Whiteboard space is at a premium in my office, but I’m always willing to erase a section to brainstorm a potential solution to new problems—afterall if we can visually create a “product” that provides insight, we can work backward and determine what data we need to produce that work.
I read Steven Covey’s book The 7-Habits of Highly Effective People well over a decade ago; however, “Begin with the End in Mind” has stuck with me as I’ve worked on various projects since that first reading. Currently, I’m working on several data-driven activities that remind me why this habit is so important.
Data Analysis (and Data Science) are hot topics, but too often folks want to jump right in and start crunching numbers—”Let’s run an X test on the Y samples and calculate the Z results.” It is easy to create large datasets that miss the mark and become wasted effort. While it may be hard, we need to get everyone to step back and ask the simple questions: “What problem are we trying to solve?” “What will this look like when we are ‘done’?”
I am glad I can get out of the office and learn what companies are doing in Utah and Day 2 of the SSTS 2019 offered some more reminders of what is on the mind of tech leaders. In no particular order, here are several items I noted:
- Companies are looking for individuals with domain knowledge who can reach across disciplines.
- Culture eats strategy (i.e., it doesn’t matter how great your corporate strategy is if the culture of your company is terrible.)
- Good companies promote a learning culture.
- As a leader, get your hands dirty.
- Make hiring decisions based on core competencies even if the person looks different than you.
- How do customers increase revenue when they adopt your product or service?
- Start by measuring something.
Day 1 of the 2019 SSTS is in the books and my favorite session from the day was a panel discussion on The Future of AI Talent in Utah. Not because I know anything about AI, but because one can apply the points made by the panel across many disciplines.
When answering a question about talent in Utah, the panel consensus was something along the lines of “Most applicants look the same—1% of the applicant pool are getting all the offers.”
I suspect this is somewhat exaggerated, but it begs the question: What makes that 1% special? How do people differentiate themselves?
What did the panelist say to the young crowd looking at AI as a career path?
- Don’t rely on “cookie-cutter” (or in software terms “hello world”) projects to demonstrate competency.
- Show a passion for solving problems.
- Demonstrate your capabilities by publicly sharing your work—in the area of software development, maintaining a public Github repository for one or more projects. For those of us not in the software space, publishing in a reviewed, online journal would be an option.
- And while we’ve all heard the college dropout success stories, having a strong educational foundation and simultaneously learning how to solve NEW problems—the ones not in the textbook today—is needed to stay competitive.
These recommendations sound like good advice for everyone looking to differentiate from the crowd.