How to Make Data Experiments Powerful

Easy Implementation Steps for Analytics Project Management

This article provides two clear examples of how managers can move their projects and organizations to become data-savvy organizations. The fun of this Analytics article is that an example is from an industry that you wouldn’t think of being open to IOT and Big Data!


Experimentation is powerful when it deepens managerial intuition. The first example asks teams to runs lots of tests and to ask how each one impacts the organizations key performance indicators (KPI’s.)  So the team has to look at all the “touchpoints, the task completion, metrics, more deeply than have they had in the past. Managers can quickly test their insights, either validating their thinking or sending them back to think more, then swiftly bring changes to scale.This really changes the team culture and how they look at analytics.


Experimentation is powerful when the organization has unique data. The next example looks at a laundry operations that has a unique set of data and opportunities for introduction of IOT to collect data. “This organization has made it easy, both technically and culturally, for managers to test ideas and learn from them.”


This is an easy read and a big win for project managers looking for examples of how to implement Analytics Projects.

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PMI Silicon Valley Presentation on Analytics Project Management by Rosemary Hossenlopp

Big Data Analytics Projects Initiatives; Myths and Mistakes

How to help big data project teams

How to improve analytics project team performance

This education-packed session on July 13, 2016 shared trends in the analytics project management industry. This presentation provided key analytics project insights needed to manage analytics programs when you need to show business results for your big data program.


This talk covered:

• 3 Myths of Analytics Projects

• 3 Mistakes Managing Analytic Projects, and

• Understanding of Project Health Check Approaches

Analytics projects are filled with lots of new words. Rosemary shared key definitions to help you sort through the acronym soup which technical personnel may be using.

Next, this session looked at why this is really beginning to be an Analytics-everywhere world. We want to see how the trends apply to the project management space.

Most importantly, we  shared how there are a lot of myths out there. This really isn’t anyone’s fault. It is because no one is looking at this from a project management perspective. There is a lot of information on how to implement tools yet that doesn’t get teams to innovation. There is a lot of content on specific techniques yet without linkage on how to link to a strategic outcome. So due to the lack of a standard approach to analytics projects, there is a high failure rate.

Rosemary shared an analytics life cycle that will improve project outcomes. She also provides a framework to look at projects to see how to move your teams into innovation and growth outcomes!

Click Here to Receive a PDF of the Slides.

Email me if your team has questions on Analytics projects.