The opening article in this week’s reading list is a great reminder that even data infrastructure is about people. You can have the greatest infrastructure and tools but if users are not compelled that it’s solving their problems and if it’s not easy-to-use, it will always be bypassed and result in a patchwork of solutions.
This week’s list is — hopefully — ‘aggressively helpful’, fair and also a bit nostalgic.
I’ve read a lot about data infrastructure this week. Every time I dig a bit deeper, I’m amazed by the number of new tools. How can one keep up with all the development? And not even hands on, just knowing what’s out there?
Beyond that, an article about the importance of the CDO role and one about data literacy made it on the list today.
Data is increasingly important for business success. Companies are spending greatly on data infrastructure and data professionals alike. Yet, leading opinion-makers are warning that it might not be enough.
Data literacy — the ability to read, work with, analyse and communicate with data — is lacking in the majority of organisations. As a consequence, these organisations are not able to fully use the vital business resource of data to their business advantage. Leading voices are now advising on how to assess data literacy and how to boost it.
There are many initiatives aiming at training people to become data literate…
The week has brought some interesting news in the world of data. First, Belgian start-up Soda — building a data monitoring tool — has raised $11.5 million in the series A funding, which is great news for the growing data quality space. Likewise, Databricks closed their $1 billion round G resulting in a mind-boggling $28 billion valuation (with $425M ARR!!). And finally, the new CEO of Amazon will be the current head of AWS.
It is a pleasing reassurance of the importance of data on the scale of small start-ups, large tech companies plus the massive ones!
Moving on, what…
Predictably, most of the reading I did this week came through two sources: (1) what others were sharing (on LinkedIn, Twitter, newsletters or directly with me); and (2) what I read when preparing for work-related projects. I generally like to combine these because the first usually widens my horizons and the second deepens my understanding.
Can you tell, which article came from which source?
Chief data scientists are typically recruited from a pool of talented data scientists. And though the transition from talented to chief sounds like a small and logical step, the role is very different.
Data scientists are solving problems in the front line. Every day they are sharpening their technical, coding, problem-solving and communication skills. Some aspire to become chief data scientists. And when they finally get the position, they are amazed by how different the new role is.
Personally, I struggled with not building models, learning new technologies, studying new methods and trying them out all day, every day. I…
I saw the image above in a blog post by Sandeep Uttamchandani about examples of what can go wrong in a machine learning project. People often read these articles as funny anecdotes that can never happen to them. Machine learning projects usually require significant investment (talent, time, and technology). Accordingly, it would be a mistake to think that the probability of making a mistake is more important than what would be its consequences.
Also, I subscribed to yet another newsletter — Machine Learning Ops Roundup. Check it out if MLOps is of interest to you.
But now, let’s get into…
My week, apart from being extremely busy, was epitomised by data visualisations. Until this week, I’d found most of the visualisations I enjoyed either through The Big Picture, FiveThirtyEight or randomly. Whereas, I stumbled across the visualisation of 645 hours of watching series! It is noteworthy for two reasons. Firstly, it reminded me how lucky I have been with Covid-restrictions in Switzerland having spent almost the same time on my bike last year. But more importantly, it navigated me to new sources of great data visualisations — @VeryData_365, #MakeoverMonday and the whole community around it.
Other topics on this week’s…
Data is powering our economy. Businesses are using data to make better decisions, refine operations and create new revenue streams. Or are they?
Companies are certainly amassing vast amounts of data every day. Whilst the number (and variety) of data professionals on their payrolls is growing each year. Yet, data technology isn’t for free — as skyrocketing revenues from the large tech companies attest.
First week of the year. Everyone is back from holidays full of energy and ideas. People around me are querying which new technologies were identified as ‘to watch’ in 2021. And, everybody is launching their plans for the year ahead. I sincerely wish we will all be able to follow our intentions as much as possible and not get punched in the face by everyday reality too quickly.
But it will happen, as it does every year. So, don’t be surprised!
A bit of everything this week; enjoy the variety of topics or pick based on your interests.