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>(1) If you're going to hop on the 'hot' AI/ML/DS bandwagon, make sure that you actually have 'work' for the highly coveted person you are trying to hire. 'Ever

by apohn 8y ago

>(1) If you're going to hop on the 'hot' AI/ML/DS bandwagon, make sure that you actually have 'work' for the highly coveted person you are trying to hire. 'Everyone else is doing it', 'all our clients have data, so selling them data-sciency projects shoudn't be a problem', 'Hire a first DS person, to have them start a brand new DS division' are things that probably are doomed to fail. I'd add that if you have all these conditions and still want some hope of getting value from your Data Scientists, give your data scientist(s) a period of time (6 - 12 months) where they can discover how they can provide business value. Accept that during that period they may not provide the value you are hoping for. Support them if they recommend changes (e.g. different ways of collecting or processing data) Lots of middle/upper managers want to hire a team of Data Scientists and want them them to deliver high business value from day one. They want a magic black box to solve their business problems. This results in exactly what you describe - constant fires, games, gimmicks (that look good on PowerPoint), and good people eventually quitting or getting fired. On the other hand, if you give them 6-12 months to interact with different teams and identify relevant business problems, they'll find ways to provide value. Or, worst case scenario, they'll tell you (maybe by all quitting) that your problems aren't data science problems and the money you are spending on them is best invested elsewhere.

crispyambulance 8y ago
> if you give them 6-12 months to interact with different teams and identify relevant business problems... I fully agree with your points BUT 6-12 months of self-directed, open-ended discovery just ain't gonna happen. This isn't the era of Bell-Labs, this is the era of PMP-certified project managers. In many places the "data scientist" will be dropped into a project and expected to start producing "deliverables", outcomes will be a mixed bag. A more organic approach would be for teams to start picking up tools and skills for basic data analysis. We used to call it "stats". Doesn't have to be cutting-edge ML stuff to provide value and new opportunities for the practitioners. Of course whatever they start doing, HR and management will pump it up: mining a few hundred megs of csv files all of sudden will get called "big data". Something similar happened ~10 years ago with the advent of "BI" (Business Intelligence). It was nothing more than folks realizing that their corporate databases have useful stuff in them and they could query them in an ad-hoc fashion _outside_ of the applications that typically use them to provide "business value".
- apohn 8y ago
  >A more organic approach would be for teams to start picking up tools and skills for basic data analysis. We used to call it "stats". Doesn't have to be cutting-edge ML stuff to provide value and new opportunities for the practitioners. Of course whatever they start doing, HR and management will pump it up: mining a few hundred megs of csv files all of sudden will get called "big data". Here's my one caveat to this based on personal experience. This happens to be why an entire data science team in one division of a large company quit over the course of a year. If middle/upper management is purely focused on "show value on day one", it's very easy to get stuck in a position where basic stats and building Dashboards for simple data mining becomes the gold standard for what a Data Science team is for. Management develops an addiction to fast data mining projects that look good in PowerPoint, but don't actually deliver much business value because nobody is actually measuring business impact outside of how impressive it sounds. When the Data Science team tries to focus on projects that require more work that deliver more value, they're shot down because they don't fit into the "monthly management update" mentality. You need a good management structure that realizes that quick data mining exercises are a stepping stone to delivering longer term value. They are not the ultimate goal.
- bertil 8y ago
  > This isn't the era of Bell-Labs, this is the era of PMP-certified project managers. In many places the "data scientist" will be dropped into a project and expected to start producing "deliverables", outcomes will be a mixed bag. I have been on the receiving end of that argument a lot. Given the efficacy of well-thought ML, I suspect that competition will make those companies less durable than they seem to expect.

by apohn 8y ago

crispyambulance 8y ago

> if you give them 6-12 months to interact with different teams and identify relevant business problems... I fully agree with your points BUT 6-12 months of self-directed, open-ended discovery just ain't gonna happen. This isn't the era of Bell-Labs, this is the era of PMP-certified project managers. In many places the "data scientist" will be dropped into a project and expected to start producing "deliverables", outcomes will be a mixed bag. A more organic approach would be for teams to start picking up tools and skills for basic data analysis. We used to call it "stats". Doesn't have to be cutting-edge ML stuff to provide value and new opportunities for the practitioners. Of course whatever they start doing, HR and management will pump it up: mining a few hundred megs of csv files all of sudden will get called "big data". Something similar happened ~10 years ago with the advent of "BI" (Business Intelligence). It was nothing more than folks realizing that their corporate databases have useful stuff in them and they could query them in an ad-hoc fashion _outside_ of the applications that typically use them to provide "business value".

apohn 8y ago
>A more organic approach would be for teams to start picking up tools and skills for basic data analysis. We used to call it "stats". Doesn't have to be cutting-edge ML stuff to provide value and new opportunities for the practitioners. Of course whatever they start doing, HR and management will pump it up: mining a few hundred megs of csv files all of sudden will get called "big data". Here's my one caveat to this based on personal experience. This happens to be why an entire data science team in one division of a large company quit over the course of a year. If middle/upper management is purely focused on "show value on day one", it's very easy to get stuck in a position where basic stats and building Dashboards for simple data mining becomes the gold standard for what a Data Science team is for. Management develops an addiction to fast data mining projects that look good in PowerPoint, but don't actually deliver much business value because nobody is actually measuring business impact outside of how impressive it sounds. When the Data Science team tries to focus on projects that require more work that deliver more value, they're shot down because they don't fit into the "monthly management update" mentality. You need a good management structure that realizes that quick data mining exercises are a stepping stone to delivering longer term value. They are not the ultimate goal.
bertil 8y ago
> This isn't the era of Bell-Labs, this is the era of PMP-certified project managers. In many places the "data scientist" will be dropped into a project and expected to start producing "deliverables", outcomes will be a mixed bag. I have been on the receiving end of that argument a lot. Given the efficacy of well-thought ML, I suspect that competition will make those companies less durable than they seem to expect.