Delivering on the AI Promise with the Existing Team You Have Today
One of the most frequently asked questions that I get in conversations with analytics leaders is, “How do I know if my team is ready for machine learning?” That’s an excellent question. Despite the hype and simplicity of automated machine learning platforms, building AI solutions is not for everyone. Your team will need some training in order to succeed, no matter how easy the tools are to use. However, the path to AI success is likely much shorter than you think.
Getting AI Ready
At DataRobot, we’ve helped some of the world’s largest organizations and most well-known brands adopt and democratize data science with automated machine learning. I currently work alongside more than 200 data scientists doing this type of work with clients globally. There is a learning curve for citizen data scientists — business analysts, business intelligence professionals, data engineers, and software engineers — to become proficient in building trustworthy AI models. Data science, data preparation techniques, and language differences usually need to be learned.
Your team will also need to master the Art of AI Storytelling. From accurately defining AI projects to understanding what data to use, skills such as preventing bias, interpreting results, and effectively communicating findings are all critical for helping stakeholders understand results and get the most actionable value from machine learning projects.
In order to help analytics leaders understand if their teams are ready for AI and what they need to do to get started, I’ll be holding a webinar on February 7th called From Analytics to AI: Is Your Team Ready? In the session, I’ll cover FAQs, the wrong reasons to delay adoption, and how to prepare your existing analytics talent for machine learning and AI. To make this session actionable for team goal setting and planning, I’ll also share a checklist of pre-requisite skills along with recommended practical citizen data science training outlines for upskilling existing talent.
Looking ahead, your talent plans will need to factor in the effect of automation with routine machine learning tasks and model factories. Automation changes the blend of needed analytics and data science skills. Much like the traditional BI to self-service BI movement, automation in the citizen data science wave will end up shifting tasks away from programming intensive work to delivering more business value, more quickly, in more projects with higher strategic impact.
Competing in the Algorithm Economy
As analytics legend Tom Davenport told us years ago, “Decision making and the techniques and technologies to support and automate it will be the next competitive battleground for organizations. Those who are using business rules, data mining, analytics and optimization today are the shock troops of this next wave of business innovation.”
We are just starting to see data-driven organizations becoming algorithm-driven.
We are just starting to see data-driven organizations becoming algorithm-driven. The gap between self-service analytics and data science limits organizations’ ability to exploit analytics as a game-changing competency.
If you do have data-savvy analytics talent using tools like Alteryx, Tableau, Qlik, Power BI, TIBCO Spotfire, R or Python, then you also have a solid foundation to begin your AI journey with automated machine learning. Capable, motivated business intelligence and analytics professionals can elevate their skills to drive better outcomes. Please join us in the upcoming webinar, contact us for help or review the following resources to learn more.