Keeping Up With Data #78

5 minutes for 5 hours’ worth of reading

Adam Votava
3 min readApr 15, 2022

“We have many ideas for how data and analytics can help our company, but the business is not ready yet.” I’m often hearing this from data teams. It could well be an objective fact. I give them that. But it could also be an excuse. Showing the value of data to the leadership is a job of data leaders. They need to step up and evangelise. And being an evangelist is not about having many great ideas and complaining that nobody wants to hear them. It’s about taking responsibility, making a move, and start compelling others.

If I hadn’t enjoyed reading an article written more than 50 years ago this week, I would have said the pieces below come from the archives of the internet. But that doesn’t make them any less relevant.

  • Data Analysis: The Hard Parts: A view on data science (the article says data analysis, but it’s mostly speaking about predictions) from 2014. Many things has changed, but the main idea imho still holds. No matter how good the tools, data analysis is hard. Why? 1) Data analysis so easy to get wrong; 2) it’s too easy to lie to yourself about it working; 3) it’s very hard to tell whether it could work if it doesn’t; and 4) there is no free lunch. The tools are constantly making the life of data analysts easier (more boring?) and wider-spread. But making a meaningful impact with data and analytics is still hard. (Marginally Interesting)
  • The Sobering Truth about the Impact of your Business Ideas: In the beginning there is a business idea — e.g., “let’s lower prices to increase demand by 10%”. But very few companies are rigorously quantifying the impact of an implemented solution. When they do, they typically find out that a vast majority of business ideas fail to generate a positive impact. But because they don’t measure it, they can’t fix it. We have to admit, that “it is unlikely that companies will increase the success rate for their business ideas.” And that’s why it’s important to focus on building a culture of experimentation. We should be using data not only in decision making, but also in decision evaluation. PDCA! (MultiThreaded)
  • How retailers can drive profitable growth through dynamic pricing: “Sales growth of 2 to 5 percent and increases of 5 to 10 percent in margins, along with higher levels of customer satisfaction through improved price perception on the most competitive items,” could be the benefits of dynamic pricing according to McKinsey. I’ve only worked on a very few dynamic pricing projects but I do remember them for their difficulty. The idea behind the dynamic pricing is simple — you flexibly change the price based on current market demands. The complexity lies in two things — you need to build different pricing modules for different product life-cycle stages, and involve pricing experts in the design of the solution and even let them overwrite the recommendations. These were my lessons learnt. Now confirmed by McKinsey’s eloquent article. (McKinsey)

Happy Easter!

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

Thanks for reading!

Please feel free to share your thoughts or reading tips in the comments.

Follow me on Medium, LinkedIn and Twitter.



Adam Votava

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