An Introduction to Predictive Analytics

Predictive Analytics

Companies have tons of data. Business experts are constantly trying to analyze it without extracting all its value. In this context, analyzing data just for the sake of analyzing it serves no purpose other than to get information about what happened but for what purpose. If you don’t extract the value of that data and turn it into something useful for business, it doesn’t make sense at all. But how do you extract the real value from this data? How do you deduce future behaviors from all this data and get a real impact on your business? That’s exactly what predictive analytics are all about. 

Predictive Analytics are key to anticipate the future and make better decisions. Companies need to go further than simple Descriptive Analytics and Business Intelligence to get fast actionable insights. Empowering  business experts with AI-based tools is now essential to delivering better insights and accelerating business.

From Description to Prediction

There are currently four levels of analytics: describe, diagnose, predict and prescribe which is the very advanced level of analytics in business. Let’s focus on Predictive Analytics because usually companies get stuck at the first level of analytics: Descriptive Analytics.

Predictive Analytics are the most recent evolution of the Analytics revolution that was triggered by the emergence of data:

  • First came Descriptive Analytics, that can be defined as “what happened” and provides a view of the past events. This is what Big Data is all about, gathering data from the past and trying to untangle it.
  • Then, Diagnostic Analytics is where we get to the “why”. From an observation, we try to understand why. Why is this graph up or down, what are the reasons behind these results? 
  • Finally, we have Predictive Analytics which is “what will happen”. It relies on Descriptive Analytics and Diagnostic Analytics to fuel its insights. It then uses Artificial Intelligence techniques such as Machine Learning to predict future outcomes.

To sum it up, Predictive Analytics does with data what humans do with past experiences: learning and anticipating what will happen. It just does it better by finding correlations between data and expressing it through mathematical models and formulas.

In business, we are always looking at the future by building plans over the upcoming years. Anticipating future events is thus crucial to business success. However, as data becomes more accessible and Predictive Analytics get more popular, companies are still stuck in the “Big Data” mentality. They either consider they don’t have enough data for AI, or they throw a lot of money and time on a single Artificial Intelligence project. In the end, it’s a losing game for Business experts who get lost within their data and don’t get the insights they need.

Predictive Analytics for Small Data

There is a solution though – and it’s Small Data. 

Business experts comprehend Small Data naturally and the good news is that they have a ton of Small Data at their hands. This is why building Predictive Analytics on top of Small Data makes sense to bring them fast actionable insights and make sure they will get understandable results.

Using an Explainable AI algorithm designed for Small Data just like ours makes sense. Our algorithm, ZGP, creates predictive models that highlight the key reasons why business outcomes happen. ZGP combines all the techniques to provide short, understandable predictive models that can be used for simulation and help the decision-making process, based on Small Data – starting at 300-row, 15-column tabular datasets. By being transparent and explainable, we make Predictive Analytics easier to understand for business experts. Furthermore, we allow business experts to test different scenarios, assess options and make the right decisions.

Here is a concrete example: a MyDataModels customer designs paints for the Defence & Space industry. These paints must not ignite for at least 15 minutes when subjected to high heat so that the passengers can leave safely. The painting company has been active for 40 years and has labeled data for 500 real-life tests of formulas that their employees conducted. The main issue is that these tests are expensive and time consuming, with unsure results.

Thanks to their use of MyDataModels Decision Intelligence Platform for Small Data, they were able to identify the key chemical elements for building new paints in a few days with an accuracy of 86%. Even better, they can now create combination simulations virtually to determine first what would be the 3 best options and test them in real-life instead of testing 50+ combinations. With this solution, not only they reduce their cost, but they also accelerated their go-to-market.

This is our core belief at MyDataModels and the purpose of our Decision Intelligence Platforms. We are looking forward to improving business experts decision-making, so contact us now for a chat about your use cases!


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