Friday, April 18, 2008

A conversation with Jeff Bezos

A couple of days ago, I had the pleasure of chatting with Jeff Bezos before and after he gave an excellent talk to about 500 of our alumni. Jeff made a number of interesting (and humorous) observations, speaking on topics ranging from why Amazon experiments actively to how we've become a society of information "snackers" to his basis for spousal choice ("someone who can get me out of a third-world prison").

What made me think the most during the few minutes we chatted was his (seemingly simple) framework for making difficult decisions. Innovative companies like Amazon often have to make big decisions with little or no data. In making these choices, Jeff says that his choice is governed by "what would be better for the customer?". His point was that in the long-run, the interests of one's customers are perfectly aligned with the interests of one's shareholders. (This is clearly not the case when one has to manage short-term earnings.) He cited cases ranging from launching Amazon Prime to allowing customer reviews (both positive and negative) to remain on the site as examples where this framework paid off in the long run.

This observation (which seems to make more sense the more one thinks about it, although "perfectly" aligned might be a slight simplification) is an interesting one when applied to data ownership. Because it implies that in making data collection and retention choices, the smartest companies might be the ones who formulate policies that are aligned most clearly with the welfare of their customers. This is a lot simpler than thinking about expected future value and liability. I'm not yet convinced, but there's something interesting here.

I still don't have a comfortable feel for why they've entered the cloud computing business, but that's a subject for a different post.

Monday, April 7, 2008

Kansas, Memphis, data: value and liability

I ended up watching the Kansas-Memphis game. The first college basketball game I've watched this year. Nevertheless, I made many predictions. Most of which were wrong.

The one prediction I was most confident about was when Kansas made the 3-point shot in the last seconds, tying the game. At that point, it was absolutely clear to me that they would win the game. I had no data, no context, no history, but it didn't matter. All I had to do was look at the faces of the teams, and it was so clear who would perform better in the next 5 minutes. I didn't need historical performance data of any kind.

Companies have so much customer data these days. These data seems valuable, and worth storing, even though their value isn't immediately apparent. But I wonder if we're forgetting that data in isolation might not be marginally that valuable any more, and further, that firms need to understand how to associate a data trail with a conversation and a person before making a business decision. And if they don't, while they might anticipate value from mining the data in the future, perhaps they shouldn't be keeping the data, because it could end up being a liability to them. As Professor Vasant Dhar and I have discussed and written about in the past, firms may well need to rethink their "data valuation" models and strategies.

Just one dimension of a much larger discussion about how firms should manage their customer data.