It’s no secret that Big Data’s real challenge may not be the technology, but finding people with the right skills for Big Data success.
But, as argued recently in two different pieces, it’s not a problem you solve by simply “hiring a data scientist.”
The first piece is a Harvard Business Review blog post written by Matt Ariker, the COO of McKinsey’s Consumer Marketing Analytics Center, with Tim McGuire and Jesko Perrey, both of whom are McKinsey directors who work with clients on Big Data, advanced analytics and customer life cycle management.
“Big Data talent is a critical issue. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills, according to the McKinsey Global Institute,” they write. “But companies need to spend time upfront to identify the kinds of roles they need to make the Big Data machine run rather than just rushing to recruit math and science jocks.”
Instead, they recommend companies focus on building the right team that includes five important roles:
- Data hygienists, who address data quality and cleaning issues
- Data explorers, who separate the data you need from the data you don’t
- Business solutions architects, who put the discovered data together and organize it for analysis
- Data scientists, who develop the sophisticated analytics models
- Campaign experts, who turn the models into results
The article refers to marketing campaigns and customer data, and talks about how campaign experts “use what they learn from the models to prioritize channels and sequence the campaigns--for example, based on analysis of an identified segment's historical behavior, it will be most effective to first send an email then follow it up 48 hours later with a direct mail.” But I actually think this type of role could be helpful whenever you are using large data sets that need to be interpreted in a larger business context.
Why do you need all these people? Because you want to build analytics that are useful, the article notes.
“It's demoralizing to build a product or service that no one uses so the burden is on your team to demonstrate how its models can benefit internal business owners,” they write. “That requires thinking of the business owners as customers.”
It also requires transparency at every level, including the models. You can encourage broad business appeal by measuring internal adoption of new models, the McKinsey team suggests.
Vincent Granville, of Big Data News, offers another suggestion for ensuring your Big Data investments pay off. Instead of hiring data scientists, he suggests you hire a consultant much like, oh, himself.
“Working with a guy like me for about 20 hours to help you jump-start your big data projects and assess expected ROI, in a role very similar to a management consultant. This is the real solution to the problem.”
It’s a brassy statement, but Granville seems to have the credentials to back it up--although calling out some people as “fake” data scientists probably won’t win him many friends.
But he has a point: Hiring one person who’s excellent at statistics isn’t necessarily going to be enough to deliver an ROI on your Big Data investments.
“One of the main reasons big data ROI seems so obscure to CEO's is because CEO's don't use the right people to assess ROI on big data--they might indeed use nobody, but gut feelings instead,” he writes. “There is still a lot of confusion about what a data scientist is, and I believe this is one of the major bottlenecks against adopting big data.”
Once again, we’re reminded that the success of technology projects is almost never about the technology, but about how the business can leverage the results of that technology.