Monthly Archives: October, 2014

Starting a Predictive Analytics Initiative

Starting something new is hard.  An exercise regimen, a diet, studying for certification exams.  Getting to the finish and attaining the desired goal is even harder.  Completing a predictive analytics initiative is no different and the first one is particularly hard.

You’ve spent time on research, reading and investigation, and you see the upside.  You know there can be a high return on investment, and a successful outcome can change the culture of your organization.  But you’re a DBA, a BI practitioner or analyst and predictive analytics is new to you and your company.  You want to ensure success but how do you go about it to make sure it works?

There are characteristics that successful initiatives have in common, and the ones listed here are what I’ve seen lead to success.

Tightly couple the team with BI

The predictive analytics initiative should be part of the BI team, or work very closely with it.  Why?  The BI team knows where the data is, how to get it, understands its quality, and has already acquired much of it.  There will be some data needed that BI people don’t care about, but 80% of it will overlap.

Hire a Data Scientist or assign the role

Make someone the data scientist, either by title or role.  You’ll have to decide whether to bring them in from outside the organization, or assign someone with the essential skills, but make sure the role is occupied.

Buy, Open Source, or use what you own

Don’t get hung up on which tools to use.  There are many good tools out there and you probably already own some of them, so use what you know and have.  Do you have to use R to be successful?  No, although much of what you read makes it sound like you do.  But also consider which open source tools may help augment your development suite.

Choose a Focused Goal

Make the first initiative you pursue focused on trying to solve a single problem.  This is true of most of BI development.  We shouldn’t build a whole data warehouse in one shot, don’t try to solve every predictive problem out there.  And choose one management will get behind, like churn or upselling.

Give it Time

To get good results requires time.  It takes anywhere from 4-12 months to put a solutions into production.

All the parts that go into a successful initiative will be covered at my pre-con at the PASS Summit titled Predictive Analytics in the Enterprise.

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