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  1. Big Data, Mining, and Analytics: Components of Strategic Decision Making
  2. Is Game Theory important for Data Scientists?
  3. 1. Choose the right data
  4. Big Data, Mining, and Analytics: Components of Strategic Decision Making
  5. 2. Build models that predict and optimize business outcomes

A mainstem cancer by Abigail Lynch. Education ': ' Education ', ' III.

Big Data, Mining, and Analytics: Components of Strategic Decision Making

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Is Game Theory important for Data Scientists?

Please provide your Email ID. Email ID is incorrect. Mainly Statistical Analysis, focus on prediction and discovery of business factors on small scale. Mainly data analysis, focus on prediction and discovery of business factors on large scale. Big data takes the form of messages, updates, and images posted to social networks; readings from sensors; GPS signals from cell phones, and more.

Many of the most important sources of big data are relatively new. The huge amounts of information from social networks, for example, are only as old as the networks themselves; Facebook was launched in , Twitter in The same holds for smartphones and the other mobile devices that now provide enormous streams of data tied to people, activities, and locations. Thus the structured databases that stored most corporate information until recently are ill suited to storing and processing big data.

At the same time, the steadily declining costs of all the elements of computing—storage, memory, processing, bandwidth, and so on—mean that previously expensive data-intensive approaches are quickly becoming economical. As more and more business activity is digitized, new sources of information and ever-cheaper equipment combine to bring us into a new era: one in which large amounts of digital information exist on virtually any topic of interest to a business.

Mobile phones, online shopping, social networks, electronic communication, GPS, and instrumented machinery all produce torrents of data as a by-product of their ordinary operations. Each of us is now a walking data generator. Analytics brought rigorous techniques to decision making; big data is at once simpler and more powerful. We just have more data. But the truth, we realized recently, is that nobody was tackling that question rigorously.

We set out to test the hypothesis that data-driven companies would be better performers. We conducted structured interviews with executives at public North American companies about their organizational and technology management practices, and gathered performance data from their annual reports and independent sources.

Not everyone was embracing data-driven decision making. In fact, we found a broad spectrum of attitudes and approaches in every industry. But across all the analyses we conducted, one relationship stood out: The more companies characterized themselves as data-driven, the better they performed on objective measures of financial and operational results.

1. Choose the right data

This performance difference remained robust after accounting for the contributions of labor, capital, purchased services, and traditional IT investment. It was statistically significant and economically important and was reflected in measurable increases in stock market valuations. Often someone coming from outside an industry can spot a better way to use big data than an insider, just because so many new, unexpected sources of data are available. One of us, Erik, demonstrated this in research he conducted with Lynn Wu, now an assistant professor at Wharton.

They used publicly available web search data to predict housing-price changes in metropolitan areas across the United States. They had no special knowledge of the housing market when they began their study, but they reasoned that virtually real-time search data would enable good near-term forecasts about the housing market—and they were right.

In fact, their prediction proved more accurate than the official one from the National Association of Realtors, which had developed a far more complex model but relied on relatively slow-changing historical data. This is hardly the only case in which simple models and big data trump more-elaborate analytics approaches. Researchers at the Johns Hopkins School of Medicine, for example, found that they could use data from Google Flu Trends a free, publicly available aggregator of relevant search terms to predict surges in flu-related emergency room visits a week before warnings came from the Centers for Disease Control.

Similarly, Twitter updates were as accurate as official reports at tracking the spread of cholera in Haiti after the January earthquake; they were also two weeks earlier. So how are managers using big data? One uses big data to create new businesses, the other to drive more sales. Minutes matter in airports.

Big Data, Mining, and Analytics: Components of Strategic Decision Making

So does accurate information about flight arrival times: If a plane lands before the ground staff is ready for it, the passengers and crew are effectively trapped, and if it shows up later than expected, the staff sits idle, driving up costs. So when a major U. The pilots made these estimates during their final approach to the airport, when they had many other demands on their time and attention. In search of a better solution, the airline turned to PASSUR Aerospace, a provider of decision-support technologies for the aviation industry. It calculated these times by combining publicly available data about weather, flight schedules, and other factors with proprietary data the company itself collected, including feeds from a network of passive radar stations it had installed near airports to gather data about every plane in the local sky.

Every 4.

Webinar: Big Data for Strategic Decision Making

This allows sophisticated analysis and pattern matching. When did it actually land? After switching to RightETA, the airline virtually eliminated gaps between estimated and actual arrival times. PASSUR believes that enabling an airline to know when its planes are going to land and plan accordingly is worth several million dollars a year at each airport. Obviously, it would be valuable to combine and make use of all these data to tailor promotions and other offerings to customers, and to personalize the offers to take advantage of local conditions.

2. Build models that predict and optimize business outcomes

Valuable, but difficult: Sears required about eight weeks to generate personalized promotions, at which point many of them were no longer optimal for the company. In search of a faster, cheaper way to do its analytic work, Sears Holdings turned to the technologies and practices of big data.

see url As one of its first steps, it set up a Hadoop cluster. This is simply a group of inexpensive commodity servers whose activities are coordinated by an emerging software framework called Hadoop named after a toy elephant in the household of Doug Cutting, one of its developers. Sears started using the cluster to store incoming data from all its brands and to hold data from existing data warehouses. It then conducted analyses on the cluster directly, avoiding the time-consuming complexities of pulling data from various sources and combining them so that they can be analyzed.

This change allowed the company to be much faster and more precise with its promotions. Because skills and knowledge related to new data technologies were so rare in , when Sears started the transition, it contracted some of the work to a company called Cloudera.