Keeping Up With Data #91

5 minutes for 5 hours’ worth of reading

Adam Votava
4 min readJul 15, 2022

Why data matters? What a seemingly simple and innocent question. I’ve recently read an article by CMO of Teradata with this question in the title. And it got me thinking.

Us, in the data industry, often jump over this question, because it’s too obvious to us. Data matters because it matters to us. It’s our living. But certainly it is not the case for everyone, which makes the question very relevant.

So, what is the answer? When asked, some start throwing around statistics about growing volume (and other ‘V’s) of data. Some, often technology providers, revert to increasing adoption of various technologies. And big consultants revert to infinite number of use cases (playing on the fear of missing out). Others point to the expanding value of data as an asset — using images like the one above.

I think the key to the answer lies in understanding who is asking. All the technologies, use cases, quick solutions, recipes, and statistics containing very large numbers are great, but they are generic. They don’t talk about why data matters to the inquirer!

In my mind, the answer should play into the uniqueness of data to individual businesses and organisations. It’s their data, their insights. Not something available to their competitors. They can use their data to make their decisions better. To improve efficiency of their operations. To create new revenue streams for them.

Increasingly more executives understand that data can provide great value, they see and read it everywhere. Especially after Covid-19 (there is always a silver lining), which was a global crash course on data literacy — understanding charts, statistics, biases in data and algorithms, impact of assumptions, data quality and integrity. Plus the impact of ever-growing digitalisation, and preference towards self-service (what else when everyone was at WFH).

So yes, data is growing. Yes, it has a lot of value (trillions of dollars if that tells you anything). Yes, there are cool technologies and smart use cases. But it’s also yours.

And that gives every organisation an opportunity to either use this unique and valuable asset to their advantage. Or not, and hope that it won’t impact their P&L and valuation.

To quote Martyn Etherington — the author of Why data matters article: “The C-suite can no longer view data as an afterthought. It’s a business asset and should be prioritized as highly as revenue, customer experience and profitability.”

What else have I read that I wanted to keep for later? Articles about CDAOs, analytics used in education, and why becoming data-driven is so hard. Enjoy!

  • The Two Types Of Chief Data Analytics Officers: Should your organisation appoint a self-made CDAO or formally trained analytics professional? Both have their advantages. The self-made CDAOs coming from other business functions often excel in combination of critical thinking, business acumen, and solid understanding of problem-solving and decision-making using data. When they can work with formally-trained data experts, they got what’s needed for the job. Similarly, the data leaders who climbed through the ranks of data science also have their advantages. And if they develop leadership skills and learn how to bring value to the business, they too can be excellent CDAOs. The morale is, as often, that it’s not either/or, but and — combining the best of both worlds. (Forbes)
  • Colleges are using big data to track students in an effort to boost graduation rates, but it comes at a cost: An exciting article about data and analytics helping students and universities on the path to graduation. Algorithms could detect early signs of possible drop outs enabling universities to guide students to prevent that from happening. That sounds like a win-win, right? Students get their degree, and universities the tuition. But every time there is an algorithm making decisions about people’s lives we need to be incredibly cautious. What is the data used, how are the models trained, what are the biases in the training data, and who is ultimately making the decisions. (The Hechinger Report)
  • Why becoming a data-driven organization is so hard? Well, it’s not due to the lack of data or technology. Becoming data-driven comes down to the ability of people and organisations to adapt to change. We all know how hard business transformation and culture change is. Now there are new cultural dynamics shaping the efforts of becoming data-driven: Covid-19 pandemic raising awareness of the importance of data, and rise of the self-service when individuals consume information and data when they want and how. But worry not, we can learn from driving principles of successful data-driven companies. These think differently, fail fast (and learn faster), and focus on the long-term. Becoming data-driven is a process and perfect shouldn’t be the enemy of good. (HBR)

Beyond the world of data, we’ve seen an absolutely fantastic week at the Tour de France. I find their abilities — both physical and mental — absolutely amazing. What an inspiration for us MAMILs!

In case you missed the last week’s issue of Keeping up with data

Thanks for reading!

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Adam Votava

Data scientist | avid cyclist | amateur pianist (I'm sharing my personal opinion and experience, which should not to be considered professional advice)