[blaird]

Curious if there is a correlation with companies that failed to capitalize with the ones who relied on consultants versus really reshaping their own people.

I worked for a financial services co that saw massive gains from big data/ML/AWS. Given, we were already using statistical models for everything, we just now could build more powerful features, more complex models, and move many things to more-real time, with more frequent retrains/deploys bc of cloud.

I do agree that companies who don't already recognize the value of their data and maybe rely on a consultant to tell them what to do might not be in the position to really capitalize on it and would just be throwing money after the shiny object. It really does take a huge overhaul sometimes. We retooled all of our job families from analysts/statisticians to data engineers and scientists and hired a ton of new people

[apohn]

>Curious if there is a correlation with companies that failed to capitalize with the ones who relied on consultants versus really reshaping their own people.

I've worked in Data Science customers facing roles for 2 companies, and one anecdotal correlation between success with Stats/ML/AI I've seen is how "Data Driven" people really are for their daily decision making. The more data driven you are, the more likely you are to identify a problem that can actually be improved by an Stat/ML/AI algorithm. This is because you really understand your data and the value you can get from it.

Everybody has metrics, KPIs, OKS, etc, but the reality is that there's a spectrum from 100% gut to 100% data driven. And a lot of people are on the gut side of things while thinking (or claiming they are) they are on the data side.

I'll provide an example. I currently work for a company that sells to (among others) companies working with industrial machinery. If your industrial machine runs in a remote area (e.g. an Oil Field), then any question about that machine starts with pulling up data. Being data driven is the only way to figure out what's going on. These folks have a good sense for identifying the value they can get from their data and they usually understand when you say dealing with their data is a engineering task in itself.

The other side of this is a factory filled with people. Since somebody is always operating and watching the machine, the "data driven" part is mainly alarms (e.g. is my temp over 100C) and some external KPI (e.g. a quality measurement). They are much less data driven than they think they are, and a lot of them don't understand what value they could get out of their data beyond some simple stuff you don't really need ML/AI for.

I mention industrial equipment because I think a lot of people (even me) are really surprised when they hear about people working in factories not being super data driven. You think of factories, engineering, and data as being very lumped together. It's amazing how many areas (sales, marketing, HR, are other great examples) exist where people aren't as data driven as they think they are.

[blaird]

Yep, agreed. If decisions can be made by a human often they'll stick to that, often arguing there is no need for data.

In my former space (credit card fraud detection and underwriting), you obviously need a data driven solution. Without even considering latency requirements, you aren't do 6-10B manual decisions/year. The rationale for a more complex ML approach is easier to prove the ROI for, given the need is already there, just with an inferior technical solution.