A recent study from Gartner suggests nearly one in five business leaders expect to recruit a chief digital officer by 2014 and about 17 percent foresee appointing a chief data officer within the same time frame.

The development of the CDO position today is akin to the development of the chief information officer position a decade or more ago. A quick comparison between wikipedia’s full description for CIO and the sparse description for CDO shows just how new these developments are. While many CDOs work for content companies and are tasked with digitizing analog businesses, I expect the position to evolve to also encompass digitizing and monetizing business models that didn’t (and couldn’t) exist in the analog world.

At the end of January, Facebook announced they would partner with the suicide prevention group Save.org. The goals of the partnership are to research the online behaviors of suicide victims in the days and months leading up to their deaths in order to identify and detect patterns of potential suicides.

If successful, the research will identify common strings of those most at risk of committing suicide before they do it. If successful, this research could create the groundwork for the implementation of what I’ll call “digital safety nets” and aid in the prevention of suicides.  These digital safety nets are really just triggers that key a series of digital (and ultimately physical) responses to a given risk. At current, there is a vast amount of research going into the creation of digital safety nets for a swath of risks.

I’ve long been intrigued by the work being carried out by researchers at Northwestern who have been working with a mobile app platform they created call “Mobilyze.”  The mobile platform is designed to help those suffering from depression by prompting users to make changes in their surroundings and/or behavior to reduce or eliminate depressive symptoms. The platform is also designed to identify the patient’s state and provide intervention prompts like text messages or phone calls.

Many of the personal connected devices we see today take what we might already know and provide the information back to us in more exact terms.  For example, we know we exercised or walked or biked, but a given fitness device with an embedded GPS can tell us exactly how far we went or how much we did.  What we are seeing with the creation of digital safety nets is slightly different. Here these connected devices might take what we ourselves don’t know and provide us insight. These connected devices could develop the capability of identifying for us are own tells.

Yes – there are of course the risks of false positives.  But the alternatives are much worse.  And algorithms should get better and more refined (perhaps I’ll write a bit more about why this might not actually be the case in a future post). And while we are currently focused on behaviors with large negative outcomes, we could theoretically alter any behavior – simply through a  mix of digitization, inverted crowdsourcing or perhaps the use of quasi probe data, and finally digital prompts that subsequently exert influence on the behavior we are seeking to alter.

An AP story from earlier this week highlights the work Scott Harrison and Charity Water is doing to to install sensors in wells in Africa so water flow can be monitored remotely. With declining sensor prices, expect to see more sensors deployed to monitor charitable work in hard-to-reach places.

Today I had lunch with a friend whom I haven’t seen in several years.  We met in a part of the city I rarely visit so I wasn’t familiar with the restaurants. Our phone conversation setting up our lunch plans went something like this:

friend: “What are you in the mood for…”

me: “I don’t know, what is there…”

friend: lists several nearby restaurants – none of which I know

me: “umm…I don’t care, I’m fine with anything…”

Upon arriving we settled on one of the restaurants near the intersection where we met.

Upon going into the restaurant, our conversation went something like this:

friend to waiter: “are there any lunch specials…”

waiter points to lunch specials on the menu and then leaves us to make our decision.

friend to me: “want to share a pizza…”

me: “sure…”

friend: “want kind of pizza do you like…”

me: “any kind….lunch special sounds good”

friend: “no, too many meats. What about margarita or perhaps quattro formaggi…”

me: “sure, that sounds fine…”

waiter returns and takes our order.

In reality, you can see that these two decisions – where to eat and what to eat – didn’t take a tremendous amount of coordination.  But it did take some coordination.  More, it monopolized most of the initial conversation with someone I haven’t seen in two years. As we begin to (first) digitize broad swaths of information and (second) intermingle and intertwine these diverse streams of data, I can envision a variety of ways these series of decisions are handled very differently in the future.

Imagine this (not implausible) scenario:

First, my phone has stored locally everywhere I’ve eaten in the last two years or more likely my phone has access to a service or set of services which knows this information.  Three or four years ago information like this wasn’t digitally available. Today, this information is stored across a variety of services.  Foursquare knows where I’ve eaten and where I eat frequently. Facebook might know where I’ve eaten. Yelp might know what I like or Pinterest or Facebook photos or the tips I’ve left on Foursquare or the tweets I’ve sent through Twitter. I’ve left digital breadcrumbs across a number of Internet accessible services and the rate and magnitude of these digital bits of information are only increasing.

The second element of this future scenario requires sharing these diverse (and dispersed) streams of digital data. I believe cross pollination is the proverbial next big thing when it comes to digital data.  We’ve started to capture (personal) information digitally, but we aren’t yet making predictions based what can be derived from looking at these streams collectively as opposed to individually.  We are quickly entering an Age of Algorithms.

Here’s an example of how this might work.  Not only does my phone have access to my personal digital data, but it also sees the digital data of my friend. This also isn’t that far fetched. Since we are connected on Facebook for example, this single service already knows a lot about each of us. Facebook could have recommended one of the nearby restaurants by mapping our profiles against each other and examining the intersection of our profiles while utilizing the GPS coordinates of my phone to identify high probability matches.

The second question – what to eat – could be solved in a similar way.  Imagine our phones have embedded NFC or some comparable technology.  As we enter the restaurant I opt to “share” my information with the restaurant.  The digital streams of data I share will include obvious things like food allergies, but it could also include explicit or implicit eating profiles.  I might share what restaurants I’ve ranked highly in Yelp or in other similar services. I might share my credit card information which knows not only every restaurant I’ve eaten at, but also what I ordered. In the future I might share my grocery store scanner receipts which contain a record of everything I’ve bought at the grocery store. Rather than asking the waiter what he or she would recommend and getting back their personal (and therefore biased) input, I could get a series of computer-generated, probabilistic recommendations based on the analysis of literally thousands of data points. These recommendations could provide options for what I like individually or what multiple people might like to share. In this way, we are increasingly turning over decisions to machines. Or at least, we are allowing machines to influence our decisions.  In this we see how the digital world is going to increasingly influence what happens in the physical world.

Last week I began looking for tickets for the weekend NLDS series between the Nationals and the Atlanta Braves.  The Braves of course were subsequently knocked out in the elimination Wild Card game against the St. Louis Cardinals and now the Nationals are playing the St Louis Cardinals in St. Louis.  But in looking for tickets in Atlanta, I noticed that Atlanta has implemented Demand Based Pricing.  You can read more here.

Here is a screenshot of the FAQs.

With large troves of data, I imagine most event ticketing will move to demand-based pricing.


I caught the recent story of a man arrested for drug possession in Orlando.  Apparently police monitoring a live video feed sent officers to the scene who then made the arrest.

In the accompanying video, one of women being interviewed about the role of video cameras says something interesting. When asked if it is acceptable to use live video feeds to monitor behavior she responds, “around here they need to be watched.” Note, she doesn’t say, “we need to watch.”  She suggests the video doesn’t apply to her behavior, but rather the behavior of others. As we transition fully into the digital decade there remains diverse opinions on the role of data, but many seem to share this similar sentiment – that this broad movement doesn’t apply to oneself.



Much has been written about big data (insert air quotes) over the last 12 months and articles are now regularly showing up in mainstream publications (also see: Six Provocations for Big Data, IBM‘s Big Data landing page, and a couple of NYT articles from the past few months here and here). During a panel during The TV of Tomorrow conference held earlier this month in San Francisco, Jeremy Toeman suggested big data was a bubble.  He made this comment with a reference to twitter and other similar data.  I’ll call these data public data – which suggests there are private data which I’ll talk more about below.