A long time has passed since I’ve last written. Thank you to my peers that have reached out and asked what’s going on. Rapidly growing DataRobot keeps me extremely busy. To be completely transparent, I do miss sharing what I’m learning – especially while I’m at the forefront of bridging data science to analytics seeing astounding AI impact and filling my brain with new knowledge.
I also had a recent health scare with an awful antibiotic adverse reaction. It will take some time to recover. What I learned from that event…I can’t afford to sway from a limited diet. It also provided me the gift of deeper appreciation for my health, my husband, my pups, and my career.
It is an open secret that I started questioning if I should leave the tech industry and take my talent elsewhere to continue growing a few years ago. Truth be told. I did look at other professions. I considered going back to school to be a veterinarian, oceanographer, or researcher applying my data skills in other ways. At the end of each soul searching exercise, I realized that I simply love working with data. It is an obsession. It is a part of my being. Thus, I needed to find ways to relish what is good about the tech industry, try even harder to ignore what is bad, and consider setting new personal stretch goals.
Are you AI Ready?
Every January, people around the world assess where they are at in life, make resolutions, set goals, and start thinking about new roles. If you are in analytics, I urge you to set goals to get started learning machine learning and artificial intelligence (AI). Not only is it the most exciting area of analytics, many roles will soon require these skills as are our profession evolves to include citizen data science.
Per the World Economic Forum’s annual The Future of Jobs report, 73% of companies will adopt machine learning. AI and machine learning specialists are also emerging new era roles.
According to PwC analysis of Burning Glass Technologies jobs data, the 2020 estimate calls for 2.7 million job postings in data science and analytics. These roles will fall into two distinct skills-based markets: Data Science and Citizen Data Science.
Gartner defines a citizen data scientist as “a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics. Citizen data scientists are “power users” who can perform both simple and moderately sophisticated analytical tasks that would previously have required more expertise.”
At DataRobot, we’ve helped some of the world’s largest organizations and most well-known brands adopt and democratize data science with citizen data science. I currently work with 200+ data scientists doing this type of work globally. I know there is a learning curve for analytics pros. The data prep and language differences alone are significant. You will need training to do citizen data science right – avoid AI gone wrong – even if your tools are simple to use.
You will need training to do citizen data science right – avoid AI gone wrong – even if your tools are simple to use.
To help Analytics Leaders and my data-savvy peers understand if they are ready for AI and what they will need to do to succeed, I’ll be holding a webinar on February 7th called From Analytics to AI: Is Your Team Ready? I’ll cover FAQs, how to prepare your existing talent for the next generation of analytics, share a checklist of essential skills along with recommended practical training for machine learning and AI upskilling.
Looking ahead, your talent upskilling plans will need to factor in automation of routine data science tasks and model factories. Automation changes the blend of skills you need for both analytics and data science occupations. Much like the traditional BI to self-service BI movement, automation and citizen data science end up shifting tasks away from programming intensive busywork to delivering more value, more quickly, in more projects.
Automation changes the blend of skills you need for both analytics and data science occupations.
Art of AI Storytelling
You ‘ll also need to master the Art of AI Storytelling. From accurately defining AI projects, to understanding what to data to use, preventing bias, interpreting results and effectively communicating findings, all of these critical skills are needed to help stakeholders understand AI results and get the most actionable value from machine learning projects.
I’m currently writing a book on that hot topic – please contact me to get added to a list to be notified when my book is available.
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.”
Starting to see data-driven organizations becoming algorithm driven.
We are just starting to see data-driven organizations becoming algorithm driven. Are you ready to compete for analytics roles in the algorithm economy? To be brutally honest with you – probably NOT! I learn so much every single day just from our Slack channels. There is just so much to learn to thrive in this domain and do AI right. However, getting started may be easier and faster than you realize when you are focused on specific outcomes.
The gap between analytics and data science does limit an organizations’ ability to exploit analytics as a game-changing competency. According to McKinsey, early adopters know it. Here is a simulation of results.
2019 is the Year to Upskill
As you set goals for the new year, consider adding machine learning and AI to your list. I am doing it with the help of a few patient, articulate DataRobot data scientists who guide me along the way. I also see amazing success stories from other analytics pros who took the plunge into citizen data science. Don’t be fooled by easy buttons or wizards that you are seeing in the market. Approach AI responsibly with some education. With a little training and a lot of motivation, you can learn to build and explain trustworthy machine learning models in the emerging citizen data science space.