Machine Learning and Artificial Intelligence: An Aid to Predictive Analysis

Business Intelligence originated almost two decades ago and now it has taken the whole business world by storm. Since very long BI has been undergoing several changes and have morphed itself to evolve as a master for the ever-changing business scenario. Business intelligence has also been meeting ever-increasing demands with more in-depth analysis and increased speed to extract desired outputs. With each new evolution, new use cases were born, and the resulting business value increased.

Today business gurus often consider the progression of Business Intelligence solutions into three main categories:

Descriptive analytics: This deals with the reports based on statistical information. It usually has depictions of what has happened in the past and things related to it.

Predictive analytics: This deals with reports which provide a highly-informed analysis of what is likely to happen in the future based on previous trends discovered by descriptive analytics. In other words, descriptive analysis and predictive analysis are mutually dependent on each other.

Prescriptive analytics This deals with the in-depth analysis of details on what to can expect to happen in the future, and also provide solutions on how to handle it.

Machine learning and AI are largely responsible Predictive and Prescriptive type of analytics.

Moving away from reactive analysis to proactive analysis is the new trend to gain most from any BI solution. It not only offers alerts and real-time insights but also helps stakeholders in making better-informed decisions.

Let’s have a look at the following use cases of proactive analytics that are already in effect.

  • Sketching out a report on products that appeal to potential customers based on the information that they have already acquired or what they are actively searching for over the internet or through retail dashboards.
  • Rearranging operations by recognizing seasonal trends in supply and demand chain.
  • Predicting before time repairs and maintenance for machinery in any mechanical industry.
  • Assisting doctors and surgeons in making faster and more reliable diagnoses through healthcare dashboards.
  • Identifying and acting upon excess product orders that seem unusual for any specific customer.
  • Automating merchandising based on a predictive understanding of potential as well as existing consumers.
  • Proactively addressing impending equipment malfunctions based on real-time data from IoT sensors.
  • Swiftly accelerating the manufacturing and discovery of new medications.
  • Generating a full-fledged maintenance schedule based on historical equipment failures to reduce flight take off delays.

The above-mentioned examples are mere instances of immense capabilities of machine learning in BI to improve business efficiency and deliver more tailor-made consumer experiences. On the other hand, AI can help businesses in the same way to make sense of the overwhelming amount of data that is being delivered.

In any scenario when solutions become more complex, then there is every possibility that companies would require more experienced employees to produce and at the same time effectively manage outputs that would effectively inform decision-makers.

At the same time, there is a challenge for BI providers to stay ahead of the cutting-edge capabilities while driving innovation and provide new functionalities to add value to their businesses without facing any intervention for highly skilled data analysts and data scientists.

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