A recent McKinsey study says that by 2018:
The United States alone faces a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts to analyze big data and make decisions based on their findings.
Stephen Gold, vice president of worldwide marketing at SPSS, an IBM (s IBM) company, talked with me about how people and educators should be thinking about the opportunities that this shortage presents and how people can become data-savvy managers (and individual contributors). He pointed out that there is an “explosion of information” in almost all organizations and functions:
Think about all the activities that are being tracked today, and not just the amount, but the type [much of it unstructured] and it’s doubling every two years. Originally, people said ‘that’s just the social media,’ but there is unstructured data in surveys, call centers… What if we could put that to work?
There is a void of skills: both deep analytical skills — the heavy lifting of data mining — but also for the data savvy manager. If I’m in finance, I need analytics for risk; if it’s supply chain, then I need optimization; for marketing, customer segmentation. So we’re seeing this demand and this need.
And the need is starting to push out. People in a variety of roles are starting to understand the value of being data-savvy and how the different kinds of data and analyses can be used to influence behavior, improve organizational policies and practices, or pick the best answer:
- Predictive analytics: Using past and current data to answer questions about the future
- Scorecarding: Tracking business metrics against strategic and operational outcomes for better decision making
- Dashboards: Real-time presentations and aggregations of relevant data
- Social analytics: Metrics from social media and networks
- Content analytics: Assessment of available content and how it is used
- Web analytics: Tracking traffic and key words used to find particular sites
According to Gold, universities can provide students with data-savvy backgrounds through four steps:
- Extend the current course curriculum with insights and exercises. People need to be comfortable with information and analytics. This could be basic applied statistics. Moves toward greater visualization of the data with drag and drop interfaces that put the heavy technical issues into the background are helping.
- Develop specific courses that focus on the applications for marketing, finance, social networks, and operations. There needs to be a basic understanding of natural language processing to know the kinds of questions that can be asked — but the focus needs to be on the questions and problems to be solved.
- Create more Centers of Excellence that are cross-college collaborations: Computer Science + Business for example.
- Establish degree programs: DePaul, Carngie Mellon, Northwestern, and many other schools are formalizing the background for data savvy managers — as are community colleges and continuing eduction providers.
Our discussion stayed focused on questioning skills, not skills required to run specific tools or analyses. Gold described an IBM collaboration with Yale’s Center for Customer Insights to create student learning opportunities with real-world data and enterprise analytic software. Even though the students weren’t especially quantitatively-oriented, they were able to understand and digest the technology, understand the problem the customer was having, and suggest “deep meaningful validation points” to the organization.
(On the technical side, IBM is offering IT professionals free bootcamps to get up to speed.)
Business analytics took a huge jump in 1979 with VisiCalc bringing spreadsheets to personal computers. We seem to be poised for a similar jump with data savvy managers needing to open their minds to modern massive, unstructured, and linkable data. Events such as GigaOM’s Structure:Data conference provide opportunities to see the moves leading companies are making and better position yourself for a role as a data savvy manager.