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featuring Andrew McAfee

NoveMber 15, 2013

big Data’s Three big Questions

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OVERVIEWCompanies today are awash in data. It comes from multiple channels, about every aspect of the business. Big data can deliver value for firms but it requires that organizations consider three big questions: 1) how much data are we talking about; 2) can big data transform the business; and 3) what will the challenges be?

One key to success is embracing data-driven decision making. Identifying the best solutions depends on information analysis done by “data geeks,” rather than reliance on the intuition of highly paid decision makers. This approach requires a change in culture along with CEO support.

CONTEXTAndrew McAfee discussed the business case for investing time and resources in big data, and shared real-world examples of companies that are taking advantage of big data.

KEy lEaRNINgsorganizations no longer inhabit a world of just well-organized, numerical data.

The volume, velocity, and variety of data that organizations encounter today are unprecedented.

• Digital data is growing exponentially. We are close to running out of metric system prefixes to describe the world’s digital data volumes. When Teradata was founded in 1979, its leaders chose the name as shorthand for unimaginably large amounts of data. The “tera” prefix worked well for almost 30 years. Yet, between 2008 and 2012, the world moved from the petabyte age to the exabyte revolution, to the zettabyte era. A working group has been formed to extend the metric system.

• The velocity of new data generation outpaces anything seen before. Every second, around 2,500 photos are uploaded to Facebook. Google processes over a billion searches a day and 500 million of those have never been seen before.

• Data is accumulating from new sources and in unfamiliar formats. Smartphones create an exhaust trail of data, thanks to their GPS sensors, accelerometers, and com-passes. Information from the social web comes in many different forms like pictures, status updates, hash tags, videos, search terms, and more. This “ragged” data is amorphous and unstructured, but valuable signals can be derived from it.

CONTRIbuTORsAndrew McAfeePrincipal Research Scientist, Center for Digital Business, MIT Sloan School of Management

Angelia Herrin (Moderator) Editor, Special Projects and Research, Harvard Business Review

November 15, 2013

big Data’s Three big Questions

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November 15, 2013Big Data’s Three Big Questions

On a scale of 1 to 5, McAfee estimates that most companies are at a 0.7 on their big data jour-ney. Islands of innovation and excellence exist; however, most organizations aren’t on top of the volume, velocity, and variety of data that have emerged during the last 5 to 10 years. Busi-nesses that believe they are still in a calm, tidy world of well-organized, numerical data will be left behind.

When used wisely, big data improves forecasts and business operations, as well as enhances innovation.

Big data help enterprises of all kinds look at problems with fresh eyes. New, relevant sources of information can reinvent how business is done. To illustrate this, McAfee provided three case studies:

1. Improving forecasts. In real estate, people want to know how housing prices will change. The status quo approach is using forecasts from the National Association of Realtors (NAR), based on a statistical model. However, MIT professor Erik Brynjolfsson believed a different way to predict housing price changes might exist. He and a doctoral student took freely available search data related to housing prices and built a model. This approach, using publicly available data, is 23.6% more accurate that NAR’s model.

2. Managing business operations. Inexpensive table service restaurant chains suffer from slim profit margins. However, they generate huge amounts of data with their point of sale (POS) terminals. Through pattern matching on this data, NCR (the maker of the POS termi-nals) was able to flag suspicious behavior for restaurant managers. After implementing the new technology, observed theft at restaurants decreased by $25 per site per week.

3. Using big data for innovation. Even successful companies can develop more innovative approaches through data analysis. Google found that people who did well on brain teaser interview questions didn’t have great success at the company. Other factors they looked at during the interview process like GPAs or what school the candidate attended also had no correlation with job success. As a result, Google developed new and different talent man-agement processes.

Adopting data-driven decision making is one of the biggest challenges associated with big data.

Organizations face several challenges associated with big data. Key challenges include:

• Technology. The technology stack is a key issue. However, companies like Teradata do a great job rolling out tools to manage and gain insight from data.

• Skills. If technical teams grew up in the world of Access and SQL databases, they must learn new tools and techniques to manage big data.

• Culture. The greatest challenge is the cultural shift needed to engage in data-driven decision making. At most companies, decisions at all levels are made by the highest paid person; McAfee calls this form of decision making HiPPO (highest paid person’s opinion). Classic HiPPO decisions may use data as an input, but intuition also plays a key role.

“How much data are we talking about? a lot, more all the time, and in formats and forums we don’t have much experience with. This is a brave new world of digital information for the business world.”

—ANdrew mcAfee

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November 15, 2013Big Data’s Three Big Questions

In contrast, “geeky” decision making is based on interrogating data and following what it says. This approach rejects decisions based on intuition or second guessing of the data. Evi-dence suggests that this type of decision making yields better results than HiPPO. Examples include:

— Weather data and Bordeaux wine. When Bordeaux grapes are first harvested, it is difficult to know which wines will mature into great vintages. In the past, HiPPOs tasted the wines and made predictions based on personal taste and experience about which ones would eventually be great. But a Princeton economist concluded that weather was the key variable. He correlated historical weather data with wine values and built a model to predict the eventual quality and price of Bordeaux. Although the wine industry laughed, the model works.

— 2012 presidential election. In the last presidential election, data geeks used polling data to predict the winner. Their results were far more accurate than the pundits’.

both HiPPos and geeks play an important role in deriving value from big data.

Although HiPPO decision making is less effective than a strictly data-driven approach, both HiPPOs and geeks are essential as organizations wade through big data. McAfee made the following recommendations:

1. Ask HiPPOs to point geeks in the right direction. An important job for HiPPOs is knowing which questions to ask and what opportunities to tackle through data analysis. Asking good questions is a subtle art that requires acknowledgment of blind spots and areas of ignorance. Having HiPPOs play a questioning role requires a deep cultural shift.

2. Test and experiment. A productive approach is to engage in incremental experiments, obtain data about the results, and then proceed down the most beneficial path. A test-based approach differs from pilot programs where the goal is to confirm the HiPPO’s beliefs.

3. Give geeks a seat at the decision-making table. When geeks are given more vis-ibility in the organization, it sends a signal that the business is committed to a data-driven approach. Moving to data-driven decision making is a slow, steady process that takes sus-tained CEO leadership.

OTHER ImpORTaNT pOINTs � Finding the right geeks. Internal technology staff can reskill themselves for the big data

world through massive open online courses (MOOCs) and other means. It may be useful to hire one or more data scientists skilled in big data, artificial intelligence, and machine learning.

“ask first what the business needs to get better at. Then you can find out how a data-driven approach can help answer the questions.”

—ANdrew mcAfee

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The information contained in this summary reflects BullsEye Resources, Inc.’s subjective condensed summarization of the applicable conference session. There may bematerial errors, omissions, or inaccuracies in the reporting of the substance of the session. In no way does BullsEye Resources or Harvard Business Review assume anyresponsibility for any information provided or any decisions made based upon the information provided in this document.

November 15, 2013Big Data’s Three Big Questions

bIOgRapHIEsAndrew McAfee

Principal Research Scientist, Center for Digital Business, MIT Sloan School of Management

Andrew McAfee is principal research scientist at the Center for Digital Business in the MIT Sloan School of Management. He is the author of numerous articles and books, including Enterprise 2.0 and Race Against the Machine, which he co-authored with Erik Brynjolfsson. He has written columns for the Washington Post, the Financial Times, and Canadian Manager, and been a guest on the Charlie Rose show. He was previously a professor at Harvard Business School and a fellow at Harvard’s Berkman Center for Inter-net and Society. He received his doctor-ate from Harvard Business School, and completed two Master of Science and two Bachelor of Science degrees at MIT.

Angelia Herrin (Moderator)

Editor for Research and Special Projects, Harvard business review

Angelia Herrin is Editor for Research and Special Projects at Harvard Business Review. At Harvard Business Review, Herrin oversaw the re-launch of the management newsletter line and estab-lished the conference and virtual seminar division for Harvard Business Review. More recently, she created a new series to deliver customized programs and prod-ucts to organizations and associations.

Prior to coming to Harvard Business Review, Herrin was the vice president for content at womenConnect.com, a website focused on women business owners and executives.

Herrin’s journalism experience spans twenty years, primarily with Knight-Ridder newspapers and USA Today. At Knight-Ridder, she covered Congress, as well as the 1988 presidential elections. At USA Today, she worked as Washing-ton editor, heading the 1996 election coverage. She won the John S. Knight Fellowship in Professional Journalism at Stanford University in 1989–90.

Teradata’s Perspective on big Data Every company wants to make the right decisions—for their customers, their employees and shareholders, and the environ-ment—but what does “right” look like, and where do you find it? The answer is found in the transactional, operational, behavioral, and customer data that stream into departmental and corporate databases every day. Yet the intelligent enterprise recognizes that, while data hold the answers, analytics unlocks them.

As the world leader in analytic data platforms, marketing and analytic applications, and services, Teradata helps the world’s leading companies transform data into insights, which lead to actions that make a difference. By matching customized analytics with real-time business needs, Teradata helps organizations do amazing things:

• An automobile manufacturer builds safer, more energy-effi-cient cars.

• An Asian Internet brand develops a new system that acceler-ates response times by 200 to 250 percent.

• A beverage company analyzes the social media buzz from its Super Bowl ads minutes after halftime.

• A European telecommunications provider increases close rates on their offers by more than 200 percent.

Data can help you identify trends, tell you what your customers really think, and even predict outcomes. When you listen, you stop looking at business as usual and start seeing your business as exceptional.

At Teradata, we believe every business has the potential in its data and its people to be exceptional. Our goal is to help you unlock that potential sooner with unique and powerful analytics, and then apply your newfound knowledge in meaningful ways that strengthen your business and improve the lives of those touched by it. Teradata helps take the fear out of big data, so you see opportunities instead of obstacles.

AbouT TerADATA

Teradata, a global leader in analytic data platforms, marketing and analytic applications, and consulting services, helps organi-zations know more so they can do more of what’s really impor-tant. For more information on Teradata’s perspective on Big Data, visit www.Teradata.com/DataDriven.

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