Science, Technology, Engineering, and Mathematics
It is clear that the ubiquity of data, and particularly of the very detailed, timely data associated with big data, will create demand for professionals to manage and manipulate the data, and for a population able to under- stand the uses and implications of the new data world. The shortage of people with deep analytical skills is estimated [3] as ranging from 140,000 to 190,000 in the United States alone. The same source estimates the short- age of data- aware managers and analysts to be roughly 1.5 million. This is the tip of the iceberg. As the benefits of big data permeate nearly every industry, they will also impact every enterprise and every consumer. This is already true for fields like retail, online advertising, finance, defense, healthcare, aerospace, and telecom, among other industries. The implica- tions to our economy, and the economy of every nation, are enormous.
At the same time, we are facing a decline in the production of graduates at all levels in the STEM fields, of which technologies associated with big data are an exploding part. As indicated in [4], both bachelor of science and associate degrees in STEM fields, as a percentage of all such degrees, have been declining for nearly a decade. The absolute numbers for each degree type have been essentially flat for that period of time, while the total num- ber of degrees has increased. This source estimates that approximately one million more STEM professionals will be required over the next decade than will be produced with current trends. The unavoidable conclusion as it relates to big data is that not only will there be a substantial increase in demand for people with the skills required to allow our economy to take advantage of this technology, but also that supply, given the momentum view, isn’t increasing and will face increased international competition for people with these skills across the STEM fields. Furthermore, evidence [4, 5, 6] suggests that the “pipeline” of mathematically trained people com- ing out of high school and interested in the STEM fields is well short of the upcoming demand.
The United States alone has far too many initiatives and approaches to the STEM education problem to enumerate here, many with impressive initial results, but too little evidence of which of these will have the critical properties of measurability, scalability, and sustainability. It is reasonable to discuss thought processes that may be of use. In the next section, we outline just a few.
RECOMMENDATIONS
1. Leverage expertise wherever possible. World- class use of the cur- rent volumes, velocities, and varieties of data is an inherently multi- disciplinary activity. Only a relatively small number of very deep scientists and engineers will be creating new, fundamental technol- ogy, but orders of magnitude more data scientists, domain experts, and informed users will be required to maximize the value of data. At AT&T Labs, a multi disciplinary research lab called InfoLab was created about 15 years ago to address opportunities in what is now called big data. It has observed over the intervening time a large list of useful techniques, technologies, and high- value results.
2. Leverage technology aggressively. The difference between force- fitting a technology and using the best available technology, at scale, can be huge. Think in terms of small, multi disciplinary teams (where small is a single- digit number), armed with the best technol- ogy available. Right now it is not clear what the winning tools across the big data landscape will be five years from now. It is clear that a revolution in the basic set of tools is appearing to address a variety of issues in this area. In this world, worry less about standards than productivity at scale. Ignore tools that don’t scale easily.
3. It’s all about the data! Initiate a proactive effort to make data easy for the teams to access. Experience indicates that getting the required data is often more than 75 percent of a data analysis effort, especially when real- time data is involved.
4. Use crowds and networks where possible. Hide data from employees only where it is absolutely necessary, and encourage people to look at data critically. It may provide some surprising insights, and will certainly increase data integrity.
5. In your own interests, get involved in improving STEM education. There are many approaches to improving all levels of education. Some, such as use of virtual classrooms, inverting the learning model via online learning, seem very promising. Examples are mentoring pro- grams to increase retention in STEM, outreach to help minority and female students understand what STEM employees do, interaction with education partners on industries’ needs in the area, and investi- gation of online classes in big data. Most importantly, generate what- ever data you can on techniques and outcomes. Much of the current data in this space is anecdotal, and that will not be sufficient to make the needed progress.
In summary, the trends in terms of value and spread of data use guarantee rapid and broad increases, while the trends of skilled workers in these fields are not likely to keep up, at least in the short term. Proactive work to address your skill needs will pay disproportionately large dividends.
REFERENCES
1. Friedman, T., The World Is Flat—A Brief History of the 21st Century, Farrar, Strauss & Giroux, 2005.
2. Shim, Simon S.Y., The CAP Theorem’s Growing Impact, Computer, 45(2), February 2012.
3. McKinsey & Company, Big Data: The Next Frontier for Innovation, Competition, and
Productivity, McKinsey Global Institute, Cambridge, May 2011.
4. Office of Science and Technology Policy, The White House, Report to the President:
Engage to Excel: Producing One Million Additional College Graduates with Degrees in Science, Technology, Engineering, and Mathematics, http://www.whitehouse.gov/ sites/
default/ files/ microsites/ ostp/ pcast- engage- to- excel- final_feb.pdf, February 2012. 5. TechAmerica Foundation, Recommendations for Education and the Advancement
of Learning (REAL) Agenda Commission: Taking Steps to Invest in Promise of Their Future and Ours, http://www.techamerica.org/ Docs/ fileManager.cfm?f=taf- real-
report.pdf; April 2012.
6. Carnevale, A., N. Smith, and M. Melton, STEM: Science, Technology, Engineering,
Mathematics, Georgetown University Center on Education and the Workforce
Georgetown University; http://www9.georgetown.edu/ grad/ gppi/ hpi/ cew/ pdfs/ stem- complete.pdf; 2011.
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