Keeping Up With Data — Week 42 Reading List
5 minutes for 5 hours’ worth of reading
The first Keeping Up With Data was published on October 23, 2020. Which means that today marks a year of reading hundreds of data articles, writing 53 weekly reading lists, and 13 opinion pieces. It has been an incredibly valuable journey, in which I’ve pushed myself to develop a strong reading habit and overcame a writing anxiety.
All the reading has been constantly forming my data philosophy. And the Keeping Up With Data has created me a fantastic library of articles I’m frequently coming back to. If the Medium statistics are not lying, there seem to be some people who find it valuable too.
But enough with sentimentality, and let’s get into the articles.
- Data Ethics: What, Why Now, and Where Do We Start? Data is an incredibly valuable asset. Which is why many people are pondering how to realise the value of data — being very creative with data collection and usage. What we shouldn’t forget during the process is to stop and ask: “Should we be doing this and how should we be doing this?” The CDO authority Peter Jackson comes with three advices when it comes to data ethics: rethink the data collection, reassess how we use algorithms, and take a customer-centric approach to data ethics. And remember: “Just because we can, doesn’t mean we should.” (Dataversity)
- Data Observability 101: Everything You Need to Know to Get Started: Data observability is about solving for the unknown unknowns when it comes to data quality. Things you can’t write tests for upfront. Together with data tests, data observability is aiming to increase company’s trust in data. In my mind, your business context defines the data you need, you then start to collect it, and process it. And while doing that you need to ensure that everything works as intended. As assumed. As expected. And if not, you can identify and fix the problem quickly. To limit the data downtime. (Monte Carlo)
- Data’s Secret Sauce: Should the analyses be shared like recipes or treasured like a secret sauce in your grandma’s pie? Doesn’t a rising tide lift all boats? And just like with a family recipe — that often came from an old cookbook before being attributed to your grandma — a secret sauce of a business’ data analytics often came from a publicly available article, a book, or an scientific paper. And it was the application to the business that made it so special to them. “Sharing success vaguely” gives others a chance to imitate and possibly also find success. It’s not the recipe that will make people fall in love with the analysis. It’s the application to their context that will do that. (Ray Data Co.)
- Mastering the Data Economic Multiplier Effect and Marginal Propensity to Reuse: One of the most amazing properties of data as an asset is its reusability – the same data set can be used in multiple use cases. And it is the reusability that is a key driver behind the Economic Multiplier Effect for data. Companies should therefore encourage people not only to use data as much as possible, but more importantly, to reuse it. Because as Bill puts it: “Anything that prevents the reuse and refinement of the data and analytic assets is destroying the economic value of data.” So, let’s break down data silos, eliminate orphaned analytics, and take a strategic approach to data! (Bill Schmarzo)
Here is to another year of Keeping Up With Data! Crystal Widjaja once wrote that it’s a “common mistake [to view] data as a team to hire or set of tools to implement rather than as a strategic lever for growth”. And this strategic lever for growth should be studied from various angles with proper attention!
