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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

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. 

To demonstrate and give our users a running start at successfully repurposing something, we want to share an editable data application, the Taco Cuisine Genome AtlasWe held an internal hackathon in which teams had a day to design and build a xap. (A xap is what we call data application built with our platform. Learn a bit more about our dataflow programming environment here.) One team took algorithms and visualizations created for a cancer research application and applied them to tacos. Application users can identify, according to multiple ingredients, specific tacos and where to find them.

The best part is that this wasn't entirely an act of frivolity. Repurposing healthcare and life sciences tools on different, albeit mundane, data led to a potential improvement for the cancer research application - a map visualization of clinical trials for specific cancer types. 

It can't be said enough. New perspective is a key catalyst for innovation. 

So, we've made this xap available for the public to kickstart other work. Explore itbuild off it, and apply it to your own data. You can also learn the basics of how it's done.

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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. 

Dave King, our CEO and one of our chief software architects, spoke with Software Engineering Daily about what makes data applications important and best practices for building them. Check out the podcast or read the abridged transcript beneath it. (Learn how they're built, or try building a data application if you'd like.)

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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. 

Eric Kavanagh:  Ladies and gentlemen, hello and welcome back once again to Inside Analysis. My name is Eric Kavanagh. I’ll be your host for today’s conversation with David King. He is founder and chief executive officer of a very cool company called Exaptive. David, welcome to the show.

Dave King: Thanks for having me.

Eric: Sure thing. First of all, I’d like to just throw out a couple quick thoughts to frame the discussion here. I’m familiar with what you are doing at Exaptive, and I think it’s absolutely fascinating. In this world of enterprise software, we have these huge organizations, these very large companies, IBM, SAP, Oracle and SAS, and they’re just obviously prodigious companies building enterprise software. There’s a lot of great stuff that’s come out of that. No doubt about it, but of course, there are some pretty significant constraints. One is cost. A lot of that stuff is pretty expensive, but there are other  walls that have been built up. Some are virtual, some are metaphorical, and they make the whole process of really digging into data and analyzing data somewhat cumbersome I think.

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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?

Data stories, believe it or not, can be gripping. You worked on the project because there was some urgency to it. The story is interesting because of that urgency and how you dealt with it.

In prior posts on communicating about data, I’ve introduced what it means to use ‘story’ to connect with your audience and how to structure a post to catch a non-captive audience's attention. In this post, learn what kind of content makes a data story a pleasure to experience.

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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.

Great points. Essential.

But how literally should “story” be taken? Story, often romanticized as an abstract concept by omitting an article in front of it, can be an enchanting idea.

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