Data Storytelling

Have you ever watched a movie that had great actors, costumes, sets, cinematography, and special effects but a terrible plot? If so, you probably left the movie theater or walked away from your television feeling disappointed. On the other hand,...

Continue Reading...

Overcoming Common Barriers to Asking Data Science Questions

In a previous post, Getting Better Data Analytics Questions, I stress the importance of asking compelling questions when serving as a member on a data science team. After all, questions are the impetus for exploration and discovery. In that post...

Continue Reading...

Missing Data Leads to the Wrong Conclusions

In my previous post, Challenging the Evidence in Data Science, I encourage data science teams to be skeptical of any claims or evidence that supports those claims, and I provide several techniques for challenging claims and evidence.

However,...

Continue Reading...

Three Places to Look for Data Analytics Questions

In a previous post, Getting Better Data Analytics Questions, I recommend a couple ways to get the get the ball rolling when it comes to getting people in your organization to start asking compelling questions. However, getting people to ask...

Continue Reading...

Using a Question Board in Team Meetings

The question board helps with this because it provides a place for people to focus their discussions. It also helps the team stand up and participate physically and come up with new ideas.

Many of your questions will be interconnected. Often,...

Continue Reading...

Getting Better Data Analytics Questions

The success of any data science initiative hinges on the team's ability to ask interesting questions that are relevant to the organization's success and its ability and willingness to challenge assumptions and beliefs. After all, without...

Continue Reading...

Making Your Sprints More Insightful

In my two previous posts, Building a Data Science Life Cycle and Conducting Data Analytics in Sprints, I present a six-stage framework to structure the work a data science team performs and five techniques for performing the work in intense,...

Continue Reading...

Conducting Data Analytics in Sprints

In my previous post, Building a Data Science Life Cycle (DSLC), I encourage you to adopt a structure for your data team's activities that is conducive to the type of work it does — exploration. I refer to this structure as the Data Science...

Continue Reading...

Building out a Data Science Life Cycle (DSLC)

A project lifecycle can be a useful tool for structuring the process that a team follows. (A lifecycle is a repeating series of steps taken to develop a product, solve a problem, or engage in continuous improvement.) It functions as a...

Continue Reading...

Defining Success Criteria for a Data Science Project

In a previous post, Comparing Software Projects to Data Science Projects, I point out some of the key differences between traditional projects and data science projects. One of these differences is in the deliverables. With traditional projects,...

Continue Reading...
Close

50% Complete

Two Step

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.