🎓4 Novel ways to build data science talent in-house
🎓4 Novel ways to build data science talent in-house
Hello,
We tend to overrate goal setting but undervalue the systems needed to achieve them.
Directionally sound goals (good-enough strategy) coupled with a great system (excellent execution) can deliver exceptional results. This is true in organizations and our personal life. “Figure out what kinds of environments you can thrive in, and then create it around you so that you’re statistically likely to succeed,” says Naval Ravikant.
I highly recommend reading The Almanack of Naval Ravikant. It’s packed with pithy, ageless wisdom. This is amongst the few books I plan to re-read periodically.
This newsletter will take you about 5 minutes to read.
I. Spotlight: 4 Novel ways to build data science talent in-house🎓
Recruiting data science talent has been one of the topmost challenges for data leaders over the past several years. Thanks to the Great Resignation, we can now add talent retention to this list of top challenges.
It’s actually not the Great Resignation, but the Great Upgrade, says a witty tweet.
People tend to stay at companies when they're still learning. Upskilling can be the perfect antidote to the Great Resignation for smart companies.
If there is a way to upskill your internal talent into data science *and* retain them in the process, this may be an attractive option after all.
No, building data science talent internally isn’t terribly difficult. Here are 4 ways you can do this in your organization:
(Photo by Karsten Winegeart on Unsplash)
1. Look for talent beyond your IT team
“Every organization is underutilizing their current staff due to a lack of awareness,” says Lisa Palmer, chief technical advisor at Splunk. Teams often restrict their internal search to technology teams. “You’d be surprised by the versatility and depth of talent available outside IT, in your lines of business,” she adds.
To discover the gems hidden across your organization, maintain a self-identified list of skills for every employee. Update the list every six months and make it openly searchable by associates. Palmer recommends self-classifying each individual’s skills into four categories: expert, functioning, novice, and desired stretch assignment.
2. Curate your data science curriculum using public content
Finding the right content to upskill your in-house teams is a challenge. Despite the rapid mushrooming of training portals and MOOCs (massive open online courses), the curriculums may not meet your organization’s specific needs. With rich materials available online, often for free, it’s a bad idea to recreate your own content.
“You must design your own curriculum by curating content from multiple online sources,” says Wendy Zhang, who was the director of data governance and data strategy at Sallie Mae. Customize the training plan on your team’s background and roles to get the best of both worlds – content reuse and flexibility to suit your needs.
3. Bridge your team's technical skills with domain expertise
Good AI solutions need the right combination of domain and technical expertise. People who go through the upskilling are often siloed in their perspectives. Technical training often fails to provide exposure to business applications, while business orientations aren’t grounded in technology.
“To address the challenge, we created an Agile routine called Learning Days,” says Todd James, who was the SVP of Intelligent Automation at Fidelity Investments. This provided a platform for the data scientists to educate business teams on AI use-case identification using practical examples. The data science teams, in turn, received briefs from business partners on strategy, products, and business processes.”
4. Enable the application of new skills through experimentation on the job
To paraphrase Julius Caesar, experience is the best teacher. You internalize any new skill only when you apply it in practice. The best courses will amount to nothing if you don’t let your teams experiment, make mistakes, and learn on the job.
When internal candidates have a growth mindset and an aptitude to learn, design on-the-job training. Pair up novices with more experienced employees and set clear expectations for the shadowing period. “Define beginner tasks that the novice can take on immediately and create laddered tasks as they gain proficiency,” adds Palmer.
This is an excerpt from my article published in The Enterprisers Project.
II. Industry Roundup:
1. Article: Are You Using the Right Data to Power Your Digital Transformation?
6 minutes | HBR | Mohan Subramaniam
Most firms use episodic (point in time) data, as opposed to interactive (streaming) data. Interactive data can deliver richer insights, and it increases in value when shared widely. This article shares how a legacy mattress maker tapped into interactive data to deliver a new class of insights. This switch isn’t easy but is critical for every digital transformation intiative.
2. Podcast: How AI is helping this logistics firm deliver vaccines safely
27 minutes | MIT SMR | Ranjeet Bannerjee
What was AI’s role in distributing COVID vaccines efficiently and safely? This episode shares how data science is doing more than resolving supply chain bottlenecks - how is it providing assurance for products transported long-distance. Bannerjee shares how his team leads with the problem before bringing in AI. He talks about the role of leadership in connecting the dots to create holistic solutions.
3. AI/ML salaries dropped in 2021 even as Tech salaries climbed 7%
2 minutes | IEEE | Tekla S. Perry
Average salaries for AI, ML, and NLP dropped in 2021 after rising continuously for three years. Perhaps, the war for AI talent is cooling off due to an increase in the available pool. Or, this is a much-needed correction after years of unsustainable jumps. However, the demand for these data science skills is still high.
III. From my Desk:
1. Article: The Future of Medicine - Fighting deadly diseases with smart devices
8 minutes | Forbes
Smart devices are turning game-changers in the fight against deadly diseases. Find out passive data and intelligent algorithms are shaping our future. In this article, I share exciting research advances in this space and a vision for the future from experts innovating in this space.
2. Book: Make AI & BI work at scale
95 pages | AtScale
I contributed a chapter to this book on how AI and BI can deliver business value at scale. This was conceived as a body of knowledge to provide a holistic perspective
on data management, data engineering, data science, and decision science. This book will improve your odds of delivering value from analytics.
3. Interview: How Digital Biomarkers are bringing healthcare home
15 minutes | Gramener
Does your Apple Watch know if you really slept? Is sleep the minutes you were in bed or the spells of deep sleep you had? Tracking health with wearables and analytics is very tricky. Andy Coravos reveals how HumanFirst is mapping thousands of digital devices and their data to disease measurements. Check out this fascinating interview.
This chart shows who we spend time with across our lives. Time with children peaks between 30 to 40 years and then drops. Post 40, time spent alone shoots up 😮
Thank you for subscribing and reading the newsletter. I appreciate your attention,
Ganes.
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I’m Ganes Kesari. I publish ‘Data-Driven Future’ to help understand how data shapes our world, explore key trends, and explain what they mean for you today. I speak and write to demystify data science for decision-makers and organizations.
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