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Anecdotally[1] speaking, you're correct but you're missing the message. You're correct that no one should hire expensive data scientists for this. But what happ

by cosmie 9y ago

Anecdotally[1] speaking, you're correct but you're missing the message. You're correct that no one should hire expensive data scientists for this. But what happens is that there's no marketing against these sorts of pragmatic best practices, so it never comes on the radar of business executives making decisions. Instead they're inundated with ML/AI/Data Science pitches and mentions everywhere. And so when they go to invest in improvements like this, they reach towards the buzzwords they know instead of the solutions they're not aware of. What ends up happening is effectively the Data Science engagement becomes 90% data cleaning, a handful of SQL statements that should have existed beforehand but never did because the data infrastructure wasn't there, and possibly a veneer of ML/AI just to say it was used. Clients come out happy (sometimes), despite overpaying for what was a much more basic engagement than they think it was, and they go on preaching to their business exec friends the virtue of ML/AI and the cycle continues. [1] I built up a Business Intelligence/Analytics team at my last job, and currently work for a marketing agency managing digital analytics for Fortune 50 clients. Lots of exposure to analytics in lots of varying environments, and I've seen firsthand how ML engagements get pitched and results get presented. I also own a consultancy that's the anti-version of this phenomenon, offering digital analytics management and support services. 50% of my work involves being a knowledgeable resource for marketing and business execs to lean on to cut through the bullshit. With most of the rest being basic Google Analytics/Google Tag Manager management, CrazyEgg, and drip marketing campaigns. All of which seems like AI-level magic for clients when done correctly.

xamuel 9y ago
Interesting comment, makes me wonder if people just generally underperform. You hire an "AI" team with multiple PhDs, you get work that a DBA could've done. You hire a DBA, you get work that a PHP intern could've done. You hire a PHP intern, you get work a kid could've done with Excel formulas :P
- cosmie 9y ago
  It's less about over/under-performance and more with incentive alignment and political perception, and organizational maturity. It's really, really hard to get executive budgetary approval for a foundational data audit/cleaning project (comprehensive data cataloging, data cleaning, source auditing and validation, etc). Doing so implicitly admits that you weren't doing that before, and now you have to pay gobs of money to fix it. The larger the company, the more infeasible it is to push this through because of the breadth of technical/analytical debt that has accrued and the price tag associated with the project, combined with the perception of incompetency (i.e. it's an expensive project that's fixing a problem you as the executive shouldn't have let happen to begin with). Whereas an ML/AI project/initiative/push is a net new capability that you're spearheading, and it's easier to get the political traction to spend money on net new things, especially buzzwordy net new things that the firm can use to be viewed as cutting edge. The fact that you're rolling up the cost of a complete data management audit to be able to even do the ML/AI project is a minor bullet-point that doesn't matter. Executive expectation is that anything ML/AI/new-age-techy is going to be astronomically expensive anyway, so it doesn't get noticed that they're paying a premium on labor to do it as a combined project rather than as two separate projects. Effectively, the foundational work that's needed to support ML work is also the work that's needed to do basic SQL-based analytic work, but it's way easier to get that budgetary line approved in a flashy ML project, even if you're paying a premium by having the ML/AI firm do the foundational work instead of the specialist work. Plus, spearheading ML/AI initiatives make for better resume points than "data management initiatives". So there's little reason for anyone in this process to attempt to change anything, unless you happen to materially benefit from a firm's profit. For this reason, my main consulting clients are bootstrapped firms that actually care about being pragmatic over being trendsetters. Note: This is a huge generalization, and doesn't apply universally. But it's far more common than you would expect, especially as you veer away from the type of companies that pop up on HN towards more traditional industries.

by cosmie 9y ago

xamuel 9y ago

Interesting comment, makes me wonder if people just generally underperform. You hire an "AI" team with multiple PhDs, you get work that a DBA could've done. You hire a DBA, you get work that a PHP intern could've done. You hire a PHP intern, you get work a kid could've done with Excel formulas :P

cosmie 9y ago
It's less about over/under-performance and more with incentive alignment and political perception, and organizational maturity. It's really, really hard to get executive budgetary approval for a foundational data audit/cleaning project (comprehensive data cataloging, data cleaning, source auditing and validation, etc). Doing so implicitly admits that you weren't doing that before, and now you have to pay gobs of money to fix it. The larger the company, the more infeasible it is to push this through because of the breadth of technical/analytical debt that has accrued and the price tag associated with the project, combined with the perception of incompetency (i.e. it's an expensive project that's fixing a problem you as the executive shouldn't have let happen to begin with). Whereas an ML/AI project/initiative/push is a net new capability that you're spearheading, and it's easier to get the political traction to spend money on net new things, especially buzzwordy net new things that the firm can use to be viewed as cutting edge. The fact that you're rolling up the cost of a complete data management audit to be able to even do the ML/AI project is a minor bullet-point that doesn't matter. Executive expectation is that anything ML/AI/new-age-techy is going to be astronomically expensive anyway, so it doesn't get noticed that they're paying a premium on labor to do it as a combined project rather than as two separate projects. Effectively, the foundational work that's needed to support ML work is also the work that's needed to do basic SQL-based analytic work, but it's way easier to get that budgetary line approved in a flashy ML project, even if you're paying a premium by having the ML/AI firm do the foundational work instead of the specialist work. Plus, spearheading ML/AI initiatives make for better resume points than "data management initiatives". So there's little reason for anyone in this process to attempt to change anything, unless you happen to materially benefit from a firm's profit. For this reason, my main consulting clients are bootstrapped firms that actually care about being pragmatic over being trendsetters. Note: This is a huge generalization, and doesn't apply universally. But it's far more common than you would expect, especially as you veer away from the type of companies that pop up on HN towards more traditional industries.