Harnessing Spare Bits: VLAB Data Exhaust Alchemy panel
VLAB (web, twitter ) — the MIT/Stanford Venture Laboratory — hosted a session 1/18 at Stanford on “Data Exhaust Alchemy – Turning the Web’s Waste into Solid Gold“.
I’d never heard the term data exhaust (or digital exhaust, thank you wikipedia ), but it’s a handy idea. The proliferation of social media and the internet transformed “media” (generally media is considered a one-way push system) into “social” (the personalization and intimacy of narrowcast, or at least a many-to-many set of connections). Everyone set on broadcast, and all broadcasts stored for perpetuity. If you like data (and I do) and you are interested in how people act, interact, and think (and who isn’t) the idea that all those feeds and updates and comments would be going to waste — well, it’s heartbreaking.
Here’s the panel:
- Roger Magoulas (http://www.oreillynet.com/pub/au/2717), O’Reilly Media, big data http://radar.oreilly.com/2010/01/roger-magoulas-on-big-data.html, geolocation, former Sybase research team
- JB (Mike John-Baptiste), PeerSet, former JumpTV
- Dr. DJ Patil, LinkedIn, former eBay Analytics/Architecture lead, social network analysis, weather complexity research, former AAAS DoD Science & Tech Policy Fellow, Voldemort, Hadoop geek
- Jeff Hammerbacher, Cloudera, former Facebook data team lead (Hive, Hadoop, Cassandra)
- Mark Breier, In-Q-Tel, Blogger (http://10secondtips.blogspot.com)
- Pablos Holman, Intellectual Ventures Laboratory (http://en.wikipedia.org/wiki/Intellectual_Ventures), Komposite, “notorious hacker”
The basic premise of the panel appealed to the entrepreneurial marketer in all web 2.0 hopefuls — consumers are telling you their preferences (loves, hopes, fears, feedback) faster than can be processed, and companies that analyze and parse the data the best will cash in by designing the best promotions and products for the hottest, most profitable market segments. In addition to the new faddish feeds of facebook, twitter, foursquare, and blippy, some classic sources of consumer preference were mentioned: credit card data, point-of-sale purchase (hello grocery store shopper’s club), and government-held spending data. Everyone knows there’s gold in dem dere hills — like mashing up maps and apartments for sale, or creating opt-in rewards programs so you can figure out not just the best offer for Sally Ann, but also for all of her friends.
Philosophically, the reason we’ve been stuck with an advertising model (broadcast brand-centric messages rather than customer-centric, tailored promotions) is because we’ve never had a mechanism for scaling up a business to consumer communication — that’s tuned to the consumer. Now with the Web and current delivery mechanisms (inbox, feed, banner, search placement, etc.) not only can companies talk to their consumers — the consumers will engage in conversation. The better you know your customers, the better your ability to reach them (promote) and meet their needs (product). As long as you know how to interpret the conversation correctly.
“What people say has no correlation whatsoever with real life, but what they do has every correlation with real life…” Mark Breier, In-Q-Tel
Quickly the conversation moved into some of the technier topics: while there’s a promise of reward for the cleverest analysts, there’s some tricky issues too. For one thing, processing exhaust data in a way that makes sense, at scale, requires some serious processing horsepower and an architecture that can accommodate “big data”. While less time was spent on the technical issues than I expected (with DJ from LinkedIn and Jeff from Cloudera on the panel, a full-on Hadoop immersion was a distinct possibility), time was spent both on the computer science research in the area (machine learning, natural language processing) as well as the (more interesting to me) current thinking on the most appropriate analytic methods. More time on sentiment analysis and entity extraction would have been great, but probably really require follow-up sessions.
Magoulas from ORA did a nice job outlining some of the issues consumers are having with social marketing: besides basic privacy and data ownership questions (who owns the pics you’ve uploaded to FB? And how should filters be set-up for consumers to remain in control of information they want to keep semi-private?), there are issues emerging around security/spoofing/hacking — meaning, how much public information is available for consumers to be targeted. (Marketers can guess your age and who your friends are, more nefarious entitites can try the same techniques to learn more private details). My favorite issue described was the “creepiness factor” — which I can definitely relate to — which is the idea that consumers may not react happily to services that know them *too* well.
Ultimately, analytic marketing is here to stay. Since consumers create data and leave digital detritus wherever they surf, leveraging that data will continue pay dividends as service providers nimbly design customer-centric offers and products. Still, concerns about how personal data (whether private public) is used by unknown intermediaries could either drive withdrawal from social media (if consumers opt to lock-down their profiles and share only in private circles) — or — a more hopeful thought, drive further innovation in this space. Look for savvy organizations to weave opt-in, consumer-driven agent technology with the power to aggregate (and segment) the behavior and preferences of wide communities of people.