The other day I spoke to a group of institutional research professionals. The topic was the College Transparency Act that has been introduced into both chambers of Congress. The Sway I used to frame my talk is here. In it, I reference the discussion that follows this tweet:

and then led to this:

Counting is definitional. Measurement is about dimension and experience.

In order to count, we must first define the distance (but really the difference) between zero and one. Counting to one is not merely the pointing to an object and saying, “One.” Instead it is, or should be, a conscious decision to decide what is being counted. Apples, dogs, Golden Delicious apples, Irish Setters,  ripe/unripe Golden Delicious Apples, male/female Irish Setters, etc. From the gross to the specific, we can decide what to count and how to count it. In doing this, we define the distance between zero and one, and that distance remains constant. It’s like a number line of objects.

This is because we can now communicate about the thing we can count, the thing we have defined. Sender, message, recipient, feedback; this loop is possible because the sender and recipient both have a shared understanding. Whether the definition is at the highest, most generic level, or the most specific, it allows two or more people to talk about it.  Once we can talk about something, we can express ownership of it, we can exchange it for another thing, we can monetize it. We can control it.

Is there really another reason to count something other than to exert control in some fashion? Sure, we can study for the sake of learning and knowledge, to satisfy curiosity, but I would argue that those are efforts at a different types of control.

Measurement allows for supplying additional information of a thing, such as dimensions along an axis, or color (“red” is just a generic term for a reflected light in certain range of frequencies), or cost, or potential acceleration and speed, fuel economy. The more we measure, the more we create definition and difference. Difference provides comparison. Difference allows us to determine or assign value.

Measurement also allows us to determine change. Change along an axis of direction (movement), time, and progress towards a specific change. Measurement is about the experience of a thing, from the perspective of those who measure, not necessarily the perspective of what is measured. When measuring people, measurement makes the personal into the deeply impersonal.

Knowing the difference between 0 and 1 is the foundation of effective communication and the ability to exert control and ownership. Measurement allows us to describe what we are counting, create differentiation in value and experience.

If you are not familiar with the term “kludgeocracy,” take a look at the paper by Stephen M. Teles over at New America. Basically it describes the patchwork of policies and regulations that have become a crazy-quilt of “clumsy and temporarily effective solutions” to problems identified in American policy.  I wrote about this topic in 2013 over on the SCHEV Research blog, bringing together the ideas of the kludgeocracy, Big Data, and personalized public policy. I want to revisit these ideas outside the realm of public policy.

I know someone who is a “returner.” This person shops insanely. Buys things and takes them home to see if they “work.” Often they do not. So, they will take them back to the store for a refund or credit. Over the years I have warned them that this cost the stores money, even without considering various efforts at fraud (e.g. I bought a scale at Walmart and opened it at home to find someone had returned their nasty old scale in the package of the new one), and that someday returns, even with a  receipt would not be allowed.

And so we are coming to that day. Return policies at big box retailers are changing. I think WalMart now allows only three returns without a receipt per year, another national retailer, only three returns  per year, period. Think about this. I’m not sure people really think about closely and completely their transactions are being tracked. It’s not just the purchases, but the returns as well. It’s everything.

My friend’s experience is highlighting to me how much return policies are changing. At one national chain, this person’s purchase history was inexplicably “lost” or at least “unfindable” during a four-week period in which the chain’s return policies became much more restrictive.

Retail businesses often live or die at the margins of cost and revenue. It is clearly rational to me that they attempt to do anything to reduce marginal costs and increase marginal revenue. With Big Data and predictive analytics, it would seem to be rational to know your customer’s purchasing habits (and corresponding return habits) to not only advertise to them based on those habits (which is a policy action) but to grant them enhanced status or benefits, also based on those on those habits. Of course, the reverse is true – assign lessened status and benefits.

In other words, I see a day where return policies are determined for very small groups of customers (basically to ensure some veneer of legality and pretense of equity of treatment – really, I mean individuals). For example, your purchase receipt might allow you to return an item for refund or credit within 24 hours while mine might allow me 30 days since I don’t return things (I tend to accept and own my mistakes).

When a store or chain has your entire purchasing history accessible to each transaction, all sorts of possible kludges are possible. All of which are as specific to you as the business can get away with. Further, when multiple store brands are owned under the same corporate entity (TJ Maxx, Homegoods, Marshall’s) the data collection gets larger. When corporations buy and sell customer data from each other, it get’s even larger. They can build a profile of you, of each of us, that allows opportunity to maximize revenue – which is really all that matters.

I think this is happening now, or will very soon. What should the public policy kludge be in response? It seems to me that upfront disclosure and transparency are the minimum. Perhaps the number and demography of people in one’s “policy group” should be at the heart of the disclosure. For example, Google Fit will tell me I have been more active than 96% of Midlothian, VA in the last week, but I have no idea what that means. I know it is of Google Fit users, but is that 10 or 10,000? What’s the distribution across the population? How do they compare to Garmin Connect users in Midlothian (which gives me a somewhat lower ranking)? Basically once a person has been reduced to a number or complex set of numbers which affect their ranking or the benefits they receive, they should be given enough information to allow them to modify their behavior and improve their ranking, if they so choose. Like a credit score.

The kludgeocracy in Corporate America is here. It is not a black box, but an interconnected series of black boxes based on algorithms written by people that have basically one view of how things should be and have been told to “reduce costs, increase revenue and profit.” At least I know that as a middle class, middle-aged white male, these kludgeocracies will tend to benefit me. But, I’m pretty sure that’s wrong.

Actually, I know it’s wrong.

I spent yesterday in class.

It went like this.

“Take this time to read these pages. I will then talk to you about what you read.” 

“Evangelize” would be more accurate than “talk.”

For a class on data visualization, there were very long periods of talking with no visualization. A large chunk of his talking is about how to do meetings and dump PowerPoint presentations following Amazon’s model of using a six-page narrative memo that is read at the beginning of the meeting and then discussed.

Laura Gogia attended the same event the day before. Her cogent analysis is here. You should read it as I can’t top it. I can only add a another dimension.

Tufte has clearly thought long and hard about presenting data and information. There is a lot to learn from him. However, his focus on paper as the ideal medium for presentation simply doesn’t fit my concept of the dynamic nature of the Web. Many of his ideas I would love to implement overnight, but the current technologies simply don’t offer it. For example, placing text and labels appropriately within a graphic to move away from the use of legends and have a more map-like graphic (his ideal model for us to follow is Google Maps) can easily be done with static presentation graphics. Doing the same where a different institution or student group or state is selected is next to impossible when the images within the chart are now of different size and shape. How does a piece of software know where to place text so that is easily readable in relation to all other objects and pieces of text? How does it do so aesthetically?

His basic complaint he describes as “going into one room to make a table of numbers, going into another room to make text, and yet another room for graphics.” Using “room” as a metaphor for “apps.” I totally get this. I have had the same complaint for years – I want to work on one canvas where I can do everything. Photoshop, Illustrator, and a host of other tools allow me to do this for static designs. Nothing really allows me to do it dynamically.

I love having a big canvas where I can place things where I want them. I can create the message and content to match my vision of what I need to convey. But it is static and I work with lots and lots and lots and increasingly lots more data. When I create a data display that has to provide the same information on a small junior college as it does for a large research university, things change. In order to place objects on a digital canvas for the Web, every pixel has to be mapped and every item anchored according to a grid, with a combination of absolute and relative positions. I can do much of this with our current BI tool, but it has limits. There are simply a fundamental differences between digital and paper tools. One of which is that paper does not allow hyperlinking – save through footnotes, endnotes, and directives (e.g. see pp x-xx).

I would pay good money for a digital platform that allows me to create dynamic content that follows Tufte’s design principles.

It seems to me that this is the solution to Tufte’s complaint. It also seems to me that he can can solve this himself. After all, he created and owns his own publishing house to meet his standards for printing. So why not work with venture capitalist or two to develop a startup company to build such an app?

The more I think about this idea, the more doable I think it is. It simply needs a rethinking of what the underlying mapping strategy of content objects looks like. It would be a radical departure from current apps. If a VC wants to fund this, I’ll pull a team together.

That’s the battle to fight to win the war against bad data visualizations and bad presentations.

I’ve followed Tufte’s work for at least 20 years. It has been a key part of my thinking and design practices, as best as I can implement with the available tools. I have also followed my own muse, my own analysis of users interests and behaviors. I do plan to implement some modifications based on yesterday’s class because we can always do better.

These are the notes I used at panel discussion at the 2017 AIR Forum. The panel was “Big Questions About Big Data” with Jeffrey Alan Johnson and Loralyn Taylor.

Being here today, I wonder if we are becoming the people William Gibson warned us about. If you haven’t read Gibson’s works, especially some of his early works like Neuromancer and Virtual Light, it might be time. His vision in the early 1990s of identifying and tracking people through their economic transactions helped define how I, and I think others, began to think about tracking students across time and location.

What we see happening today with Big Data goes beyond the early days of Gibson’s Cyberpunk. He did not imagine social media to extend quite the way it did and to become another a dataset, at least as I recall. I remember though how it struck me to think of people as being nothing more than their economic transactions. I could see the same applied to students. Track their own economic transactions, financial aid and tuition and fee payments, course/credit attempts, credentials earned. All of this combined with demographics and definitions to add shape and structure to the patterns became my interests for a while.

The reporting almost became secondary. It was the attempt to envision the world through the flow of what was then very limited data.

Of course, it all came at the price of not looking at the world, only its representation in the data. It was too easy to forget that these images in the data were real people and I was making recommendations for policies and interventions that could change their lives.

Remember, Jeff Goldblum’s character in Jurassic Park, Dr. Malcom? “Yeah, yeah, but your scientists were so preoccupied with whether or not they could that they didn’t stop to think if they should.”  I know we all have good intentions, we want to serve students better, to ensure their success. We want to be more efficient and effective, always seeking better use of each dollar regardless of its source. But, what are the implications for what we are doing?

Two years ago, I sat in a six-year planning meeting for a college, where institution leadership announced its partnership with a leading tech company to significantly increase retention and graduation through predictive analytics and intrusive advising. In fact, they went so far as to say, “If the model predicts a student heading for trouble and is unresponsive to attempts at communication, we will go so far as to have someone waiting for them in the parking lot at their car.”

Say what? I was taken aback. I understood the intent and appreciated their commitment to student success, but asked something along the lines of, “At what point does an intrusive advisor look different than prospective rapist? Aren’t you normalizing behavior, a use of data, and student identification that you would not normalize or even accept under other circumstances?”

This was when I blogged about the need for ethics and suggested and each following meeting that every Virginia institution considering the use of big data and predictive analytics should develop a statement of ethics about use.

If we take Big Data and predictive analytics to their idealistic conclusion, we would seem to remove all doubt from outcomes and changing individual performance. But what would happen to maturation along the way? Would some numbers of our students mail to mature during college since they failed to fail and thus learn from the experience? Would we end many of the failures and happy accidents like those that led me to becoming an art major which set off a chain of events that put me here today? Admittedly, I had privilege, support, and a fair amount of well-being, so I was not that much at-risk, save for the riskiness of being in the Army, which is where I went after dropping out of college.

One of the most exciting things of my job is the Virginia Longitudinal Data System.  Using processes of de-identified matching, we have the ability to observe individual and group outcomes across nine agencies (and growing) to learn how to better deliver education, workforce, and social services. We do this under some of the most restrictive state privacy laws in the nation, and complying fully with federal privacy laws and regulations. The underlying story of longitudinal data is that time, place, demography, and experience create a “dataprint” of an individual that is at least as unique as a thumbprint. Patterns emerge from large datasets describing not just unique groups, but unique individuals. In committing to the VLDS privacy promise,  we rely on adherence to the letter and spirit of the law, and best practices.

It is through developing a real understanding of privacy laws that guides us in this work. There are three critical components to know:

• A data subject (someone from who you collect data) is entitled to know what data the government has on her. This includes any identified data acquired from a third-party.
• Data and information through analysis and sharing cannot be used to cause “harm” to a data subject unless the possibility of such harm was disclosed to the subject at time of disclosure.
• The subject has the ability to give consent or not to the provision of data and is informed as to what limits of service that may create.

Big Data can push us in a direction away from these principles. We can’t allow that. We must embrace these to ensure fair treatment of each person in the data.

I had to leave the conference after the first day for a meeting in Williamsburg. While away, I followed some of the action on Twitter. I saw a photo from session on analytics that listed “Black,” “Hispanic,” and other demographic variables as “risk factors.” It seems to me that when demographics and identity are risk factors in your education enterprise, you are not trying to educate the students you have, but the students you wish you had. There is a fundamental difference.