What is Data Analytics?
To understand the meaning of Predictive Analytics, let’s describe what Data is first. Data is a collection of facts, information, and observations related to a context. The data can be either structured or unstructured, stored in databases, spreadsheets, files, etc. Data analytics is the science of examining the data to drive conclusions and find answers to particular questions.
Data Analytics can be defined and applied at different levels:
As the above Gartner diagram shows, we can define four levels of analytics:
- Descriptive tells us “What happened?” which is pretty much all of the standard reporting capabilities that we have seen so far in any computerized system. For example, a sales report generated for a customer to understand the transactions for the current year.
- Diagnostic tells us “Why did it happen?” by using advanced techniques such as drill-down, data discovery, data mining and correlations (e.g., BI finds the relationship between data points and helps us to understand Why a specific event has occurred).
- Predictive tells us “What will happen?” by using historical data and an understanding of the past in order to predict the future. This is called supervised learning in AI.
- Prescriptive tells us “What should I do?” based on the information that we could predict using “Predictive” analytics. The system can prescribe the best next action, offer, decision, etc., to the user or it can fully automate the cycle (when possible)
Let’s see what is so special about Predictive Analytics and how it can help your business.
What is Predictive Analytics? Why does it matter?
Data Analytics nowadays is a hot topic, especially with the advancement of AI and accessibility of fast and cheap computing power. Predictive Analytics is a special branch of AI that uses supervised learning, statistical techniques from predictive modelling, machine learning, and data mining to analyze current and historical data in order to make predictions about the future. Predictive Analytics can play a big role in helping you to win the competition.
How? Predictive Analytics can harness the data and create a unique opportunity for organizations.
Real Life Examples
Below are just a few scenarios in which you can use Predictive Analytics to find a new opportunity and take action on it:
- Forecasting sales volume based on last year’s sales history of a product and current orders to ensure you will have always have the right amount of the item in stock. Potentially, you can use a Workflow to automate the whole approval-buy cycle of the purchase.
- Finding the probability of a patient illness based on the history of similar patients in the medical center during the current season, and helping the GP to identify and diagnose illnesses based on the probability of these hypotheses. Potentially, you can use Decision Automation to prescribe the right set of activities, drugs, and regimens for the patients.
- Classifying customers based on buying habits, and sending the right offer to the right customer to boost sales. Potentially, you can automate the entire cycle and let the system send out vouchers, sales offers, etc., using the right groups of products to the correct sets of customers. Then the system monitors the customers behavior again and feeds the results back to the model. This improves the quality of the next offer in order to maximize profitability.
It is important to understand that the value of Predictive Analytics has become a reality. However, it will have positive impact on our day-to-day jobs only if we utilize them and close the loop of the Observe-Orient-Decide-Act (OODA), not just show these on a beautiful dashboard.
These are just few examples and in the next post I will show you how to build a predictive model using FlexRule.
Last updated October 11th, 2019 at 07:27 am, Published January 24th, 2018 at 07:27 am