The Data Story’s in the Details

In a previous post, Structuring Your Data Story, I provide guidance on the big picture of storytelling — nailing down the five key elements of a story: characters, setting, plot, conflict, and resolution. However, if you've ever heard...

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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...

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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,...

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Challenging the Evidence in Data Science

Data drives the data science team's exploration and discovery, so the team must be on the constant lookout for bad data, which can lead the team astray or result in erroneous conclusions. In this post, I present several ways to challenge the data...

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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...

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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,...

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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...

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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,...

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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...

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Defining Areas of Responsibility for Your Data Science Team

Scottish novelist and folklorist Andrew Lang once wrote, “I shall try not to use statistics as a drunken man uses lamp-posts, for support rather than for illumination.” Unfortunately, many organizations that consider themselves...

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