Decision Mechanics

Insight. Applied.

All maps are wrong

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Map of the world

Maps of the world are a perfect example of how difficult it is to prevent bias creeping into your modeling.

People always have a perspective. And it’s difficult to eliminate or compensate for it.

Vox have a great video that discusses the difficulty of producing an accurate map of the world. It starts with the presenter slicing up an inflatable globe and trying to spread it out across the floor—with limited success.

You can see the impact of different mapping approaches in a gallery on Wikipedia. And, if you want to see how the dominant Mercator projection mangles the size of countries, there’s an interactive tool that illustrates the effect. Try dragging Greenland onto Africa for a dramatic example.

The National Geographic Society have apparently adopted the Winkel tripel projection due to the trade-off it makes between preserving shape and size.

Effective data science is often about choosing the appropriate trade-offs.

Highwaynet

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AWS Snowmobile

There are a lot of advantages to storing your big data in the cloud. Start-ups can get going without having to set up servers and data centers.

However, what about organizations that already have data centers? How do you move petabytes of valuable data to a cloud provider? You rarely see much coverage of this—because it’s a difficult problem.

There are no clever technical solutions. Amazon’s solution extends one of the oldest electronic data transfers strategies around—the sneakernet.

First, they introduced the Snowball—a suitcase of SSDs that can hold 80 terabytes (TB) of data. You fill it up at your data center and ship it to Amazon.

“80 TB?!”, I hear you scoff. “I’m not moving my kid’s blog.”

OK. Well, fear not—Amazon has your back. Let me introduce you to their Snowmobile—an 18-wheel truck that’ll hold 100 petabytes of data.

Drive up, fill it with your cat GIFs, tear down the highway and upload it to AWS. Now you’re running in the cloud.

Clarkson must have had something to do with this.

Amazon AI

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digital brain

Amazon is the latest company to offer developers access to AI services. There are four available.

  • Amazon Lex—Build conversational interfaces using voice and text, powered by the same deep learning technologies as Alexa
  • Amazon Rekognition—Deep learning-based image recognition
  • Amazon Polly—Turn text into lifelike speech using deep learning
  • Amazon Machine Learning—A scalable machine learning service for developers

Supporting data users

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stress photo

A critical, but overlooked, area of data science is linking analytics to action. Decision-makers must

  • Understand the information being presented to them
  • Have confidence in the analysis
  • Be able to place information in the context of the decisions they are required to take
  • Understand how to apply the information to make better, more informed, decisions

Too much focus has been placed on the needs of the analysts—and this is stifling the overall effectiveness of data science initiatives.

Brian Williams has produced an article—Creating an analytic culture through data interaction—discussing this topic. He has some interesting things to say.

He defines the problem as follows.

The complexity of information environments is challenging most organizations. Not all decision makers are ready to become more analytic, and as we focus on visualization, data mining, and predictive analytics, decision makers will become even more reliant on analysts and may not perceive they have the abilities to keep up with the quants.

One area of data science I’ve found to be weak is visualization. Many data scientists assume that charts are visualization tools that can be presented to decision-makers. Wrong. Charts are tools for data scientists. Managers may not have the time and/or the skills to draw conclusions from a chart.

The article has this to say on visualization

When analysts design visuals without user interaction in mind, their charts and infographics simply are a static sharing of their own analysis. […] Design needs to be sufficient enough to be a starting point for a decision maker that invites interaction.

Tooling clearly has a major part to play in bringing visuals to life.

Managers who are clued-up about data science—and its strengths and limitations—will be able to direct analysts to produce more actionable insights. There’s a constant cry for this sort of guidance from data scientists who feel they have the tools, but not the questions.

If we are to realize the substantial potential of data-driven decision-making we need to develop suppliers and consumers in lockstep. The current focus on supply-side tooling and training will result in disillusionment and throwing the baby out with the bathwater.