AI exists to solve business and organizational problems. But for businesses, the technology will only gain traction if resources are allotted intelligently to lead to improved outcomes. This means supporting the data science team with solutions which reduce effort spent on support tasks and enable increased time to be spent on high-value tasks.
Given the current data science skills shortages, it would be hugely beneficial to AI projects if AI technology were usable by a wider talent pool group, and if the number of people who could lead AI projects – e.g., junior data scientists and developers – were to increase.
The talented data scientist, still a rare breed
Data scientist is one of the most sought-after roles in industry today, and this type of job posting on Indeed.com rose 75% over the last three years. Yet, qualified, talented data scientists remain hard to find. Historically, most people working in data science came from academia, not business. The recent explosion in demand for data science experts in industry has created a talent gap, with large, deep-pocketed technology companies securing most of the best talent. And IBM predicts an increased demand for 700,000 more data scientists by 2020 in the U.S. alone.
Data science resources are hard to find and expensive. And you don’t just need any data scientist; you need the right data scientist mapped to your particular project and objectives. Aligning your data science team to your business objectives over time is imperative. So is making your AI team’s time as productive and efficient as possible.
The recent explosion in demand for data science experts in industry has created a talent gap
For AI to be operational and make more sense for businesses, it has to have reduced inputs and increased outputs and value. And this has to be true over time, not just as a one-point-in-time boost. To get the most value from data and drive productivity from AI projects, the data scientist is a key resource on your team.
Productivity, the economic measure of output per unit of input
Productivity improvements can come when your data scientist and your team can focus on mainly high-value activities like modeling, insight and the business domain areas and outcomes, rather than all the support processes. Every percentage point of effort that can be shifted in this equation improves productivity dramatically. Freeing data scientists up for more value-add tasks has a direct impact on entire teams and the project overall.
There’s definitely healthy potential to shift the formula and “upskill” the talent pool. For example, moving toward “productizing” tasks would allow more narrowly skilled or less experienced team members (such as developers) to take more involved and leading roles. Productizing allows the platform (versus the individual) to undertake more tasks such as experiment management and auditing – and to learn and evolve in responsibility over time, taking over more and more of these tasks.
Giving AI teams an easy to use end-to-end platform that’s GUI-based is another way to reduce the overall effort and free up data science teams to focus on more high-value tasks. In addition, speeding up the experimentation cycle has big impact – reducing the time to import and process data, create a deep learning model to test in minutes and running tests on models quickly and in parallel.
In AI projects, productivity metrics hinge on freeing up your data science resources to focus exclusively on high-value activities, shifting the 90/10 factor and eliminating the lower-value support functions from their repertoire.