Keeping Up With Data #56
5 minutes for 5 hours’ worth of reading
I’ve been in Prague for a conference this week. It was great to see how the local data community is growing and flourishing. There were many great presentations and a panel discussion at the end. The first question was, ‘if data were a person, how old this person is?’ Having the whole 2 seconds to answer that, I said a 15-year-old teenager. Data industry is still in the forming years — developing infrastructure, tools, processes as well as ethics and code of conduct. We are seeing many mistakes and promissing signs of productivity too. Well, let’s hope that data will turn into an incredibly productive and efficient grown-up person with strong morals soon.
Two main topics are on the menu — advices to data newbies and ROI of data work.
- What I’ve learnt moving from a Product Manager to a Data Scientist role in big tech: Many people are switching to a career in data. At least one of them came from product management role. Product management environment teaches people to understand the business really well, be able to focus limited resources on the key features, say no to everything else, and be transparent about obstacles. New data scientists could benefit from these immensely. The advice they’re being given is: “Find a way to do as little work as possible, in the right direction, but exceptionally well.” (Rian van den Ander @ Medium)
- Tough Love for Naïve Data Newbies: Staying on the topic of advising data newbies, here is another take (from yet another — a bit more famous — South African). I won’t lie: I often struggle with the Cassie’s (esoterically geeky) story-telling style. But the content of this article is so strongly resonating with me that I recommend to read it to everyone, not only data newbies. As data professionals, we are dealing with lot of ambiguity, wealth of assumptions, and complex solutions that are difficult to be thoroughly inspected. There isn’t a chance to tame all this. We need to be humble. And we shouldn’t fear to admit what we don’t know. (Or as Cassie puts it: “Don’t be afraid to say: ‘I. Don’t. Know.’”) (Cassie Kozyrkov @ TDS)
- How to think about the ROI of data work: I typically think of ROI of data work at a level of data strategy, data initiative, or at least a data project. But sometimes (especially when defending corporate budgets and head-counts) we need to go to a level of individual people. Mikkel explains that some data roles are closer to making a direct business impact than others (see the image above). Because of that the ROI has to be calculated differently for each role. Though the impact of data engineers is very indirect it can be a huge lever if they make others more productive. In my opinion, the concept is incredibly helpful when managing the team. For ROI, I’d suggest to stick to a higher level and not go down to individuals. In fact, data is a team work. (Mikkel Dengsøe)
That data is still a naughty teenager has been recently demonstrated by what happened at Zillow. With more businesses embarking on data journey, it’s a timely warning that data projects not making it into production isn’t the only risk. As Vin Vashishta puts it: “Businesses have put off making the jump but now understand the imperative. The next 3 years will see more Zillow like stories but unlike Zillow, those stories will end with the business in bankruptcy.” Well, I’m afraid these are the life lessons needed when data is growing up.
