IoT Agenda

Oct 26 2018   12:59PM GMT

End user-enabled predictive analytics is your next competitive advantage

Matt Schnugg Profile: Matt Schnugg

Tags:
Data analysis
Data Analytics
Internet of Things
iot
IoT analytics
IoT data
Predictive Analytics

Every day, your enterprise continues to collect more and more data. With the explosion of sensors monitoring performance of every important piece of every asset that you own, simply harnessing this data is a tall order. However, data alone won’t give you a competitive advantage — but having a strategy to efficiently cleanse, manage and then subsequently build and deploy predictive analytics with it will.

So, how do you build an accurate and successful predictive analytics pipeline? It’s all about knowing what matters to your business and the best outcomes that you can drive as a collective team.

Your mantra: Focus on output, not simply input

Too often building a predictive analytic begins by throwing a bunch of smart people and a ton of compute at a data set in hopes of finding a killer insight. In reality, the output of this work usually ends in insights without relevant context or actionable guidance for key stakeholders to actually improve their performance or change their behavior.

As such, we’ve found that the best way to drive actionable insights across all analytics (and particularly predictive/prescriptive analytics) is to understand the outcomes that you want to enable. After all, the goal of any analytic is to create a significant, constructive impact — and that can’t happen if your guidance isn’t relevant to your stakeholder.

One straightforward way to maintain this focus is to ask the following questions, throughout the analytic build process:

  • What is challenging the stakeholders of this analytic?
  • What information would improve their ability to do their job?
  • How can the analytic enable functionality beyond what is currently being provided?

In asking those questions, you’ll put yourself in the shoes of your stakeholder and be on your way to creating models with lasting and significant impacts. And while this doesn’t necessarily replace the EDA (exploratory data analysis) exercise, it will certainly reduce the amount of breadth required in that process.

A simple way to ensure the appropriate level of engagement from your operational stakeholders and domain in this process is to embed them on the scrum teams responsible for delivering these models. This will ensure native engagement with an end-user perspective, ultimately “codifying” important domain expertise into the model itself.

So with this outcome-based focus in mind, how do you get started? Every journey is a bit different, but in my next piece, I’ll discuss the steps we’ve found to be universal in building predictive analytics to make the most out of your data.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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