The Top 5 Ways to Sabotage Your Predictive Analytics Project

by Jim Berkowitz on May 20, 2010

Here are several excerpts from an excellent article by Eric King and Thomas Rathburn from The Modeling Agency, (no, not that type of modeling agency, a data modeling agency),  101 Ways to Sabotage Your Predictive Analytics Project, Here Are the Top Five.  Be sure to check out the complete source article for much more discussion on this topic:

The Lure

Business intelligence (BI) practitioners continue to hear about the tremendous return and impact of data mining and predictive analytics applications through reinforcing case studies across industries.

It’s no wonder so many organizations are striving to make their way down the BI development chain to arrive at a practice that offers self-validating prospective insight. These organizations are anxious to uncover and leverage the highly valuable intelligence hidden within their existing operational data.

The Catch

Like any endeavor with rich rewards, there are often numerous risks, barriers and pitfalls that stand in the way. In predictive analytics, those barriers are not in the typical places that a seasoned BI practitioner would expect. Time and again, those new to data mining fall to rookie mistakes.

What Most Suspect is the Biggest Barrier is Not Even in the Top 90

If you guessed “technical complexity, you’re certainly not at all alone; it’s the most common response. But you would be far from correct.
Sure, there are many advanced technologies and complex mathematics that are intimidating to new practitioners. But there are amazing wizards in a wealth of predictive analytics software solutions that effectively manage the neural networks, decision trees, logistic regression, genetic algorithms and other methodologies. Not realizing that modern software is very well equipped to allow general BI practitioners to build very good predictive models causes many to hesitate and suspend entry into the practice.

In reality, it’s not the paralysis of overestimating the tactical implementation, but the rush of underestimating the strategic approach that kills most data mining implementations before they begin. And it’s an expensive oversight because it leaves the practitioners wondering why their models are uninterpretable after having completed the process. Our mini-countdown will reveal the most common pitfalls that cause the majority of data mining projects to fail or fall considerably short of their potential.

Don’t Bring Your Data Warehouse Project Framework to this Game

Unlike a data warehouse design, which is similar to an engineering project, predictive analytics and data mining are a discovery process.

Those who make the effort to educate themselves on the industry-standard approach to predictive analytics are nearly assured to reap residual returns – long before their counterparts who typically rush to acquire a tool and dive headlong into the data.

The Countdown

So, let’s touch on just five of the most critical and popular ways to sabotage an initial data mining implementation:

#5: Approach Predictive Analytics Like an Engineering Project
#4: Gloss Over a Comprehensive Project Assessment
#3: Focus on Methods, Tactics and Optimal Model Performance
#2: Over-Reliance on Software Solutions
#1: Lack of a Solid Project Definition

The most important first step toward properly maneuvering implementation hurdles associated with predictive analytics is to first educate yourself.

Even if you outsource the majority of the implementation, you will be better positioned to interact confidently with your team, understand strategic priorities and appreciate the dynamics of the discovery process. If time is not pressing, there are virtually limitless books on the topic if you have the discipline to really absorb them.

Beware of online education for predictive analytics. When it comes to data mining, online training is effective for a surface orientation or training sample at best. Do not pursue online training if you are seeking true functional knowledge and reinforced skills.

The fastest way to establishing a real-world understanding of data mining is to participate in instructor-led classroom training. The dedicated distraction-free and concentrated time, direct access to the instructor and interaction with related practitioners provides for deeper understanding of the strategies and tactics, practical benefits and lasting effects.

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