It’s like a different kind of writer’s block. You’re sitting at the keyboard, ready to try your hand at building a predictive model. But everything comes to a crashing halt: How exactly should the model be set up so it generates the needed answers? What are the key elements that must be included, and what’s the relevant time frame?

Defining the right predictive model to build might seem like an easy first step toward implementing predictive AI. But surprisingly, it’s a stumbling block for many who are new to the process.

While you might have a general idea of what you want to predict — say, which customers are likely to churn — it often turns out to be much harder to precisely identify the group of customers you want to analyze and which data is relevant.

So how do you get this process unstuck and make progress on your model?

There’s good news: Pecan’s Predictive GenAI takes all the mystery and confusion out of this step of predictive modeling.

Let’s explore what this process usually looks like in a traditional predictive modeling workflow — and how Pecan demystifies it and removes this obstacle to success.

In the traditional data science lifecycle, the first — and often toughest — step is translating a business need into a predictive modeling project.

Typically, this step requires in-depth, prolonged conversations between business leaders and data professionals. They have to build a common understanding of the problem they want to solve together. They need to agree on a shared language for describing the problem.

For example, what is “customer churn,” exactly? Is it when a customer completely ends their relationship with the company? Or is it when their activity falls below a certain threshold?

And even once they have a common understanding, there’s another tricky task: translating these ideas into the data that’s available for modeling. Which customers are of interest for churn prediction? The ones who subscribed at any time, or maybe only those who subscribed in the last month?

What sounded like a simple question is actually pretty complex. There are many ways to define every element of the problem, and each one leads to a different predictive model.

Fortunately, there’s now an efficient way to get through this process of translating a business need into a specific question that a predictive model can answer. As you can see below, Pecan helps automate every step of your predictive modeling journey, including that critical first step on the left of “defining a business need and desired action.” But instead of those long, drawn-out conversations between data professionals and business stakeholders, we take a different approach to articulating the main challenge you want to solve.

At Pecan, we call this the predictive question. Crafting an effective predictive question is the foundation of a good predictive model. It’s the north star by which we navigate our existing data, our business destination, and how we are going to get there.

A good predictive question bridges the gap between present knowledge and future insights. Unlike BI (Business Intelligence) questions, which look backward to analyze historical data and inform current decisions, predictive questions propel us forward, aiming to unveil what lies ahead.

A well-formed predictive question doesn't just seek to understand the "what" or "how much" but is intricately designed to predict "what will happen next?"

Let’s see how we can build an effective predictive question that will lead us to a useful predictive model.

An excellent predictive question is specific, granular, and focused on forecasting future outcomes based on historical data patterns.

For example, a classic predictive question might be, "Which active customers are likely to churn in the next 30 days?" or “Which patient is most likely to become diabetic?”

These questions are forward-looking, grounded in historical data, and seek a predictive insight that can directly inform action.

So there are two ways to arrive at a good predictive question. One is the more time-consuming, challenging process of manually defining the question — and the other is how Pecan does it!

Log in to Pecan, precisely answer our Predictive Chat’s questions, and watch as it crafts a state-of-the-art predictive question for you!

We at Pecan are well acquainted with the challenges of the “manual way” of defining predictive questions. For that reason, we’ve built the Predictive Chat to guide you to an effective, well-defined predictive question that includes all the necessary elements described above. Each of those elements will translate directly into your predictive model by defining the data used to train it.

Even with a tool like the Predictive Chat to guide you, it can still sometimes be a bit mind-blowing to transition from doing historical analysis in BI tools to predictive modeling with AI. Let’s dig into some of the key differences to help you get re-oriented in this new approach.

BI questions are retrospective, asking "What happened and how did we get here?" They dissect past performance to inform present understanding.

Predictive questions, however, are future-oriented, asking, "What will happen, and how can we get to where we want to be?" They leverage patterns in past data to forecast future outcomes.

While BI questions provide valuable insights into past trends and performance metrics, predictive questions are inherently actionable. They provide specific forecasts that can inform proactive strategies or decisions.

For example, predicting customer churn enables targeted retention strategies before the churn occurs, and predicting diabetes diagnoses allows us to take preventive measures to keep patients healthy.

BI questions rely on traditional statistical analysis and reporting tools to interpret historical data. Usually, their answers include 1 to 5 key variables (aka the independent variables) that affect the outcome (the dependent variable). Our human minds are capable of figuring out these connections (to some extent).

However, predictive questions often require more sophisticated analytical tools and methods, such as machine learning models, to forecast future outcomes. They take into account dozens (if not hundreds) of dimensions and require complex algorithms to identify predictive patterns That’s something that our brains, extraordinary as they are, just can’t build.

BI analysis is more flexible with the data's historical scope and depth. You may not need a lot of data to draw initial conclusions, and you can come up with hypotheses and speculations to try to explain any unusual cases.

However, predictive analytics typically requires a lot of structured, cleaned, and relevant historical data to model the future accurately. This includes identifying and selecting the right variables (features) that influence the outcome being predicted. Humans can’t do this task easily (yes, even data scientists). The good news is that Pecan does all this data preparation and feature selection for you.

Predictive questions open the gateway to foresight, enabling us to anticipate changes, adapt strategies, and make informed decisions about the future. Unlike BI questions that illuminate the path walked, predictive questions light up the road ahead.

However, as you’ve seen here, it’s not always so easy to define the right predictive question and then move forward to the right predictive model. That’s why we’re making this step as easy as a natural-language chat. With Pecan, making the leap into AI from your BI experience is easy and streamlined.

By understanding the nuances that distinguish predictive questions from BI inquiries, organizations can harness the full power of their data, turning insights into foresight and opportunities into outcomes.

Try out our Predictive Chat now and refine your own predictive question!