Keeping Up With Data #115

5 minutes for 5 hours’ worth of reading

Adam Votava
Data Diligence

--

Source: https://www.datasciencecentral.com/datastrategist-datascience-economics/

Despite the economic forecasts, for many companies 2023 will be about growth. As one private equity operating partner put it to me during a recent conversation: “The companies will be asked to do in the next five years, what they haven’t done in the last hundred.”

With the rise of ChatGPT and similar tools, you can’t flick through Economist, FT, or WSJ without seeing an article about AI.

Thanks to that, investors are gearing up with their questions about how AI can fuel or disrupt their industry. And what’s the secret sauce of AI? Data!

As a side effect, they are being more conscious about the role of data and analytics to their businesses. Be it in the form of basic reporting, if not straight as conversational AI.

While data and analytics might be a topic to early adopters among investors, many companies and professionals have spent decades trying to find the right modus operandi.

Let’s learn about that!

Today’s reading list looks at intersections of data science and economics, action and inaction on data, analytics, and AI, and strategies for chief data officers to create and demonstrate value.

  • Is God an Economist? Another article from Bill Schmarzo blending data science and economics. We again get reminded about the economic value of data — “It isn’t the data that’s valuable, it’s the trends, patterns, and relationships (predictive insights) gleaned from the data that are valuable.” About economies of scale and learning — “It is from the quantification of the trends, patterns, and relationships that we make predictions about what is likely to happen.” And lastly about the AI (economic) utility function — “Predictions drive value realization through improved decisions that optimize the organization’s strategic and operational use cases.” (Data Science Central)
  • Action and Inaction on Data, Analytics, and AI: Large amount of money go into technology to unleash the value of data, analytics, machine learning, or AI. But disproportionately less is invested into addressing the people, cultural, and behavioural changes required to accompany the technology ones. I see this first hand during our value-creation data initiatives. Many people are initially confused what is the role of our strategic data consultants who are not coding, deploying technology, or building dashboards. As if the step from data outputs to business outcomes was smooth enough to happen on its own. (MIT Sloan)
  • 8 Strategies for Chief Data Officers to Create — and Demonstrate — Value: “There is a clear need for CDOs to focus on adding visible value to their organizations. The authors suggest eight strategies for CDOs to create — and show — value for their companies: assume responsibility for analytics and AI, focus on data products, measure and document results, build relationships with peers and business leaders who get it, focus on data governance, work on creating a data-driven culture even though it’s difficult to show value quickly, build analytics and data infrastructure, and focus on a few key projects of value to stakeholders.” (HBR)

Enjoy the weekend and remember that keeping up with data is easier than catching up.

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 Diligence

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