Common Mistakes That Can Derail Your Team’s Predictive Analytics Efforts

With high demand for data scientists and the high salaries that they draw, it’s often not practical for organizations to keep them on staff.  Instead, various organizations work to ramp up their prevailing staff’s analytics skills, comprising predictive analytics. But companies need to advance with caution. Predictive analytics is particularly easy to get wrong. Here are the first few “don’ts” your team needs to learn, and their parallel cures.

1. Don’t Fall for Buzzwords — Explain Your Objective

Data Science doesn’t essentially refer to any specific technology, method, or value proposition. Rather, it indicates a culture — one of smart people doing creative things to discover value in their data. It’s essential for everyone to keep this on the top of the mind when learning to work with data.

Under the broad umbrella of data science sits predictive analytics, which provides the most actionable win you can draw from data. In a nutshell, predictive analytics is technology that acquires from experience (data) to forecast the future behavior of people in order to make better decisions. Prediction is the Holy Grail for more efficiently executing mass scale processes in marketing, fraud detection, financial risk, and beyond. Predictive analytics allows your organization to enhance these functions by predicting who’s most likely to click, lie, die, buy, quit their job, commit fraud, or cancel their subscription — and, beyond forecasting people, by also foretelling the most probable outcomes for individual financial instruments and corporate clients. These predictions directly notify the action to take with each individual, e.g., by marketing to those most probably to buy and auditing those most probable to commit fraud.

In their application to these business purposes, predictive analytics and machine learning (ML) are synonyms. Machine learning is the key to prediction. The accretion of patterns or formulas ML derives from the data — recognized as a predictive model — serves to consider an exclusive situation and put odds on the result.

When you start deploying predictive analytics with your team, you’re going on board upon a new sort of value proposition, and so it needs a new type of leadership procedure. You’ll require some team members to become “machine learning professionals” or “predictive analytics managers” — which indicate much more precise skill sets than catch-all “data scientist,” a title that’s shamefaced of vagaries and overhype.

2. Don’t Lead with Software Choice — Team Skills Come First

In 2011, Thomas Davenport was sympathetic enough to defining at the conference I created, Predictive Analytics World. “It’s not about the mathematics — it’s about the people!” he totally bellowed at our enchanted audience, more noisily than I’d ever heard since high school, when educators had to get control of a classroom of teens.

Tom’s astounding tone hit just the right note. Analytics vendors will state you their software is The Solution. But the solution for what? The issue at hand is to enhance your large-scale processes. And the solution is a new technique of business that incorporates machine learning. Thus, a machine learning tool only serves a small part of what must be a universal organizational process.

Rather than following a vendor’s lead, prepare your staff to achieve machine learning incorporation as an enterprise attempt, and then let your staff to define a more informed choice of analytics software during an advanced stage of the project.

Corporate program in data analytics from a reputed organization or institution can help you avoid the common mistakes discussed above. Further, it will also help in filling the data science skill gap in your organization.