The Case Against a Quick Win Approach to Predictive Analytics Projects

The Case Against a Quick Win Approach to Predictive Analytics Projects

The Case Against a Quick Win Approach to Predictive Analytics Projects

When beginning a new predictive analytics project, the client often mentions the importance of a “quick win”.  It makes sense to think about delivering fast results, in a limited area, that excites important stakeholders and gains support and funding for more predictive projects.  A great goal.

It’s the implementation of the quick win in a predictive project that can be difficult.  There are at least 2 challenges with using a traditional quick win approach to predictive analytics projects.

Challenge #1: Predicting Something That Doesn’t Get Stakeholders Excited

Almost daily I hear of another predictive project that was limited in scope and allowed people to dip their toe in the predictive water and get a “quick win”.  The problem was the results of the project predicted something stakeholders didn’t care about or couldn’t take action on.

The problem with this quick win is that results of this prediction can lead to questions around – are they also the most expensive schools? Does only a certain economic class of person attend these schools.

Read Also:
Data Science Platforms: What are they? And why are they important?

Using these predictions opens up discussions of economic discrimination, making HR and executives nervous.  They often decide to ignore their newfound ability to predict performance, they don’t implement the prediction and the project doesn’t advance the case for more predictive projects.

The problem with this quick win?  While HR thought the project was a winner, project results got no excitement from business stakeholders and didn’t advance the goal of gaining additional support and resources for more predictive projects.

Executives have seen little or no correlation between engagement and actual business results at their own firm. Imagine trying to sell the VP of Sales on predicting engagement of their sales reps? At the end of the day their employees aren’t hired to be engaged, they are hired to do their job and sell.

In non-analytics projects you’re able to do a pilot with a small amount of people and data. You can focus on a small piece, a sample, something light, less expensive, less risky and less time consuming before you fully commit.

Read Also:
How Sense with Voice will save your sleep with big data

An example would be piloting a piece of software. You could install it for a small number of people and gain their feedback before making a broader commitment.

 



Chief Analytics Officer Spring 2017

2
May
2017
Chief Analytics Officer Spring 2017

15% off with code MP15

Read Also:
Predictive analytics and advertising: the science behind knowing what women (and men) want

Big Data and Analytics for Healthcare Philadelphia

17
May
2017
Big Data and Analytics for Healthcare Philadelphia

$200 off with code DATA200

Read Also:
Data Science Platforms: What are they? And why are they important?

SMX London

23
May
2017
SMX London

10% off with code 7WDATASMX

Read Also:
4 Reasons Your Machine Learning Model is Wrong (and How to Fix It)

Data Science Congress 2017

5
Jun
2017
Data Science Congress 2017

20% off with code 7wdata_DSC2017

Read Also:
The 10 Commandments for data driven leaders

AI Paris

6
Jun
2017
AI Paris

20% off with code AIP17-7WDATA-20

Read Also:
How Sense with Voice will save your sleep with big data
Read Also:
The 10 Commandments for data driven leaders

Leave a Reply

Your email address will not be published. Required fields are marked *