May 2017 Archives

Summer Challenge Reading

Have you set a reading challenge for the summer? This is something different: challenge reading, not a reading challenge. Four books I've read recently that challenge our assumptions and normal ways of working in today's data-centric world. Have fun.

Programmed and dangerous
Weapons of math destructionFirst up is Cathy O'Neil's warning of the unintended consequences of giving decision-making authority to algorithms, Weapons of Math Destruction. Enlisting computers to take over the tedium of large-scale decision-making is great for efficiency, but the cost is the increase in systems that (1) harm the subjects of inquiry (2) at scale (3) without accountability. Absorbing her definition of a WMD, alone, is worth the price of admission.

Black-box scores are automating decisions about education, employment, credit, and even prison terms, using criteria that can be arbitrary, unfair, and unaccountable. Even the seemingly harmless work of ad targeting sometimes embodies the dark preferences of predatory businesses. This one's important for anyone working in analytics.

SensemakingChristian Madsbjerg wants us to toss the algos altogether in favor of older methods, arguing for the humanities in Sensemaking. More thought-provoking than how-to, he makes the case that we've inherited insights into human decision-making that have been developed over centuries (millennia, really) of effort in fields such as philosophy, psychology, and anthropology. As it turns out, work on understanding and anticipating human decisions didn't start with customer databases (who knew?).

Sensemaking is a bit heavy on promotion and light on description, but it's a quick and worthwhile read as a reminder that we have ways of knowing things that aren't packaged in software. His five principles are obvious but constructive, serving as a bit of antidote to big data's streetlight effect. His view of thick data would make a great starting point for a deeper dive.

Technology vs humanityMoving beyond the data-to-decision world, the futurist Gerd Leonhard wonders how we preserve our humanity—and what that even means—as the future invents itself from forces already unleashed. Technology vs. Humanity is one of those books that sets out to map the consequences of multiple sources of change, starting with readily observable technological changes that we already live with.

Leonhard makes much of that insight that changes are accelerating exponentially and affect us combinatorially. It's that combination of trends that happen "gradually, then suddenly" that threatens to change the world before we realize what's happening. Does it really add up to a future of us versus the machines? The point is that we should think about the possibilities before emergent characteristics of market-oriented developments make the big decisions for us.

Log out of Facebook
Deep workFinally, here's something completely different and relevant whether or not you work in the data mines. Cal Newport suggests that knowledge workers set aside distractions and learn to focus on Deep Work. The catch is that "distractions" are most of what we do now, from email and meetings to hallway conversations and—yes—social media. Even collaboration tools, which are meant to foster a certain kind of productivity in work environments, create the conditions in which the highest value work can't happen.

Newport starts with a definition and defense of deep work, which includes some of the highest value work people do: inventing, coding, designing, writing, discovering… As we're changing the typical work environment to make deep work more difficult to do, its value is increasing. Computers aren't good at it, and we've distracted most of the people, so the reward for those who can do it may be growing.

The rest of the book is how-to, and the good news is that the method isn't complicated. The bad news is that you'll have to change your habits. Deep work requires that we create the mental space for it, which means cutting out some of the distractions that we like. The reward is in becoming better at the parts of what we do that are most likely to make the highlights reel.

As a visible project, The Analyst's Canvas is new, but it's been cooking for years. Now comes the fun part: working through the ways to use it. Today, let's talk about raw material: the data that go into the analytic process that leads eventually to information, insight and action.

Look at the row of three boxes across the middle of the canvas: data / sources, analysis and presentation / delivery. In 2008, I used that basic outline to describe the building blocks of social media analysis. With the boxes empty, we have a framework to summarize many different tools and approaches. In the next release of the canvas, this row will be labeled Description.

The analysts canvas captioned

The big idea of the canvas is to keep analytical work grounded with two of my favorite questions: what are you trying to accomplish, and why? Collectively, the boxes on the Description row characterize an intelligence or analytics process from source data to delivery, whether the finished product is a report, a software tool or something else. The row is below the Objective box as a reminder that the work has to support meaningful objectives.

I suspect that many discussions will take the components of an analytical play as a unit, especially since so many capabilities come packaged as turnkey tools with data, analytics and presentation built in. But whether building a new capability or evaluating an existing one, the three component boxes must be the right choices to support the Objective. It's not enough that the pieces work together, because we run the risk of developing elegant solutions to the wrong problems.

Using the canvas as a prompt
Every box is the canvas includes a set of basic prompts to initiate the exploration. The Data exploration begins with these:

  • What data/information is required?
  • What sources will provide it?
  • What are the limitations and drawbacks of the chosen sources?
  • Is this the right source, or is it just familiar or available?

Beyond the prompts, we can use the canvas to ask important questions about our preferred data sources:

  • Does this source contain the information needed to support the Objective?
  • Does the information from this source answer the questions we need to address?
  • What other/additional sources might better answer the questions?

Analyzing the canvas
If you look back at the canvas, you'll see that those questions address the relationship between one box (in this case, Data) and its neighbors (Objective, Questions and Alternatives). Its other neighbor, Analysis, is a special case. Depending on which you consider first, you might ask if the source contains the information needed for your analysis or the analysis is appropriate to the properties of the source.

Source to mission

In the first draft of the Explorer Guide, I included some suggested orders for working through the sections of the canvas for a few scenarios. In this exercise, I'm seeing something different: insights we can gain from the relationships across boundaries within the model. More to come.

About Nathan Gilliatt

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  • Voracious learner and explorer. Analyst tracking technologies and markets in intelligence, analytics and social media. Advisor to buyers, sellers and investors. Writing my next book.
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