Data, data, data. Everywhere we look, right? And the trend is accelerating as we can see in the image above. It is worth opening the infographic — as it includes charts speaking to all sorts of aspects of data: Is technology a bigger issue than people/culture when becoming data-driven? What are the most commonly reported benefits of using data effectively? Or what share of front-line workers don’t have access to data and analytics?
The upward trend of data has also been demonstrated by a massive Big Data LDN conference — with a ‘join the data revolution’ tagline — taking place…
It was demonstrated almost ten years ago, that data-driven decision making is superior to HiPPO. Since then, being data-driven has become an ambition for many companies and individuals alike.
“Companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors.” — Andrew McAfee and Erik Brynjolfsson
In order to be able to make data-driven decisions, one requires a certain level of data literacy, though I prefer the term: data fluency. One needs to be able to think in data.
But don’t be mistaken…
This week has been a beautiful demonstration of the sheer variety of the data world! There is such a broad spectrum of puzzles one can face in a data career — how to strategically leverage data to create business value? What data stack to go for? Who to partner with? How to compel people about the value of data? I must admit, I absolutely love this unpredictable nature of data-related challenges.
While contemplating on some of the questions mentioned above, I enjoyed reading the following articles this week.
Data is finding its way into private equity. Investors — just like their business executive counterparts — are viewing data as a strategic asset that can drive value creation. In fact, some private equity fund managers have gone even further, establishing their own data science functions.
One such example is HgCapital. Their ~30-strong data analytics team is supporting the particularly ‘data-rich’ software and service businesses within their portfolio by making data science and technical skills and tools widely available.
And, as many people in the private equity space have noticed, it pays off. Hg’s case study of using data science…
What drew my attention to the image above was the latest Analytics Engineering Roundup. Having built a couple of data teams myself, I had to chuckle (a few times) when I saw this Emilie Schario’s tweet.
I haven’t had as much time for reading this week as I normally do — for family reasons 🤰 ➡👶! But there were still a couple of articles that I want to highlight.
No company likes to lose valuable customers. In the beginning, a company typically focuses on acquiring new clients, then grows by offering additional products to existing clients or trying to get them to use their products more.
If all is going well, there comes a point when the company is large enough that it must also choose a slightly more defensive strategy and focus on retaining existing customers. Despite the best user experience, there will always be a group of clients who are not satisfied and decide to leave.
The company then faces the problem of how to prevent these…
Gartner’s hype cycle for data science and ML, shown above, brings plenty of terms we’ve been hearing for a while and couple of new ones, too. Gartner is often coining or popularising new terms, some of which are understandable — like ‘small and wide data’ — others need to be constantly googled (at least by me)— like ‘citizen data science’ or ‘X analytics’. Another I find slightly confusing is the co-existence of ‘MLOps’ and ‘ModelOps’ in the picture. …
Countless companies are embarking on a data journey. And increasingly they — correctly — start by designing a data strategy. But even with a great plan and positive intentions, success is not certain.
One of the reasons why data initiatives are failing is the lack of data adoption across the organisation. Adoption needs to spread from the C-suite to individuals at all levels — all the way to the front-line.
If adoption is low among the C-suite, the data strategy often fails at the first roadblock. …
This week I had an opportunity to talk on a webinar about data adoption from the front line to the C-suite organised by my friends from Keboola. We spoke a lot about the importance of using data for solving the right business problems, building a digital twin of the business, ensuring the quality and reliability of data, driving data fluency and designing self-service analytics around the business users. There was also a demo of ThoughtSpot platform and it looks absolutely amazing!
Apart from preparing for the webinar and catching up on writing some longer data blog pieces (stay tuned!), …
The image above is from histography.io — a website based on Wikipedia data and visualised in an appealing, interactive way. If you find yourself occasionally browsing through Wikipedia without any particular goal — like I do — you will enjoy the histography website — like I have.
I’m still staying on the topics I’ve been reading on in the last couple of weeks — data literacy and self-service. These are accompanied by data mastering in this week’s reading list.