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Mike Perez

Mike Perez

Mike is a public interest lawyer and speech writer turned start-up cofounder, who realized that working with evidence to discover truth and advocate is just another form of working with and communicating data. He wants to see the barriers to data-driven thinking lowered to the point where data-driven decision making is the norm across the public, private, and non-profit sectors, and he believes the stories data tell are how to move people in that direction.
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Recent Posts

The Best Innovation Management Software for Complex Problems

Some problems are complicated. Finding a solution requires expertise and analysis, but the solution exists. Sky scrapers are complicated. Some problems are complex or even wicked. These problems have no solution. They’re too big, too slippery, too thorny.

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A Data Application for the _______ Genome Project

The ability to reuse and repurpose - exaptation - is often a catalyst for exciting breakthroughs. The Astronomical Medicine Project (yes, astronomical medicine) was founded on the realization that space phenomena could be visualized using MRI software, like highly irregular brains. The first private space plane, designed by Burt Ratan, reenters the atmosphere using wings inspired by a badminton birdie. Anecdotes like this abound in many fields, and the principle applies to working with data and creating data applications, as much as it does any innovation. 

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What is a Data Application?

There are data visualizations. There are web applications. If they had a baby, you'd get a data application.

Data applications are a big part of where our data-driven world is headed. They're how data science gets operationalized. They are how end-users - whether they're subject matter experts, business decision makers, or consumers - interact with data, big and small. We all use a data application when we book a flight, for instance. 

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Finding Abstractions that Give Data Applications 'Flight'

Continuing with our recent theme of abstraction in data applications, Dave King gave a talk last month explaining his design principles for "Making Code Sing: Finding the Right Abstractions." Nailing the best abstractions is a quintessential software challenge. We strive for generality, flexibility, and reuse, but we are often forced to compromise in order to get the details right for one particular use case. We end up with projects that we know have amazing potential for use in other applications but are too hardcoded to make repurposing easy. It’s frustrating to see the possibilities locked away, just out of reach.

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Making Service-Oriented Architecture Serve Data Applications

Bloor Group CEO Eric Kavanagh chatted with David King, CEO and founder of Exaptive recently. Their discussion looked at the ways in which service-oriented architecture (SOA) has and has not fulfilled it's promise, especially as it applies to working with data. Take a listen or read the transcript. 

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How to Tell an Interesting Data Story

The Laffer Curve. Anyone know what this says? It says that at this point on the revenue curve, you will get exactly the same amount of revenue as at this point. This is very controversial. Does anyone know what Vice President Bush called this in 1980? Anyone? …Bueller?... Bueller?... Bueller?

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Communicating Data Science: How to Captivate a Noncaptive Audience

When communicating about your latest data science project, whether verbally or in writing, your audience often needs to know the takeaway right away, or you’ll lose their attention. This is especially the case if your audience includes colleagues, conference attendees, or readers from outside your field. In an earlier post on communicating data science, I dove into how the elements of story can hold your audience’s attention through a dense presentation. This post introduces (and applies) some tried and true approaches for introducing the end of your story at the beginning. You’ll capture the attention of those for whom your point is valuable and have their attention for your story, and the rest of the audience doesn’t matter.

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Communicating Data Science with 'Story'

Getting your audience’s attention, keeping it, and persuading listeners of your point are all hard to do in a world where most listeners start out thinking, and feeling, “I’ve got my own scheisse to do.” John Weathington’s recent post in Tech Republic, “Be the Hemingway of Data Science Storytelling,” makes the point that presenting data, which can be dry, is more effective if it incorporates elements of story – a protagonist, a journey with challenges, and a conclusion. Jeff Leek’s “The Elements of Data Analytic Style” has a chapter about presenting data that emphasizes story as the method for communicating results.

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