analysis help industries better serves their customers. Predictive analysis can help healthcare professionals avoid negligence issues. It can help insurance companies create more accurate risk assessments.

Understand AI’s influence on business intelligence in your sector

There are a few sectors that are seeing substantial jumps in technology on almost a daily basis. Anyone working in the medical or healthcare industry can attest to the massive changes. However, the trading industry is mostly under the rule of AI technology now. As well as financial services.

If you’re in an industry that has a strong presence of AI, then you should be aware of how it may impact the data you work with daily. Regularly, your data may fluctuate, given both internal and external circumstances. However, business intelligence software often tries to offer insight into how and why these changes happen.

Improved insights for big data

Because of the automation of a massive scale of sequence decisions, analytics is providing more insights than ever. Not only is business intelligence which utilizes AI providing more insights, but it’s providing them in virtually, real-time. As AI integrated software functions with an “adapt and learn” model, it’s able to continually improve its decision making and even cut down on execution times.

Of course, these systems work with big data best. As your company gathers more data, the insights will become more accurate. Additionally, more significant segments of data will allow the AI function of the software to utilize a larger pool of information, reinforcing its decision making.

What to expect from AI regarding business Intelligence?

Although dashboards are useful, they simply aren’t enough anymore. The principles of decision making always have focused on proactive approaches. Until recently, business intelligence wasn’t capable of providing proactive analytics. Now that this is a definite possibility, much top-level management staff should implement the tools and tactics they have at their disposal with insight from big data and more accurate projections.

However, that doesn’t mean that AI is the grand solution to all the stumbling blocks that come with business intelligence. There is an ongoing shortage of individuals that are data experts, and that can cultivate an over-compensated reliance on technology solutions. Companies should evaluate the opportunity to bring in data experts for each department. These experts can help staff and management interpret and act on valuable information from big data collections.

Finally, the most significant change for business intelligence that is coming from AI is the real-time influx of information. Insights are working with minimal lag time as many processes occur with outstanding autonomy.

There’s a lot to expect with predictive analytics. Although it’s not the newest thing on the market, the way that AI is changing is allowing BI to grow as well. It’s vital for the industries that rely on big data handling to understand and prepare for the significant changes coming their way.