Data Scientists at eCapital Advisors work with clients to deliver and implement value-added solutions. As a result, our clients unlock critical business insights and make organizational decisions based on data. Sometimes we can share quick business wins along the way.

In this article, I will outline two algorithms our data analytics experts recommend as quick business wins to help clients secure a competitive edge. The following statistical models can create high-performance, robust opportunities. You will gain an appreciation for those opportunities as well as the limitations each algorithm provides by understanding these machine learning basics.

Quick Business Win — Anomaly Detection

Anomaly detection is a statistical approach to pinpointing outliers in your dataset. Suppose your business is stalling at specific point-of-sales locations. In that case, anomaly detection can narrow down which areas behave differently from others so that you can determine why. It is essential to identify anomalies so you can decide how you want to handle them when generating future predictions. Additionally, you can analyze anomalies to see why and how to make them happen—or not happen—again.

When you analyze your historical data, a simple way to detect anomalies is by using the famous “box-and-whisker” plot method invented by the renowned mathematician John Tukey. I will follow this method below:

Why Detect Anomalies?

Anomaly Detection can help with multiple perspectives of the business. For example, detecting outliers helps data analysts identify patterns in their data. This is particularly true if they visually highlight them. Data plots are a great way to visualize outliers and understand how much they deviate from the rest of your data. This can help the business determine why the outliers are occurring. Did a campaign help the success of the product? Was a feature added to a product that clients don’t like?

Anomaly detection can also help businesses create more accurate forecasts. In finding outliers, first, the team generates a forecast model. Once the team collects the actuals for that period, they analyze the outliers. Those actuals—with the anomalies—are then put into the next forecast. Finally, we observe how those actuals impact the rest of the trend cycle. As a result, future forecasts will capture similar data points and be more accurate.

Quick Business Win —Predictive Modeling

A predictive modeling algorithm is a model that takes historical data to forecast future data. Our predictive model calculates an upper and lower bound for future predictions; we can be 95% confident that the true average forecast will lie within this range. This technique helps forecasts mediate uncertainty.

Predictive modeling can predict values like revenue, working capital, staffing, and more. If you have the data, we can predict it. We accomplish predictive modeling using a standardized workflow:

Why use Predictive Modeling?

Predictions using algorithms help you make more accurate predictions. When the algorithm is inaccurate, the model can provide feedback on why. On top of that, a predictive forecast can be used over any time period: daily, monthly, quarterly, yearly, etc. This gives you the advantage of having a prediction for tomorrow rather than waiting a month to know if your predictions are accurate. This alone makes using machine learning forecasts advantageous.

Learn More

Our expert data scientists are always prepared to help. We are experts at anomaly detection, predictive modeling, and many other models. With us, you will always be part of the solution. We work diligently to pursue data science solutions that are useable and understandable, so they are also actionable. Whether you are new to data science or advanced, contact us to learn how we can elevate your company.