5 Myths and Misconceptions About Predictive Analytics
As data analytics professionals fully absorbed in our work, sometimes we blind ourselves to the reality that (surprise!) not everyone has embraced the power of predictive analytics—and how predictive modeling can transform your business strategy.
But predictive analytics newcomers—and naysayers—are out there (we see you!), and many are asking the same questions: Is predictive analytics right for my organization? How do I know if we’re ready?
Predictive analytics requires investment: in your data, in infrastructure and technology, and of your time. It’s also an investment in your company, your internal knowledge base, and your future. We may be biased, but we think the investment is well worth it.
But too often clouding an organization’s better judgment about predictive analytics are the myths and misconceptions about it. We’ve heard a few, so we decided to address them once and for all—and hope that it makes the path toward data-driven decision-making a little clearer.
5 Myths About Predictive Analytics:
1. You need a Ph.D. to build predictive models
2. Predictive models take too long to build
3. Predictive models don’t reveal anything new
4. Predictive models are a “black box” and can’t be validated 5. Predictive models replace human judgement
Myth #1: You need a Ph.D. to build predictive models
Wrong! A working knowledge of statistics will certainly help you to better interpret the results of predictive models, but you don’t need ten years’ experience or a doctorate degree to glean insight or utilize the output from a model.
There are software packages with diagnostics that can guide you toward understanding which variables are important, which are not, and why. Knowing your data is just as important as statistical knowledge, and both will serve you well in the long run.
Myth #2: Predictive models take too long to build
Nope! We’ve heard horror stories about a model taking months, even years, to implement. If this has been your experience, sorry but you’re doing it wrong.
The advent of predictive modeling software has made the process incredibly efficient—with most solutions able to turn out models within seconds or minutes. That said (and listen closely), the bulk of time spent will come beforehand, during data clean-up, which will vary by organization, depending on factors such as data accessibility, data structure, and the tools you use to prepare that data for predictive analysis. In any case, it’s time well spent!
See how our data prep solution allows you to build analytic, step-by-step (repeatable) processes with no coding required → click here
Myth #3: Predictive models don’t reveal anything new
Yes and no! Even if you know (or think you know) your data, predictive modeling can still help. A finished predictive model can do one of two things: confirm what you’ve always believed or bring new insights to light.
Around here we call this the “turn or confirm” idea—a model will either turn or confirm the things you thought to be true.
And you know what? Most of the time, predictive models with both turn and confirm. You’ll both validate any anecdotal evidence you might have (or realize some correlations were not as strong as you thought) and you’ll learn new variables or connections that you hadn’t picked up on before.
Myth #4: Predictive models are a “black box” and can’t be validated
That depends. Certainly, there are companies and consultants whose predictive models (built for their clients) fall into a “black box,” limiting the transparency of how the model was scored. In this case, model-building involves sending data to an outside party who analyzes it, then returns to you a series of scores.
This may not seem like a bad thing (and may, in fact, be fine), but until you’ve taken your own data and built your own model, you won’t know how valuable self-service predictive modeling can be. By doing your own predictive modeling, you’ll have control over the variables used, how the model handles any missing or outlying variables, and you’ll be able to glean insight beyond a single set of scores in order to change or monitors specific behaviors going forward.
Learn how our solution gives you the most flexibility and transparency for predictive modeling → click here
Myth #5: Predictive models replace human judgment
As much as we, for one, wish to welcome our new AI overlords and be done with it, the fact is that, no, predictive models were never meant to replace or dismiss the (potentially flawed) judgment or intuition we bring to the process. In fact, 99% of the time, the aim of predictive modeling is to enhance and expand human expertise in data analysis. After all, it takes a human to decide what datasets to consider, and we haven’t seen a single stakeholder meeting that didn’t include actual people digesting data-driven reports and making smarter decisions based on that data. Seeing the future through predictive modeling might even make you superhuman.