41–50 of 102 posts
This is an article making baseless generalizations that all data science is failing in Silicon Valley, and that the true need of a company is to hire a "data strategist." Unsurprisingly, the article is written by a self-proclaimed data strategist. This blog post would be more compelling if there were citations or even general context backing up the authors pretty wild claims ("the vast majority (we are talking 80-90%…
Maybe these people want to leave because they realize they aren't adding value? I've worked in many Fortune 500's with "data science" or "big data" teams. These are well staffed, very expensive teams that have large budgets for pricey hardware (sometimes on-prem, sometimes in the cloud) I have never seen one of these teams produce insights or actionable intelligence valued anywhere near their cost. I mean not even cl…
Maybe these people want to leave because they realize they aren't adding value? I've worked in many Fortune 500's with "data science" or "big data" teams. These are well staffed, very expensive teams that have large budgets for pricey hardware (sometimes on-prem, sometimes in the cloud) I have never seen one of these teams produce insights or actionable intelligence valued anywhere near their cost. I mean not even cl…
I've seen some customers of ours being quoted six and seven figure prices by their internal data science teams to essentially slurp and transform a handful of SQL tables. It's the kind of thing that shouldn't cost six or seven hours to write the code for.
As someone near graduation with a PhD in mathematics with research focusing on machine learning / image processing and beginning the job search, this didn't leave me feeling very optimistic. Is this a problem of not setting proper expectations for a role on the company's part or not fully understanding the expected duties of the position before hired on the data scientist's part?
Academics are rewarded for building complicated things, but it often takes graduate PhDs entering industry a long time to learn about quality of execution. I think that's the fundamental tension between business and academia.
While I certainly can see overhype in the space leading to team failure (and I have indeed seen that), I'm just surprised that the 10% of teams I've been on were so successful by comparison.
But one of the nasty things I see happening in the space is that often times really great data science folks, in studying a space, come up with a way to invalidate a lot of the business models. If a major player adopted these, there is a chance of success, but it's also a major risk (as it basically unseats the rest of the business).
As such, I think there is a lot of pressure for good data scientists to end up in small teams leading what we'd call 'highly disruptive' businesses. We see this in the finance sector (where it's least likely to succeed) all the time.
Maybe these people want to leave because they realize they aren't adding value? I've worked in many Fortune 500's with "data science" or "big data" teams. These are well staffed, very expensive teams that have large budgets for pricey hardware (sometimes on-prem, sometimes in the cloud) I have never seen one of these teams produce insights or actionable intelligence valued anywhere near their cost. I mean not even cl…
Dollars to donuts, this stuff always seems to be in that enterprise buzzword laden minefield of trendy make-work. There seems to be a tremendous amount of money and energy behind it for some reason, though. I've seen some customers of ours being quoted six and seven figure prices by their internal data science teams to essentially slurp and transform a handful of SQL tables. It's the kind of thing that shouldn't cost…
They don't wanna do it so they give a ridiculous estimate
Besides the demands of agile and corporate culture, is there other reasons why data science folks are leaving (if true)? For instance lack of cooperation, or not using the insights/recommendations?
Our data scientist left because instead of data science, he ended up doing more ETL and data engineering support than what he originally had signed up for. A full-time data engineer should probably have been hired. I wonder if this leads to an analogous discussion as http://wiki.c2.com/?ArchitectsDontCode .
Who would sign up if they told you up front that your main contributions would be shuffling email, filling out TPS reports, attending pointless meetings, and being roadblocked every time you tried to automate, eliminate, or improve that drudgery? You can smash your head against the wall trying to make things better, but all that ends up doing is pissing you off and giving you a headache.
It was all hype. All of it. I enjoyed learning about data science, but in the end, there were no jobs I could actually apply for and realistically get. I also don't think they were providing the big wins for the company that would justify what they were getting paid. Again, all hype.
The real issue is setting expectations with those on the outside trying to get in - a 3-month boot camp with no prior programming or statistics knowledge is just not going to get you to the level of competence required for this type of career. All of these MOOCs for ML/AI/DS are bullshit if you haven't the foundation underneath, which, based off of those people I know who've taken those courses, doesn't exist for many MOOCers.
The fact of the matter is that most companies don't need "artificial intelligence"; they need intelligence - people with domain and statistical knowledge and enough programming skills to be dangerous. As cool as some AI/ML tech is, you could get so far as a data scientist with great knowledge of a few statistical approaches and above-average programming skills.
You're right in one sense, though: there are a lot of data science practices/applications that aren't worth the value they provide. There are others that are worth exponentially more than their operating costs. As with most things in statistics, whether data science is effective "depends."
This is an article making baseless generalizations that all data science is failing in Silicon Valley, and that the true need of a company is to hire a "data strategist." Unsurprisingly, the article is written by a self-proclaimed data strategist. This blog post would be more compelling if there were citations or even general context backing up the authors pretty wild claims ("the vast majority (we are talking 80-90%…
What do you mean by "too academically focused" ? Developing new methods?
"I made a huge difference for the business by being thoughtful about feature selection and then applying a bog-standard regression method" isn't appealing to the academic mindset.