[/caption] For many people, something of a disconnect exists between data analytics and the real world: it’s difficult for them to see how the results of mining giant datasets can have an impact on everyday life. If those people use Orbitz with a Mac, that impact is right in front of them: the travel site relies on predictive analytics, including its users’ choice of operating system, in order to serve particular results. If you’re scanning for travel deals on a Mac, you might see costlier choices than someone accessing the site from a Windows machine. “We had the intuition, and we were able to confirm it based on the data,” Orbitz CTO Roger Liew told The Wall Street Journal June 26, referencing the idea that Mac users are statistically more likely to spend more on accommodations than their Windows brethren. Reports of Orbitz’s data-mining have sparked a minor controversy online (“Creepy or Smart?” asked the Los Angeles Times, for example). As mentioned in the Journal article, Orbitz has experienced revenue losses and falling stock-price over the past few years, driving the company to figure out more innovative ways to earn money from customers: “Orbitz’s chief executive, Barney Harford, has made data mining a priority.” Like many companies, Orbitz is sitting on a massive store of user data, which any number of tools can drill into for insights. But mining data is easier said than done, and some significant obstacles confront even the largest organizations. For one thing, many of those organizations rely on Hadoop, an open-source framework for reliably running distributed applications on large hardware clusters. Although Hadoop has become something of a standard for many smaller companies and larger enterprises, which use it for data-crunching operations, recent surveys indicate a lack of skilled workers capable of operating Hadoop-based systems in an effective way. But Hadoop can also analyze huge data repositories, meaning it’s likely not going away anytime soon. (SlashBI offers some handy best practices for handling Hadoop-related offerings.) Organizations also face the prospect of drowning in the massive amounts of data flooding their data centers. A recent survey commissioned by global consulting firm Capgemini and conducted by The Economist Intelligence Unit found that 43 percent of North American respondents (all of them C-level executives, senior management and IT leaders) felt that an increasing amount of external and internal data had slowed their decision-making. Around 84 percent reported an issue with analyzing and acting on data in a timely manner. “The exploitation of Big Data is fueling a major change in the quality of business decision-making, requiring organizations to adopt new and more effective methods to obtain the most meaningful results,” Scott Schlesinger, Capgemini’s vice president and head of Business Information Management, wrote in a statement accompanying those results. “Organizations that do so will be able to monitor customer behaviors and market conditions with greater certainty.” And that’s certainly what Orbitz is trying with its current data analysis. The question is whether it—or any organization, for that matter—can mine the data in ways that translate into profitable insight. Image: ssuaphotos/Shutterstock.com Orbitz’s Mac User Controversy Shows Data Mining at Work
[caption id="attachment_2082" align="aligncenter" width="500" caption="If you're a Mac user on Orbitz, getting on this plane could cost you a little more, thanks to predictive analytics."] [/caption] For many people, something of a disconnect exists between data analytics and the real world: it’s difficult for them to see how the results of mining giant datasets can have an impact on everyday life. If those people use Orbitz with a Mac, that impact is right in front of them: the travel site relies on predictive analytics, including its users’ choice of operating system, in order to serve particular results. If you’re scanning for travel deals on a Mac, you might see costlier choices than someone accessing the site from a Windows machine. “We had the intuition, and we were able to confirm it based on the data,” Orbitz CTO Roger Liew told The Wall Street Journal June 26, referencing the idea that Mac users are statistically more likely to spend more on accommodations than their Windows brethren. Reports of Orbitz’s data-mining have sparked a minor controversy online (“Creepy or Smart?” asked the Los Angeles Times, for example). As mentioned in the Journal article, Orbitz has experienced revenue losses and falling stock-price over the past few years, driving the company to figure out more innovative ways to earn money from customers: “Orbitz’s chief executive, Barney Harford, has made data mining a priority.” Like many companies, Orbitz is sitting on a massive store of user data, which any number of tools can drill into for insights. But mining data is easier said than done, and some significant obstacles confront even the largest organizations. For one thing, many of those organizations rely on Hadoop, an open-source framework for reliably running distributed applications on large hardware clusters. Although Hadoop has become something of a standard for many smaller companies and larger enterprises, which use it for data-crunching operations, recent surveys indicate a lack of skilled workers capable of operating Hadoop-based systems in an effective way. But Hadoop can also analyze huge data repositories, meaning it’s likely not going away anytime soon. (SlashBI offers some handy best practices for handling Hadoop-related offerings.) Organizations also face the prospect of drowning in the massive amounts of data flooding their data centers. A recent survey commissioned by global consulting firm Capgemini and conducted by The Economist Intelligence Unit found that 43 percent of North American respondents (all of them C-level executives, senior management and IT leaders) felt that an increasing amount of external and internal data had slowed their decision-making. Around 84 percent reported an issue with analyzing and acting on data in a timely manner. “The exploitation of Big Data is fueling a major change in the quality of business decision-making, requiring organizations to adopt new and more effective methods to obtain the most meaningful results,” Scott Schlesinger, Capgemini’s vice president and head of Business Information Management, wrote in a statement accompanying those results. “Organizations that do so will be able to monitor customer behaviors and market conditions with greater certainty.” And that’s certainly what Orbitz is trying with its current data analysis. The question is whether it—or any organization, for that matter—can mine the data in ways that translate into profitable insight. Image: ssuaphotos/Shutterstock.com
[/caption] For many people, something of a disconnect exists between data analytics and the real world: it’s difficult for them to see how the results of mining giant datasets can have an impact on everyday life. If those people use Orbitz with a Mac, that impact is right in front of them: the travel site relies on predictive analytics, including its users’ choice of operating system, in order to serve particular results. If you’re scanning for travel deals on a Mac, you might see costlier choices than someone accessing the site from a Windows machine. “We had the intuition, and we were able to confirm it based on the data,” Orbitz CTO Roger Liew told The Wall Street Journal June 26, referencing the idea that Mac users are statistically more likely to spend more on accommodations than their Windows brethren. Reports of Orbitz’s data-mining have sparked a minor controversy online (“Creepy or Smart?” asked the Los Angeles Times, for example). As mentioned in the Journal article, Orbitz has experienced revenue losses and falling stock-price over the past few years, driving the company to figure out more innovative ways to earn money from customers: “Orbitz’s chief executive, Barney Harford, has made data mining a priority.” Like many companies, Orbitz is sitting on a massive store of user data, which any number of tools can drill into for insights. But mining data is easier said than done, and some significant obstacles confront even the largest organizations. For one thing, many of those organizations rely on Hadoop, an open-source framework for reliably running distributed applications on large hardware clusters. Although Hadoop has become something of a standard for many smaller companies and larger enterprises, which use it for data-crunching operations, recent surveys indicate a lack of skilled workers capable of operating Hadoop-based systems in an effective way. But Hadoop can also analyze huge data repositories, meaning it’s likely not going away anytime soon. (SlashBI offers some handy best practices for handling Hadoop-related offerings.) Organizations also face the prospect of drowning in the massive amounts of data flooding their data centers. A recent survey commissioned by global consulting firm Capgemini and conducted by The Economist Intelligence Unit found that 43 percent of North American respondents (all of them C-level executives, senior management and IT leaders) felt that an increasing amount of external and internal data had slowed their decision-making. Around 84 percent reported an issue with analyzing and acting on data in a timely manner. “The exploitation of Big Data is fueling a major change in the quality of business decision-making, requiring organizations to adopt new and more effective methods to obtain the most meaningful results,” Scott Schlesinger, Capgemini’s vice president and head of Business Information Management, wrote in a statement accompanying those results. “Organizations that do so will be able to monitor customer behaviors and market conditions with greater certainty.” And that’s certainly what Orbitz is trying with its current data analysis. The question is whether it—or any organization, for that matter—can mine the data in ways that translate into profitable insight. Image: ssuaphotos/Shutterstock.com