Big companies are not becoming data-driven fast enough | Hacker News Reader

Big companies are not becoming data-driven fast enough

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35 pointsMereruka14 comments

I'd like to see a solid argument for a new style of management that uses statistics in new ways. For the most part, my experience with the phrase "data driven" has been a negative one. Most of the time, when I have a client that claims to be "data driven", they are using a style of argument to avoid direct, honest conversations. When I wrote "When companies make a fetish of being data driven they reward a passive aggressive style" I did my best to explain what I've seen:

"As far as I know, there has never been a company that said “We want the worst informed people to make the decisions” so in a sense all companies have always valued data. But they didn’t make a fetish out of it. They simply expected people to be well informed, and to make intelligent arguments, based on what they know. That would have been true at General Motors in 1950. That much has probably been true at most companies for centuries. When management says that the company is going to be “data driven” they are implicitly asking for a particular type of interaction to happen in meetings, an elaborate dance where people hide their emotions and quote statistics."

http://www.smashcompany.com/business/when-companies-make-a-f...

The longer time goes on, the discussions I see and hear about "data driven" business sound more and more like the discussions my father used to have with my mother about how the bean counters were ruining the ability of the company he worked for to operate and do business.

Ultimate problem being, if you're only staring at data, it's hard to tell the difference between critical systems that look like they don't do anything and useless systems that drag the system down. You need to understand the whole system, and not just the measures you're extracting from it.

> The longer time goes on, the discussions I see and hear about "data driven" business sound more and more like the discussions my father used to have with my mother about how the bean counters were ruining the ability of the company he worked for to operate and do business.

> Ultimate problem being, if you're only staring at data, it's hard to tell the difference between critical systems that look like they don't do anything and useless systems that drag the system down. You need to understand the whole system, and not just the measures you're extracting from it.

Thanks for the insight. Do you think adoption of graph databases like Neo4j help in making the contextual use of data a more intuitive process?

How about the Oakland Athletics, the Boston Red Sox, the Chicago Cubs, or the Golden State Warriors? All of these teams bucked a very strong counter-current by applying data-driven management practices. Go back even further to General Electric under neutron Jack Welsh, who advocated six sigma practices with great success. More controversially, look at the practices of the financial industry. Decisions grounded by sound quantitative risk management practices helped to contribute to survival while others who didn't exercise such practice collapsed.

How about the organizations that failed to manage using data driven decisions and took huge risks: Merrill Lynch, Washington Mutual, AIG,....?

Trusting the algorithm gets flak but it sure has plenty of anecdotal evidence to support why it is superior to gut instinct and wisdom in many circumstances.

Well, I would argue that while being data driven often has a first mover advantage, eventually this is arbitraged away. The first few movers reap a lot of the benefits, and by the time everyone starts to replicate a similar methodology, it is either too hard to catchup, or there are too many dogs chasing the same car. A good example of this would be banks in 2008. The quants were looking at only the underlying statistics, and not conceptualizing that the historic data didn’t account for housing bubble, low rates, zero down, etc.

Here I am thinking we went too far with data-driven product development. A/B testing and optimizing for bad metrics like "engagement" which creates products like facebook that look good from a metrics perspective.

So true. Ultimately, data only gives you insight into people's short term decision processes. It doesn't help you to give people what they actually need in the long term.

Also, data can always be presented in ways which serve the financial interests of those who control that data.

I think humanity has gone too far with companies being too data-driven. Companies have been data driven for a long time, but instead of using MAUs or "engagement" (which produces optimized garbage like facebook) we use revenue and income which, when becomes the primary driving metrics, produces another kind of garbage. Products are getting cheaper, more disposable, and less sustainable.

That last thought makes me upset when I think of people as the product.

Headline is misleading. This is reporting on some research showing that adoption of the "data-driven" buzzword is fairly slow. The assertion is made that this is important, without evidence.

Not answered: fast enough for what?

For the robot overlords to take over.

You might say the article is lacking data to support its claims. I guess it was too difficult to gather.

I like what you did there!

>fast enough for what?

> "They can acquire all of the latest solutions to bring data to the forefront, but unless they combine that with a broad cultural shift and a deep understanding of how to use that data inside business processes, they will continue to struggle."

I guess fast enough to grow or maintain dominate positions.

We can never be data driven. The important data is what customers will be willing to spend money on next year - we can only make guesses. Data can educate the guess somewhat, but it is still a guess. Will our competition come up with some not patent on a product that is so much better than us that we sell nothing - if so the right answer is for the CEO to lay me off now. Will our new product be enough better with expensive to develop feature X to be worth the price - we can do focus groups and the likes but it is still a guess in the end.

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