U SE T HE D ATA - fyisolutions.com · Use the Data ! 2 Bryan Eisenberg ! Jeffrey Eisenberg July 1995. A garage-based company launches its website and sells its first item: Fluid Concepts

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U S E T H E D A T A

by Bryan Eisenberg Jeffrey Eisenberg Companion to The Money is in the Data IBM Business Analytics Summit 2013

U s e t h e D a t a s 2

Bryan Eisenberg s Jeffrey Eisenberg

July 1995. A garage-based company launches its website and sells its first item: Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought, by Douglas Hofstadter. Lauded for its unconventional business model, the company continues to develop, but remains unprofitable. In fact, it generates $3 billion in losses.

Fourth quarter 2001. In realizing its first profit—one penny per share—the company proves its business model.

March 2013. Amazon.com is the world’s largest online retailer and a major provider of cloud computing services (as well as several electronic devices.)

How did this happen? Certainly not by accident or luck. Amazon’s success is dependent on maximizing business agility with big-data computing. An ability to understand the value of big data and a willingness to experiment with it have helped Amazon achieve its position.

We are poised at an important moment in the development of digital technologies; everyone needs to be assessing big data strategies. Amazon was fully prepared to use the data.

Now it’s your turn.

Big Data People usually define big data by three qualities: it’s big (volume); it can come from lots of

different places, including unstructured sources (variety); with appropriate computing power, you can use it in real-time (velocity).



Big. Big data is … big. The-mind-can’t-comprehend-it big. We now generate every two days an amount of data equivalent to all the data in the Library of Congress before 2003. In just one minute, 639,800GB of global IP data are transferred:

• 135 botnet infections • 6 new Wikipedia articles are published • 20 new victims of identity theft • 204 million emails sent • 1300 new mobile users are added • 47,000 applications are downloaded • $83,000 in sales take place on Amazon • 61,141 hours of music is streamed on Pandora • 100 new accounts are created on LinkedIn • 3000 images are uploaded to Flickr (20 million are viewed) • 320 new Twitter accounts are created (100,000 tweets are sent) • 277,000 people log in to Facebook (6 million view a Facebook page) • 2 million search queries are entered into Google • 30 hours of video is uploaded to YouTube (1.3 million videos are viewed)

As soon as you read these numbers, they are out of date; the staggering volume of digital

data we are creating is growing exponentially.

Unstructured. The variety of data available to us is mind-boggling. Data can come from internal and external sources, from utilities, government agencies and GPS-enabled devices to radio-frequency identification chips (RFIDs), internal search and clicks on a site.

But one of the most powerful aspects of big data is its ability to compare apples and oranges without having to normalize them as fruit in the database. A majority of the information most valuable to many businesses is often found in unstructured data. As digital technologies for data-mining and text-analyzing develop, we increasingly use these data sources that aren’t well-suited to relational database formats: videos, audio files, images, texts, tweets, Facebook posts, etc.

Real-time. Some online transfers must take place almost instantaneously. If someone just used your credit card illegally, you hope your bank discovers that immediately, not at the end of the month. The velocity at which you can distill useful information from your data and execute a decision confers agility. An agile business has the advantage of being able to respond immediately, in real-time, as opportunities present themselves.



What distinguishes big data from the kind of large-scale data processing that came before?

• Until recently, massive amounts of data were analyzed by sampling. Companies analyzed subsets of data and made assumptions based on those results. With today’s data toolset, companies can analyze this massive volume of data all at once.

• More often than not, companies collected data and analyzed it later. Today, data analysis can take place in real-time. This allows agile organizations to act on data to generate additional business value out of it, no matter the velocity at which it comes into the business.

• Data sources were limited to structured data, the kind of data you can enter into a spreadsheet. Today, a business can tap into the huge pool of unstructured data now available.

• Most importantly, big data can now become the great equalizer for businesses large and small. The cost structure for using these data tools has now made it more widely available and accessible to a greater number of businesses. Data processing on a massive scale used to be cost prohibitive for most businesses. As the technology scales out, big data offers a Global 5 million, not just a Fortune 100, solution.



“Big Data is really about new uses and new insights, not so much the data itself,” says Rod A. Smith, an IBM technical fellow and VP for emerging Internet technologies. Big, unstructured and real-time suggest a world of possibility, and businesses are applying big data in ways that are not only redefining their relationships with their customers, but also helping them maximize profits. This big data phenomenon makes it possible for a company like Amazon to earn an enormous amount of money from an enormous number of customers in a single day, while serving each customer a tailored online experience.

Using the Data Having data is nice. Understanding the value of your data is nicer. Understanding how to

use your data to optimize your business is nicest. Given the shift in marketing from the dominance of creativity to the dominance of data, understanding how to use your data is the marketer’s intellectual mecca. Gone are the days when numbers were for geeks; today’s marketers require competence in both creativity and data analysis.

What data should you use? How should you use it? How should you execute the insights you learn from your data? These are the questions with which businesses are experimenting, and many are coming up with exciting, inspiring, creative and challenging ways to take advantage of big data.

How Has Amazon Used Big Data?

Through innovation and precision execution, Amazon has become a true data-driven

organization and a master at leveraging big data. The site leaves nothing to chance; every little piece of screen real estate and the content of every single product page are optimized for the customer experience.



Amazon optimizes its supply chain, making sure the products customers demand are

available and shipped as quickly and inexpensively as possible. Amazon was the first retailer to leverage social commerce with product reviews. Rather

than leave product reviews as static entities at the bottom of a web page, Amazon continuously optimizes how they are gathered, displayed and even used—interactive options have greatly enlarged the “community” feeling of a product page. Customers greatly value the reviews; Amazon values what those reviews do for sales.



Optimizing the site experience never stops; at any give point in time, the site is running

around 200 tests (Amazon was a pioneer in A/B testing). The company even optimizes how it delivers customer service. All these efficiencies help the company offer the lowest prices while maximizing margin.

When you arrive on a product page, the content Amazon serves up is based on a number of big data parameters that pertain specifically to you: where you are located in relation to warehouses stocking that product; warehouse inventory; previous purchases; site use history; participation in Amazon membership programs; etc. This page is essentially a template made up of basic product information as well as information tailored to you.

Amazon then adds several cross-selling features that are personalized so well they do not feel the least bit creepy and stalker-like (customers highly value these as less generic forms of



search). An alternative-format feature is populated with the most current prices available on Amazon or in the Amazon Marketplace.

All this happens in real-time. You click on a link to a product, land on the page, and there is no perceptible lag to account for the phenomenal amount of processing that went into rendering one page for you, one customer out of over 120 million.

Third-party Big Data Solutions

Amazon designed its own tools, but as the number of businesses providing big data

services expands, it is becoming easier for companies to take advantage of big data’s marketing possibilities without needing to reinvent their own version of the wheel. Here are just a few examples of the many big data solutions available to businesses now.

RetailNext uses sensors to capture kinetic video of in-store traffic, aisle traffic, interaction, dwell time, conversion and sales data, and then turns that video into data. Given 60 retailers and 20,000 sensors, RetailNext collects 57PB of raw data (one million gigabytes), analyzing and visualizing over three trillion data points. Using RetailNext, American Apparel’s same-store sales are up more than 30 percent, theft is down 16 percent and capital expenditures are down 40 percent; Family Dollar redesigned 1300 stores based on the information RetailNext discovered.

McKinsey & Company estimates big data analytics solutions like this can improve retailers’ operating margins by over 60 percent.



Using a technology platform that can be applied across many industries, Automated Insights can take fantasy football end game box scores and produce narratives of games that never physically took place. They create over half a million of these stories a year. The narratives are presented as articles, similar to a feature any newspaper might run—the technology is the author.

A number of online newspapers are very interested in using this merger between sports data and natural language to generate articles on topics publishers want, but do not have the staff, to cover.



Narrative Science is a similar big data/natural language company. Its proprietary process receives the data and creates the article-like narrative. In addition and if appropriate, it generates advice based on the data. A university can provide a student with evaluation of course status, identifying areas in which the student is having difficulty and suggesting specific ways to improve. A financial advisor can offer a customer a clear overview of his portfolio, immediately assess performance and risks, and make recommendations. A cafe can read a summary of sales activity and receive advice for ways to boost profit. A Little League player can read a story of the game in which she just played.

Many websites with in-site search capabilities have a problem with search queries. A business has thousands of products, and there are hundreds of ways to describe each product—there are 627 possible long-tail queries for an iPhone 5 case. Inability to offer the right search results translates into millions of dollars in lost revenue. BloomReach collects data from the site product pages, database descriptions, search queries and external data sources. It then looks at all the images and interpret all the content relationships. Based on



this new understanding, BloomReach starts prioritizing and adapting product page content. Sometimes, it creates an entirely new page.

Working with BloomReach, Neiman Marcus experienced a 31 percent increase in natural search traffic by generating 143,000 new pages in real-time. Humans edited these pages before they went live, but only big data capabilities made possible the creation of this many pages in such a short space of time. Most interesting, 50 percent of BloomSearch-generated visits come from these new thematic pages—pages no one would have created unless they truly understood the nature of the search query (for example, black A-line dress).

Challenges to Using Big Data

Many companies are currently trying to deal with the biggest marketing headaches, which

include the explosion of data, the explosion of social media and the proliferation of channels and devices, and in doing so, they are looking to invest lots of money in big data solutions. In the next five years, CMOs will spend more on information technology than CIOs.

Companies that wish to benefit from their investments in big data tools need skilled data scientists who understand the tools, understand the data and can use both effectively to achieve business goals. Unfortunately, truly qualified people are hard to come by, and industry observers foresee a talent gap. Conservative estimates suggest by 2018, the demand for data scientists will exceed the supply of qualified candidates by 50-60 percent.

Where will we find these individuals? What will we do without them? Big data tools have value only when you have a systematic process in place to manage

optimization. A critical challenge companies face, in addition to nurturing more data



scientists, is how to justify investment in tools that existing staff do not understand and cannot use effectively.

Even when the talent and tool ducks are properly in a row, many companies have to come

to terms with what “big data culture” actually means in the context of the business and how dedicated they actually are to embracing agility. Big organizations get committed to the way things were; the traditional corporate structure virtually mandates the status quo. It is often the case that entrenched corporate structures undermine experimentation and even success by setting up speed bumps and often non-negotiable barriers. We frequently call these obstacles BPUs—business prevention units. They include:

• Technological limitations imagined and real • Legal limitations imagined and real • Organizational culture

Two hours after Michael Jackson’s death was announced, Amazon completely revised its

top-level MP3 category page: Michael Jackson was the sole focus center-screen. Most companies wouldn’t be able to organize a meeting in two hours. In the era of big data and



business agility, companies need to devise ways to rethink their corporate cultures so they can embrace adaptation and promote experimentation.

The Money Is in the Data

Marketers used to proclaim the money is in the list. Today, we need to realize the money is

in the data. As you embark on using big data,

• don’t get caught up in the hype and jargon. Unfortunately, every industry has its love affair with these, but don’t let them distract or dissuade you from the goal

• identify the questions and problems you actually have that need big data solutions • map the valuable data that exists inside and outside your organization • prepare your organization to see the new landscape, circulate articles, help your

organization see the bigger picture for the application of big data • share case studies of other successful uses for big data • look for updates around automation and ‘black box’ applications, which are typically

much easier to deploy • always remain open to experimentation, because, in the upcoming years, the landscape

is going to be changing dramatically based on big data

Documents

U SE T HE D ATA - fyisolutions.com · Use the Data ! 2 Bryan Eisenberg ! Jeffrey Eisenberg July 1995. A garage-based company launches its website and sells its first item: Fluid Concepts