Businesses everywhere know that predictive analytics can help them improve outcomes like customer churn, machine downtime, supply chain delay, and more.
But predictive analytics isn’t easy. Many businesses struggle to get their projects off the ground and realize the big gains they know are possible.
Why? One of the biggest barriers standing in their way is predictive modeling. With status quo predictive analytics tools, you need data skills, complex software, and time to build and update predictive models.
But with Emcien’s automated predictive analytics engine, you can just sit back and let the self-learning software build the predictive model for you. It automates each step of the modeling process – from feature selection, to model generation, and model updates – eliminating data skill and effort, and slashing the time needed.
Step 1: Feature Selection
Feature selection is the process of selecting a subset of relevant features for use in model construction. It’s important to filter out the unnecessary features/variables and redundant data before building a predictive model. Including such data can decrease the accuracy of your predictions, cause unnecessary data movement, slow the process with unnecessary transformations and longer model-building time, and strain finite computing power and bandwidth.
Not all predictive analytics tools support feature selection. And tools that do enable feature selection need a data specialist that’s familiar with complex analytics software to identify unnecessary data and remove the right columns (ex: run the right function in R). The reality is that status quo predictive analytics tools require data and software knowledge, and time, to do feature selection.
Emcien’s automated predictive analytics engine both identifies and removes all unnecessary features for you. With Emcien, feature selection is a fully automated process performed entirely by the software in minutes. As a result, you don’t need to manually run functions, remove features, or perform other tasks. In fact, no expertise or human intervention of any kind is required so your data specialists can focus on other high-impact projects to move the business forward.
Step 2: Model Generation
You also want to use a predictive model that produces high-accuracy predictions and doesn’t take a long time to build. And, the model should be explainable and have transparency. In other words, it should reveal, in understandable terms, how it arrived at each prediction so users can understand the rationale and trust each prediction enough to take confident action.
But most predictive analytics tools are only helpful if you have enough data science expertise to know the right model to choose. And enough familiarity with the sophisticated toolset at hand to build the model. Or, you can let the software build and test a variety of models and identify the one with highest accuracy, but that process takes hours or days depending on your data set. And when you’re done, the predictive model you build will be a black box that people can’t see into, understand, or trust.
Emcien builds the model in a completely different way. Our software automatically generates an explainable, high-accuracy model for you in just minutes, so you don’t have to select a model, or have data or software skills, or set aside a significant build and test time.
How does it work? Emcien’s software converts your data to a graph representation, similar to Google. It then uses machine learning to automatically generate the predictive model.The model is comprised of a set of predictive rules, which is an easy-to-read form that uses plain-English so anyone can understand it.
For example, “customers who have purchased annual contracts, and have monthly bills overages, and have tenure of less than 2 years, have a 90% likelihood of churning” is a predictive rule. By using a rule-based prediction model, the software helps the data to speak for itself, exposing the patterns that predict an outcome in a way that’s understandable.
Step 3: Model Updates
Real-world data is continually changing. So predictive models must be routinely updated to maintain their high accuracy. As a result, while the initial model is built just once, the re-building of the model happens many times throughout the year, with frequency depending on the speed of the data and the business context.
When model building is a manual process that requires data science skills, model maintenance can become burdensome, if not unmanageable. This is a critical point of failure for manual data modeling systems.
Emcien is a fully automated process that works continuously. It can re-build your predictive model for you as often as you’d like, on a set cadence or even when the software detects an accuracy drop. And the re-build itself is just as easy and quick as the original build.
The Bottom Line
To succeed in the data-driven economy, companies must leverage predictive analytics. But the modeling step of the prediction process is a huge barrier, so it’s important that businesses automate it.
When you automate the modeling process, you no longer need to rely on data skills and complex software expertise, or set aside a lot of time.
With Emcien, feature selection, model generation, and model updating are quick tasks performed entirely by your autonomous software with no human intervention.