They’ve got data marts like gold, and ours are a little more like stone.
Benn. You did not have to come for me like this. It's not OK to attack your friends this way! 😅
1) I think you're spot on about the people & process over tools, however, tools shape the process. Just like Conway's law is that you "ship the org chart," sometimes what you ship creates that org chart. Clothes make the man and tools make the analytics?
2) I think you are making a fallacy regarding the data that Netflix, AirBnB, or Facebook have. Don't forget that at some point in the past, Netflix was a DVD by mail subscription service, AirBnb was some dudes with an air mattress, and Facebook was a way to rank girls' hotness. They didn't always have that volume of data. They used good product insights to build and grow, used data to get bigger and grow faster. In other words, your earlier post about product > data is right, regardless of what your readers say. It's just that data makes for better products if you know how to use it.
3) Knowing how to use data means _wanting_ to use data. You gotta believe there's some "there" there, first. Everything else follows from that.
4) If tools make it easier to understand data, warts and all, does that make it easier for the business to know there's value in data?
5) Why doesn't _everyone_ feel the way you do about Pinterest, I'll never know. Literally every piece of content on the platform is _some_ form of advertisement for _something_. It's a tap straight into the desire center of the consumer brain. Quite possibly the most valuable data set in history, and yet most analysts are like "yeah yeah, it's a social media site." Infuriating.
6) Maybe FB, AirBnb, Uber, etc were just _lucky_ phenomenally, inconceivably, irrationally, mind-bogglingly _lucky_
Interesting, but if you had that magic dataset about your gas station customers and you couldn't figure out how to leverage the information to make more money, that is just bad business...
1) Sell more than just gas; "cross-sell" into other stuff your customers need while pumping gas. Food, drinks, car repairs, car wash, propane tanks for bbq, etc, etc. If the data can point you to the which of those ancillary goods and services would be best without trial-and-error, that would be amazing.
2) Retain customers by making the experience better for them; if the data can tell which improvements would cause customers to come back or what is causing them to never back come again, that would also be amazing. Are you really are loosing business because your pumps are too slow? because the handles are sticky? because your station smells like sewage?
But the more valuable dataset would probably be on your competition:
- what are their operational costs? margins?
- how long do you need to squeeze them on price before they shut down and you get all the traffic?
How many businesses should have data teams is a pretty close inverse to how many businesses you think look like perfect competition in econ 101.
context is the only prison that punishes when you leave its walls.
I most of all take this as a warning / wake up call / way to communicate for those that think you should do as the big tech do. If you have some physical product and you don't have a customer base like Wallmart you may not be the type of company to build your business around the data. Meaning that you need to be careful in what you spend to get your data right. While this is true for the tooling end, maybe on the governance end your challenges are greater than those of the big tech, for with the little data you have you need it to be as accurate as can be.
New subscriber here but loved the gas station analogy. Thanks for sharing it.
As an industry our spending controls just stink, though. There just isn’t enough pressure to be good at it yet, is my guess, plus the linked tweet is dead on. We have a lot of conflict averse people who try to solve people problems with tools.
Yes! I’ve often wondered how many data requests are an Oracle-of-Delphi-like fig leaf to make us feel better about impossibly uncertain decisions.
Maybe if we had the guts to admit we always need to make decisions based on imperfect information, we could focus our energy on the marginal cases where there is genuine return-on-data-quality, rather than stressing about stuff that doesn’t actually move the needle.
This week I joined a data Slack group.
It took all of 3 days for it to devolve into influencers and vendors peddling various ideas.
The high water mark was a dbt analytics engineering data influencer who sells SQL books and SQL courses and the vendors put him on their webinars and such, and he is now working on a new product he and his business partner are pitching, which they started drumming up and hyping up in the Slack, as they do on the socials.
However, in the #general channel the very same dbt influencer was crowd sourcing answers about how to do a self-join in SQL, and he was fanning out expensive compute in loops. SQL 101, or if not 101, 102.
That's why this will all fail and it already is. Everyone is an influencer. Everyone just changed their job title.
Low interest rate data teams and gigantic stacks of tables, tables, tables and product and people are over.
The YAGNI data stack is in vogue.