AI plays an important role in driving businesses and in helping organizations make the most of business intelligence techniques. These BI techniques allow scientists to analyze data so their companies can make insightful decisions. There is an acute shortage of data scientists today and companies are scrambling to fill an ever-increasing gap.
One way they fill the gap is by enrolling their own employees in an online AI training course↗. With courses like this, it becomes easy for employees to learn the principles of AI while they work and companies can provide employees opportunities to upskill and learn how to work with data.
Business Intelligence has advanced over the years and involves three basic types of analytics:
Descriptive analytics
Basic BI systems are simply reporting engines that result in descriptive analytics. Descriptive analytics sums up business operations data by dividing data into smaller bits of manageable insights. With this type of analytics, you can study past behavior and determine what steps you need to take.
For example, a company can identify why its products aren’t selling by isolating customer behavior in the past and determine what steps need to be taken to fix the problem.
Predictive analytics
As data multiplies, BI systems have started integrating simple predictive analytics. Predictive analytics use your existing data to predict probable events that could take place in the future. For example, companies that use predictive analysis would be able to determine when people are likely to buy more from them (food companies could see a boost during the weekend) and adapt their strategy to enhance sales for the rest of the week. Predictive analytics plays a prominent role in deciding the future trajectory of your data which helps businesses to map out their business strategy.
Prescriptive analytics
As BI systems develop, prescriptive analytics is likely to come in. Prescriptive analytics works by analyzing data and recommending the next steps of action a company can take to enhance their business processes, ad targeting, marketing efforts and other business operations.
If you are a shopper at Amazon or watch Netflix, you would have been privy to these principles. These platforms check customer preferences based on their past behavior and use this information to recommend products or movies.
The journey from prescription to automation
With the addition of predictive and prescriptive analytics, BI systems have matured from descriptive reporting engines. As of now, BI remains a passive decision support system and humans are the final decision-makers in any event. This works fine in the case of traditional analytics as it relies on human experience and intelligence. Sometimes, there are contradictions between human decisions and analytics. In the battle between analytics and human experience, heavy computing wins hands down.
Copying human brain processes
Parallel computing infrastructures use graphics processing units. Here, the role of data scientists becomes critical as they get opportunities to use sophisticated models.
These models are designed to mimic the human brain - the way it works like deep learning networks that contain neurons in hidden layers and recurrent neural networks that copy the memory effects.
Understanding AI
Machine learning algorithms improve AI models that lead to those results that could previously only be achieved by humans. Modern analytics can even outperform human knowledge by leveraging data from various human experts. Moreover, this can cut out human decision making from the loop. In short, decision automating and its proper execution are the essences of AI.
Why is AI a need for BI?
While the term business intelligence has been around for a while, using artificial intelligence to improve business intelligence↗ is a brand new concept. For those who are unclear about how AI helps, here a few points.
Big Data growth
Big Data is proliferating. Tech giants are investing heavily in Big Data and are recognizing its importance in delivering substantial insights. This has to lead to an increased demand for data scientists. These data scientists translate figures into meaningful data.
Lack of Experts
McKinsey revealed that there are a shortage 190,000 people with analytical skills in the United States. Moreover, about 1.5 million analysts are required worldwide who can use data to make decisions. It can be expensive to assign data analysts every department. With Al, automation becomes easier and companies don’t have to hunt for data scientists to fill the growing gap between supply and demand.
Real-Time Analytics
With an exponential growth of big data, decision making without data has become impossible. However, with the help of natural language generation and machine learning, real-time data analysis becomes easy to perform with a single click. New data is highly valuable when it can be analyzed and used in real time.
AI’s future
Artificial Intelligence helps businesses by predicting future events and consequences of earlier decisions. However, AI and ML have a long way to go when it comes to empowering business intelligence. Moreover, innovative business dashboards advance AI, which in turn, empower business models. This has lifted us from old practices when we had to manually dig deep into data to unveil new trends.
