Ever play with a Magic 8 Ball? Back in the day, you could ask it any question and get an answer in just a few seconds. And if you didn't like its response, you could just shake it again for a new prediction. So simple, so satisfying.
Today's HR teams and businesses obviously need more reliable ways of predicting outcomes and forecasting results than a Magic 8 Ball. But while forecasting and predicting sound similar, they're actually two different problem-solving techniques. Below, we'll go over both and explain what they're best suited for.
What is HR Forecasting?
At first glance, the Magic 8 ball "predicts" or "forecasts" an answer to your question. This is not how forecasting works (at least, for successful companies or HR departments).
Instead, HR forecasting is a process of predicting or estimating future events based on past and present data and— most commonly—by analysis of trends. "Guessing" doesn't cut it.
For example, we could use predictive forecasting to discover how many customer calls Phil, our product evangelist, is likely to receive in the next day. Or how many product demos he'll lead over the next week. The data from previous years is already available in our CRM, and it can help us accurately predict and anticipate future sales and marketing events where Phil may be needed.
A forecast, unlike a prediction, must have logic to it. It must be defendable. This logic is what differentiates it from the Magic 8 ball's lucky guess. After all, even a broken watch is right two times a day.
What is Predictive Analytics?
Predictive analytics is the practice of extracting information from existing data sets in order to determine patterns and trends that could potentially predict future outcomes. It doesn't tell you what will happen in the future, but rather, what might happen.
For example, predictive analytics could help identify customers who are likely to purchase our new One AI software over the next 90 days.
To do so, we could indicate a desired outcome (a purchase of our People Analytics software solution) and work backwards to identify traits in customer data that have previously indicated they are ready to make a purchase soon. (For example, they might have the decision-making authority on their People Analytics team, have an established budget for the project, completed a demo, and found Phil likeable and helpful.) Predictive modeling and analytics would run the data and establish which of these factors actually contributed to the sale.
Maybe we'd find out Phil's likability didn't matter because the software was so helpful that customers found value in it anyway. Either way, predictive analytics and predictive modeling would review the data and help us figure that out — a far cry from our Magic 8 ball.
Which Do You Need?
Which do you need—forecasting or predictive analytics?
The short answer: probably both.
Forecasting helps you project trends—like how headcount or attrition rates might shift over time. It’s great for setting expectations and planning ahead.
Predictive analytics, on the other hand, digs into the "why" behind those trends. It identifies patterns and flags specific risks or opportunities—like which employees are most likely to leave or what factors drive engagement.
Together, they give you both the big picture and the specific actions to take.
Ready to explore predictive analytics?
We can help you figure it out and get started. Connect with us.
Forecasting vs. Predictive Analysis: Other Relevant Terms
Machine Learning - machine learning is a branch of artificial intelligence (ai) where computers learn to act and adapt to new data without being programmed to do so. The computer is able to act independently of human interaction. Read Machine Learning Blog.
Data Science - data science is the study of big data that seeks to extract meaningful knowledge and insights from large amounts of complex data in various forms.
Data Mining - data mining is the process of discovering patterns in large data sets.
Big Data - big data is another term for a data set that's too large or complex for traditional data-processing software. Learn about our data warehouse.
Predictive Modeling - Predictive modeling is a form of artificial intelligence that uses data mining and probability to forecast or estimate more granular, specific outcomes. Learn more about predictive analytics.
Descriptive Analytics - Descriptive analytics is a type of post-mortem analysis in that it looks at past performance. It evaluates that performance by mining historical data to look for the reasons behind previous successes and failures.
Prescriptive Analytics - prescriptive analytics is an area of business analytics dedicated to finding the potential best course of action for a given situation.
Data Analytics - plain and simple, data analytics is the science of inspecting, cleansing, transforming, and modeling data in order to draw insights from raw information sources.
People Analytics - All these elements are important for people analytics. Need basics? Learn more about People Analytics.
About One Model
One Model’s People Analytics solutions help thriving companies make consistently great talent decisions at all levels of the organization. Large and rapidly-growing companies rely on our People Analytics platform and One AI because they takes all of the heavy lifting out of data extraction, cleansing, modeling, analytics, and reporting of enterprise workforce data.
