Today’s reading list will be very short, I’m afraid, as I’m down in bed with Covid and have a horrible headache preventing me from reading anything.
Data science is about solving business problems with data and analytics. This has been my mantra for many years. It’s probably a confirmation bias that the articles below are reiterating that. Be it at the level of a company tightly aligning the data strategy to the business strategy. Or individual data scientists being obsessed with solving business problems.
Either way, I hope you’ll find the following articles inspiring. Or thought provoking at least.
Many years ago, I asked myself ‘how do sales people read dashboards’ — in order to design new dashboards around their subconscious behaviours. And when I didn’t find the answers, I asked experts from leading BI platforms. They were confused by my questions and responded with something along the lines of “you can build whatever you want; it depends on what you want”. Which isn’t exactly what I was looking for either. So, I changed tack and asked sales people ‘what are the questions you need answers to?’ …
Giro d’Italia is one of my favourite cycling races. Maybe because we used to take a couple of days off, pack the bikes and drive to the Italian Alps to try compare our climbing abilities with the pros on the same roads. We didn’t pay much attention to data. We barely planned the routes, ignored the distance and the vertical gain. But now data is everywhere. You can see many real-time stats of the riders at the Giro. Just like NHL coaches can track the data about their players. …
This week has been full of challenges. How to forecast demand in the post-covid times? How to automate quality control in increasingly complex manufacturing processes? How to compel front-line workers with the power of data? I wish I were a ‘multiprocessor’ like Bill Gates!
You won’t be surprised that this week’s articles are tilted towards the questions posed above.
Many companies want to ‘start with data’ properly. So, they wait for the perfect moment — when new system is implemented, more data is collected, a team is hired, transformation is finished and so on. Unfortunately, the perfect moment never comes. Just as in life. I typically suggest: just do it. Start with a data strategy built around the imperfections rather than keep waiting forever. But the first step is always the hardest.
To make is a bit easier, check out some of the articles that caught my attention this week.
Artifitial Intelligence is making more and more decisions in our lives, so naturally we need to make sure these systems are making the right decisions for the right reasons. Especially in the high-stakes decisions related to lives, health, or large amounts of money. That’s why explainable AI is important. The cheat sheet above is from Jay Alammar and it’s accompanied with a short video that provides a fantastic overview in just 15 minutes.
And if you have another 15 minutes, I recommend you reading the following three articles.
I was very happy to read about two Czech tech companies in the news this week. First, GoodData moved into the Data as a Service space with the announcement of its cloud-native analytics platform GoodData.CN. And then the news about ProductBoard raising $72 million in a series C arrived yesterday. Well done to both!
The image above comes from an article about feature stores. Moving from batch to real-time brings many challenges. One of the most painful ones is the feature engineering. Making sure features used for training a model are the same as the ones used for scoring in the real-time has caused a lot of grey hair. My business is facing this problem in one of our current assignments, I certainly hope we’ll solve it without too much stress.
A bit of a ‘pop science’ reading list with a very high frequency of the word “AI”. But it includes strawberries too.
Who wouldn’t want to listen to new songs of Jimmy Hendrix, Kurt Cobain, Jim Morrison or Amy Winehouse? Thanks to the Lost Tapes of the 27 Club it is now possible. Well, actually the songs were not written by these amazing musicians who all died at the age of 27. AI algorithm generated a string of all-new hooks, rhythms, melodies, and lyrics, which were used by audio engineers to ‘compose’ the final songs.
Hope the following reading list will offer a decent lick to finish their new solos!