by Michael Rosenbaum, Catalyst IT Services
From human resources and product development to customer service and sales and marketing, companies are increasingly looking to Big Data to identify patterns and make predictions that can help lead to breakthrough innovations. It’s a trend that spans a diverse range of industries.
Take Netflix’s bet on «House of Cards,» which is the most streamed piece of content in the United States and 40 other countries. Before production, using Big Data, Netflix already knew that the odds of a blockbuster in original programming were pretty good. The company was able to analyze and use predictive data gathered from 30 million «plays» a day, 4 million subscriber ratings and 3 million searches. In addition, Netflix leveraged insight from the time of day when shows were watched and what devices were being used, and also found that viewers who loved the original BBC version of the show also tended to watch movies starring Kevin Spacey or directed by David Fincher.
The Procter & Gamble example
Procter & Gamble (P&G) — which is the world’s largest consumer packaged-goods company, with operations in 75 countries and reaching 4.4 billion consumers — also counts on Big Data to drive change. The company uses those data across the business to make decisions about deploying innovations into its 40 largest and most profitable product categories, assess the efficiency of its supply chain and help drive real-time decision making. One example of P&G’s Big Data work is its Decision Cockpits, which use Big Data and visualization to improve decision making by providing 60,000 employees with a single source of information.
The science advantage
With this increased demand for Big Data, there is skyrocketing demand for people with education and experience in technology, engineering and math — the «TEM» in the so-called «STEM» career group. But here’s a thought: Consider adding the «S» (science) from STEM to your Big Data team.
In a marketplace where one of the most important components of innovating is the ability to come up with unexpected hypotheses and insights, a different perspective can be game-changing. The chance of coming up with breakthrough ideas can be dramatically enhanced simply by increasing the diversity of perspectives and backgrounds on the team.
Why? Most Big Data professionals come from a traditional background of statistics, theoretical math, applied math and/or econometrics. Consider someone trained in science — and, in particular, biology. Most analysts are used to structured data and clear patterns. But when scientists want to predict outcomes in the natural world, that data is rarely perfectly structured or clear.
Biology isn’t a «neat» science — unlike physics or mathematics, or even chemistry, 1+1 doesn’t always equal 2. In biological systems, myriad complex factors are at work simultaneously; adding one new factor may yield a consistent response, but more likely, it will result in a range of responses or values. A biologist sees data the way it is represented in nature.
One perfect example is the human genome (or any DNA analysis). It has 3 billion base pairs that have all been identified through pattern recognition and the sifting through of billions of base pairs — Big Data — to look for meaningful patterns that contribute to a better understanding of the role of genetics in diseases. This unique perspective can help unlock hidden insights from data sets and variables that mathematicians and scientists in other fields may not consider.
How does that relate to success in dealing with Big Data in the business world? Before drawing conclusions, biologists account for many variables and then look closely at the remaining variables, which may seem to be counterintuitive. Having people who
are used to accounting for the «messiness» and complexity in the real world — and who are good at creating structure around that messiness in a way that allows others to analyze and think about it — is key to achieving breakthrough insights.
My company, Catalyst IT Services, has applied Big Data approaches to hiring and team assembly. Catalyst has a biologist on staff who developed an approach to collecting data about applicants that provide highly reliable and predictive insights on future performance, allowing the company to build best-fit agile teams that will work well with clients to help them innovate and execute goals. Using Big Data allows Catalyst to identify high performers who others would overlook because the potential employees don’t have the typical credentials that are used to make hiring decisions — credentials that also generally fail to predict success. This approach results in higher individual and team performance with U.S.-based teams at costs equivalent to those incurred by using offshore providers.
Management guru Peter Drucker identified seven sources of innovation. However, each source leads to innovation only through what Drucker calls a «leap of imagination.» Adding diversity of thought and people who are comfortable with unexpected patterns to your Big Data team may just be the key to imaginative solutions and better, bigger, faster innovation.
Extracted from: http://bit.ly/1iHxGLh