The board has made AI a top strategic priority for your company and is ready to put the money where its mouth is. Good. Do we have the right people to execute the strategy? Who do we train and how? What people do we hire and how?
In is article we offer a decision framework to help you make clarity around the complex web of choices around your AI Talent Strategy.
Photo by Drew Graham on Unsplash
An AI Talent Strategy Framework
So, what does a good enterprise AI Talent Strategy looks like? To help defining your company needs we’ve created a simple AI Talent Strategy Framework.
We will address other dimensions of a company's AI Transformation in other articles but when it comes to AI Talent companies should look across the following three dimensions:
Let's dive into each of these pillars.
Data Scientists and Unicorns
First question to answer is: what roles should be defined and need to be developed in the company to successfully support AI and Data Science activities?
If you are fairly early in your AI Transformation journey, chances are that you are leveraging your traditional data analysts resources. This imply they are probably using “deductive” techniques to explore and summarize data. There is nothing wrong with that but you’re probably not able to extract all the insight available in your data to drive specific decisions. A professional trained in modern Machine Learning techniques can start using more “inductive” techniques and start leveraging your data for create new products, make your existing ones smarter or make your processes more efficient.
Now those companies who have started hiring data scientists to get more value out of their data have quickly come to the realization that their organizations had a number of challenges to be solved before these data scientists could actually produce any business value. The best representation of this situation has been presented by D. Sculley et alii in their now famous 2015 article “Hidden Technical Debt in Machine Learning Systems”. Below is the awesome visualization they have provided in their article putting in perspective how much work is left once you have the Machine Learning code covered.
from “Hidden Technical Debt in Machine Learning Systems” — D. Sculley et alii, Dec. 2015
Time and again we've seen companies falling in the "unicorn hunt race" assuming that by paying top dollars for very smart PhD types resources, they will solve all the data problems the company is facing and will put their company ahead. As the above visualization eloquently show, you need a lot more than a handful of top-notch Data Scientists if you are trying to extract business value from the ocean of data your company may be collecting in Data Warehouses, Lakes and the likes.
You need a team.
Organizations who have reached Data Fluency not only has clearly codified and standardized the 5 following Data Roles across the enterprise, but also the mechanisms to enable Continuous Learning of its employees for these roles:
Key Roles in advanced Data teams - Copyright The AI Academy 2021
Data Engineer: Having the data stored in a Data Lake and having quality data ready for Machine Learning algorithms are two very different things. Companies trying to do Machine Learning know this very well and list of references indicating 80% of the time in Data Science projects is spent in producing the right data is endless. Today the role of the Data Engineer is well established both in terms of a high priority need and in terms of the required skills. In a nutshell, the DE is responsible to guarantee access to quality data to address the specific business problem tackled by the team.
Machine Learning Engineer: Once the raw data has been transformed into quality data we can use this data to write Machine Learning code, generate new features, train predictive models and get models ready to be deployed. Here is where the role of the Machine Learning Engineer comes in: he/she applies advanced analytics techniques to extract insight from data and build predictive models to address the business problem tackled by the team.
Data Analyst: A common pattern we see in organizations at the early stages of their AI Transformation journey is to excessively focus on the technical aspects of the Machine Learning magic box and measuring success by monitoring only technical metrics. In a mature organization the teams creating data science products and solutions are able to clearly formulate the problem as well as measuring the results of their Machine Learning algorithms in terms of business benefits for their organizations. The role of the Data/Business Analyst is therefore critical to bring the necessary Domain Knowledge and guide the team towards a business oriented definition of success.