The Harvard Data Science Review started in 2019 and aims to be a journal that features “everything data science and data science for everyone”. It has digitally published seven quarterly issues plus a special COVID-19 issue. It’s available for free and I’ve found some good nuggets of information in the articles that I’ve browsed.
One such article is “How to Define and Executed Your Data and AI Strategy” by Ulla Kruhse-Lehtonen and Dirk Hofmann. This article appeared in the Summer 2020 journal (see link in References) and delivers a comprehensive enterprise approach. It provides a useful strategy framework (pictured below) as part of this approach.
(from How to Define and Execute Your Data and AI Strategy · Issue 2.3, Summer 2020 (mit.edu))
The entire article is a good read. It presents a business first focus for prioritizing AI investments, an approach that resonates with me. They also stress the need to link AI solutions back to operational systems such as ERP and CRM systems. Since bringing AI to Dynamics operational systems is my day job, I wholeheartedly agree!
In the Human Skills section of the article, the authors introduced a new title to a role that they assert is critical to these projects – AI strategist. An AI strategist “translates the business goals into data and AI requirements, oversees project execution, and ensures project outcomes are taken into use by business processes”. They go on to note that “Without an AI strategist, the communication distance between people with a business background and the data scientists is often too wide”. They also emphasize the end to end nature of the AI strategist, noting that driving business impact after the initial solution has been developed is a critical part of the role.
In Essential ingredients for a data science project – Lake Data Insights, I described the essential data science ingredients using the following equation. The AI strategist has expertise in all three input components; they are quite likely the only one that spans all three on the project. It’s easy to see why this is a critical role in project success.
Problem + Data + Expertise = Data Science Success
Bringing together the business expertise of the problem domain with the technical understanding of the data is often the most difficult part of a data science project. I was encouraged to see the recognition of this challenge in the article and the identification of the AI strategist role to meet the challenge. I mused about different job titles In Engineer vs. Architect vs. Scientist – Lake Data Insights and how they apply to me. I’m going to have to consider adding AI strategist to the list of possibilities… in a future post!
References:
How to Define and Execute Your Data and AI Strategy · Issue 2.3, Summer 2020 (mit.edu), accessed on 2/7/2021
Picture details: Early taste of winter, 10/15/2020, Canon Powershot G3 X, f/4, 1/800 s, ISO-800