Digital transformation: 6 ways to democratize data skills
Digital transformation and analytics are nearly inseparable. “At the core of any successful digital transformation is the ability to leverage the company’s data assets to drive superior customer experiences, products and services as well as operating model efficiencies,” says Scott Snyder, a Digital and Innovation Partner with Heidrick & Struggles, and co-author of “Goliath’s Revenge: How Established Companies Turn the Tables on Digital Disruptors.”
Companies typically need data science know-how in order to connect data to analytics or algorithms and deliver digital insight. “Without a critical mass of these data science and analytics skills, companies will struggle to keep up with both customer expectations and new innovation opportunities,” Snyder says.
The gap between supply of and demand for data sciences skills is a problem IT leaders know well. One the one hand, data is growing at an exponential rate. “It’s widely reported that 90 percent of the world’s data has been generated in the last two years, and with data doubling every 1.2 years on average versus processing speed only doubling every one to 1.5 years, companies must become more efficient at analyzing data to keep up,” says Snyder.
On the human resources side, the role of data scientist has become one of the hottest – and hardest to hire for – jobs in the United States. Data scientists were the second-highest paying job in the AI realm for 2019, according to job-posting site Indeed, earning an average salary of $126.927. And the McKinsey Global Institute predicts a shortage of 250,000 data scientists over the next decade in the U.S.
One way to sidestep the skills shortage: Democratize data science across the workforce. Companies can do that a number of ways, for example by upgrading their technology infrastructure and applications to enable straightforward and secure access to data assets and offering data science training to interested and capable employees.
Snyder offers a number of other actions IT leaders can take to meet their data science needs in more creative ways.
“It is critical that IT leaders help drive the data advantage for their organizations by putting in place technology infrastructure that makes it easy for any employee or function to access the company’s data assets and data analytics tools and services while maintaining security and protecting production systems,” Snyder says.
On a human level, your emphasis and outreach should reach across the entire organization.
