Closing the analytics talent gap
Earlier this year, our parent organization released the “CompTIA Quick Start Guide to Easing into Big Data.” While developing the guide, our research team discovered the statistic that nearly eight in 10 executives agree with the statement, “If we could harness all of our data, we’d be a much stronger business.”
Sounds simple enough but harnessing all that data is a vexing challenge for companies of all shapes and sizes.
First, there are the technical complexities. Big data – and the analytics based on that information – spans the hardware, software and services segments of the business intelligence markets.
Second, there’s the pace of development. International Data Corporation (IDC) reports that the big data market grew to more than $23 billion last year at an annually compounded rate of nearly 32 percent.
But the crux of complication isn’t the intricacies of technology or the rate of change in an emerging field. It’s people. There aren’t enough qualified workers to fill available data management positions. In fact, the management firm McKinsey predicts the U.S. may face a 50 percent to 60 percent gap between the supply and demand of analytic talent as early as next year.
How can companies large and small move fast enough to avoid this impending pitfall? One way is by rethinking the traditional internship model.
Before delving into details, let’s establish a foundation of understanding. Internships need the “4 Ps” to succeed:
· Project – Work for the intern that is challenging and valuable to the organization.
· Place – A location for the intern to use as a base for conducting work, typically a facility operated by the company .
· Personnel – Those who guide the intern and care about the intern’s success.
· Payment – Compensation given to the intern for investing time and effort. Otherwise, the intern is a volunteer.
From the perspective of my organization, Creating IT Futures, we don’t consider any programs labeled “internships” to be valid unless both intern and sponsoring company take away value in the final analysis. And we believe all four of the attributes above must be present to generate a mutually beneficial outcome.
That’s where many digital businesses get stuck today. Not every company feels as though the resources to fulfill all 4 Ps are available. And when this mindset takes hold, usually it’s a showstopper for an internship program.
Here are some ways this mentality stalls or stops internships these days in terms of the 4 Ps:
· Project Ambivalence – Executives and managers who are pressed for time pose the question, “What could an intern do for us that has actual value?” and then devote little time to finding an answer.
· No Place to Hang an Intern’s Hat – Businesses today operate in an increasingly mobile, virtual environment. Internship design must evolve to match or programs fall by the wayside.
· Too Few Personnel -- Digital transformation already consumes the attention of staff and strains available budgets. Supervising and mentoring is perceived as a long-term play that comes at too high a short-run cost.
· Too Little Payment (Or None At All) – Per CompTIA’s “Business Relevance of IT in the SMB market,” small to mid-sizes “account for the vast majority of the nation’s business entities and serve as a key driver of job growth and innovation.” But the average SMB doesn’t feel comfortable shouldering all 4 Ps of an internship program.
Do these attitudes and constraints necessarily doom all internships? No. But if we don’t become more creative about internship design, an excellent means of nurturing talent could wither and die at a time when businesses need that method to thrive.
While big data and data analytics are very complex, the solution to this dilemma is not as complicated as one may expect. Why not divide responsibility for the 4 Ps across more than one entity?
Consider these four internship configurations as examples of better fits for today’s interns and companies:
· Advocate Model – School districts, community colleges and/or non-profit organizations can provide counsel to sponsoring companies about appropriate Projects for interns. In some cases, these entities have programs offering templates and guide books.
· Shared/Managed Model – Not all employers can facilitate an internship where the analytics team resides. A shared/managed model allows for part of the internship to be handled virtually in cooperation with the employer’s remote offices. One of the Ps – Place – shifts into the mobile realm.
· Partner Model – Some large corporations can’t supervise an intern on location. But they can coordinate with their local channel partners, who have available Personnel, to offer students internships in analytics.
· Aggregate Model– SMBs often lack resources to supervise and compensate interns and/or lack a large enough workload to keep an intern fully occupied. But SMBs can aggregate resources and projects with other small firms through advocate groups, as mentioned earlier – i.e., a school district or other community organization like a Chamber of Commerce. Payment is aggregated across participating businesses and may be subsidized by the advocate organization.
With enough coordination, one can imagine models in which more than one of the Ps is shifted to fit the needs of interns and businesses. That’s great – because adjusting to shifting environments and requirement is the best way to develop more data scientists – and eventually close the analytics talent gap.