There’s a budding career for professionals who are passionate about numbers-–that of the data scientist. In order to make the best out of the surge of data that plagues the world every day, we need these people to analyze it all. A study by McKinsey Global Institute confirms that United States will need 140,000 to 190,000 mavens in analytics, and 1.5 million data-literate managers to make sense of the insane influx of data that doubles every two years. Google’s chief economist Hal Varian said that “the sexiest job in the next 10 years will be statisticians.” But do we have enough people with the right skills to meet the growing demand?
IBM fears that we currently have a shortage in graduates prepared for the data-heavy roles opening up in their offices, and this is a problem that will plague businesses of all industries before too long. Big data is not only important for the future of the tech sector, but it’s driving new trends in business intelligence, marketing, consumer health and more.
Here’s Wikibon’s break down of data science in comprehensible processes and terms. As you can see, there’s rapid growth in the types of data needed to be analyzed, sourcing from a variety of devices and services that spill over between enterprise and consumer sectors. IBM’s been working in the area of big data for several years, processing information in new ways to create AI systems such as Watson, and enabling hospitals to determine the best forms of prenatal care. And in anticipation of more big data demands in the coming years, IBM’s taken the initiative to launch education programs around big data in order to encourage more academic interest in this area.
A prime example of IBM’s efforts is Big Data University, a web-based “school system” that offers a wealth of resources and courses for Hadoop, some paid and others free of charge. It has an international appeal, with interest from other countries such as China to translate courses for extended distribution. As with many big data interest projects, Big Data University also holds contests to spur innovation from its students, while also recognizing the cream of the crop in order to reward an ongoing interest from app developers and data scientists.
Another initiative comes from Anjul Bhambhri, an IBM executive that’s spent her tenure exploring data science and its place in our future economy. To develop more interest in data science, Bhambhri’s launched a web series for IBM’s 200+ network of colleges and universities, setting up campus-specific discussions with students and faculty. Launched last month, Bhambhri’s already addressed students at Arizona State, with UCLA next in line.
“We’re all facing this–skills is a big issue in the big data space,” says Bhambhri. “To that end, it’s been pretty important from our standpoint that we do something concrete so we’re helping people build skills and understand the technology–we obviously want more and more people getting interested in this space.”
During Bhambhri’s chats she discusses real cases from IBM, bringing to life the changes big data and analysis can make in traffic and urban planning, energy efficiency and more.A recent project with Vestas Wind Systems in Denmark highlights IBM’s involvement with big data:
Situation: Vestas researchers asked the question, what if they could know when they install wind turbines how to deliver the best ROI and achieve peak performance? The company wanted to gain insight into optimal wind turbine placement to increase energy production while reducing costs. These challenges led Vestas to search for a technology that would capture, process and analyze tons of data from vital sources, a task never before attempted. The solution helped slice weeks from data processing times and support 10 times the amount of data than previous attempts.
Solution: Vestas is using InfoSphere BigInsights software and a powerful supercomputer to improve wind turbine placement for optimal energy output. Getting turbine placement right is a major challenge for the renewable energy industry. Vestas is analyzing petabytes of unstructured data such as weather reports, moon and tidal phases, geospatial and sensor data, satellite images, deforestation maps, and weather modeling research to pinpoint installation. The analysis, which used to take weeks, can now be done in less than one hour.
The prognostic potential of Big Data show a promising future across many fields such as public health, economic development, and economic forecasting. You can take a simple web search to realize the growing relationship between compounded data and human circumstances. Researchers found out that flu patients increased weeks after there was an upturn of Google search terms like “fly symptoms” and “flu treatments.” Google has also proven more accurate in predicting real house sales than real estate economists, basing results on the fluctuations of housing-related search queries.
It’s also possible to see the influences that have gone mainstream, as we analyze communication on the social media scale, like Twitter’s hashtags. The social network is a hub for mining huge data sets of collective online behavior. One of the rather interesting findings therein is that people you know but don’t communicate with can be the best source of tips for job openings.
Moreover, a detailed analysis by Wikibon predicts that “the Big Data market is on the verge of a rapid growth spurt that will see it top the $50 billion mark within the next five years.” Its current value is currently just under $5 billion based on related software, hardware and services revenue, but is expected to surge as interest and awareness of the faculties of big data increases “to gain competitive advantage and to improve operational efficiencies, coupled with developments in the technologies and services that make Big Data a practical reality, will result in super-charged CAGR growth rate of 58% between now and 2017.”
IBM, Intel and HP currently lead the big data market by revenue. However, they are facing great competition from Big-data pure plays that are developing innovative technologies–all the more reason for IBM’s preemptive call to action, rousing academia for a data-centric future.