Notes from the Observation Deck // A Data Revolution


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Notes from the Observation Deck will provide you with an examined look at the interesting phenomena and trends taking place around us today. We present them to you with the hope of sparking broader conversations, debates and ideas. Please use this as a resource for knowledge, inspiration and enjoyment.

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Notes from the Observation Deck // A Data Revolution

  1. 1. Notes from the OBSERVATION DECK 3 A Data Revolution IN THIS ISSUE Big Data Defined Our Digital Footprint Big Data In Action Mobile Data and Privacy
  2. 2. About NOTES FROM THE OBSERVATION DECK IS A QUARTERLY E-ZINE CREATED THROUGH THE BRAND STRATEGY GROUP AT TEAM DETROIT. This publication will provide you with an examined look at the interesting phenomena and trends taking place around us today. We present them to you with the hope of sparking broader conversations, debates and ideas. Please use this e-zine as a resource for innovation, inspiration and enjoyment. In this issue, we’re featuring the Big Data landscape and how it has been influencing consumer insights. If you are interested in telling us what you think or contributing to future issues, please contact Enjoy!
  3. 3. Notes from the Observation Deck Vol.3 Editor-in-Chief Gabrielle George The Major Players Creative in the Contributors Online Video Industry Assistant Editors Morgan Pomish Cameron Quattrone Alex Leonard Derrick Bollin Jessica Vaughn Jenna Thompson Kelly Schneider Kevin Conlon Kristine Taylor Nicholas Oliver Russ Hopkinson To everyone else who contributed, thank you. While Youtube is the most widely used source for accessing online video, a handful of other services have successfully inserted themselves into the conversation as well. Netflix, Hulu and Vimeo have carved out their niche in the online video sphere, and are jockeying for position in the race to online video supremacy. Below is a dive into who they are and how they’re developing in the landscape. YouTube YouTube is a video-sharing website that
  4. 4. Agenda A Data Revolution 1 A Force of Change Letter From the Editor 2 Big Data: Defined The Data Era Connecting the Dots Through Data Visualization Our Digital Footprint Predictive Personalization 3 Big Data in Action: Observations Sephora ColorIQ/Turning Big Data Into Beauty IBM SlamTracker/Home Court Advantage Echo Nest/Music Intelligence for Smarter Listening Data in the Driver’s Seat /Fiat & Volt 4 Mobile Data and Privacy: At a Glance Big Data in the Mobile World Privacy: Walking a Fine Line 5 Forward Thinking: Actionable Insights Wrap-Up Glossary
  5. 5. 1 A Force of Change
  6. 6. Letter From the Editor We’re living in a society where rapid innovation is creating a more informed world. Since 2001, we’ve entered what is referred to as the Big Data Age — a logical progression for a technology-driven world. Big Data provides new tools that help us spot trends, unveil insights for mass and niche targets, and leverage untapped opportunities in a much more informed and efficient manner. of information gathering to fill the entire ocean. The power of Big Data lies in its ability to translate streams of information into predictive insights, allowing companies to dig deeper into business solutions. With the help of data scientists and specialized computer systems, companies can better manage data and identify emerging patterns that give movement to consumer behavior. In this issue of Notes from the Observation Deck, we take a deep dive into the realm of Big Data. In short, Big Data is a collection of information that’s too big to be handled by common computers. If bytes were buckets, it would only take about 20 weeks In this issue, we’ll explore the Big Data era and prove why it is a driving force of change. We hope to introduce you to needto-know terms, best industry practices and how to translate data into killer creative ideas. Thanks for reading! Gabrielle George Notes from the Observation Deck: A Data Revoltion 6
  7. 7. 02. Big Data: Defined It seems hard to believe that the Internet was commercialized less than 20 years ago; that in 1995, people were still trying to understand the meaning of an “@” symbol, and how to pronounce the strangest combination of punctuation and letters they’d ever seen, “.com.” Let’s not forget Facebook and Twitter, sites that didn’t even exist 10 years ago, which are now deeply woven into the world we live in today. Aside from allowing people to connect to the things in which they are interested, these sites have given companies the opportunity to collect a lot of never-before known information and behavioral data on consumers. Today, the depth and width of this data is bigger than ever before, aptly called Big Data. It influences the way we make decisions, how we run businesses, interact with one another and even how we live our daily lives. Big Data is not a trend or a tool; it’s an era. An Oracle study of more than 300 executives in the U.S. and Canada, released in July 2012, indicated 93 percent believe they’re losing revenue opportunities by not being able to leverage the information available to them. Big Data Big data is a collection of information so large and complex that commonly used software tools cannot possibly capture, manage and process it. It is greater in volume, variety and velocity than normal data sets. Volume: To give context to just how much volume Big Data really has, consider this statistic: every two days, we create as much information as we did from the dawn of civilization up until 2003. Big Data is currently measured in petabytes and exabytes, but by the end of 2015, annual global IP traffic is expected to reach the zettabyte threshold. If an 11 oz. cup of coffee represents one gigabyte, then one zettabyte would have the same volume as the Great Wall of China (Cisco Systems Report). Variety: Big Data is diverse because it is being collected in myriad different formats, ranging from tweets and ad clicks, to RFID and GPS signals. The collection of data from this variety of formats and types is what makes Big Data processing multi-structured. Velocity: Input: A majority of incoming data is being collected in real time. Derived Data: Derived data, however, only moves as quickly as the computers that process it. High velocity, or predictive data, requires highly specialized computers that have the ability to process multiple types of data. Facebook’s system processes 2.5 billion pieces of content and 500+ terabytes of data each day. It’s pulling in 2.7 billion “Like” actions and 300 million photos per day, and it scans roughly 105 terabytes of data each half hour (Facebook, 2012).
  8. 8. a wide variety of data is captured at an extremely high velocity the data is collected in extraordinarily large volumes Information Stored Information Processed Usable Information Notes from the Observation Deck: A Data Revoltion 8
  9. 9. CONNECTING THE DOTS THROUGH DATA VISUALIZATION How Humans Deal with Data Data visualization is a creative way of presenting relevant data through visual interpretation. Putting data in a more artistic format helps tell stories about numbers. Data visualizations can take many different forms, ranging from computer graphics and animations to sculptures and music. (Please see page 16 for a data visualization.) Our Digital Footprint People are using new tools to manage their personal data, present it in different ways and discover how much of their information is publicly available. Companies such as Foursquare and Twitter leverage personal information to identify user locations and behaviors. Personal data curation will be a key feature that allows consumers to take greater control of their information. With over 129 million credit cards in circulation, MasterCard® is sitting on top of some of the best shopper data in the world. This past holiday season, the company packaged its customer transaction data and sold it to advertisers through a program called MasterCard Audiences.™ Categories such as holiday travelers, last-minute shoppers and big spenders provide marketers with a deeper understanding of consumer values and alignment with marketing objectives. Customer service is one area that’s set to be upended for the better, thanks to the application of Big Data. Neiman Marcus is piloting an iPhone® app dubbed “NM Service” that enables sales staff to hyper-personalize the treatment of shoppers who use the app. Sales associates can view these customers’ purchase history, as well as items they have “favorited” via the app, then tailor recommendations accordingly. They are even able to stock dressing rooms with relevant items when a customer, who has used the app, enters the store. The more data that you have, the better the model that you will be able to build. Sure, the algorithm is important, but whoever has the most data will win.” – Gil Elbaz, CEO of Factual Increasingly, savvy consumers will start to make the most of their lifestyle data by turning to brands that use this data to proactively offer customers help and advice on how to improve their behavior and/or save money.” – Trendwatching
  10. 10. HOW MUCH DATA IS IT? 1 Megabyte A Small Novel 1 Gigabyte 10 Yards of Books 2 Petabytes All U.S. Research Libraries (Source: 50 Megabytes An Encyclopedia 1 Terabyte 50,000 Trees Made Into Paper and Printed 5 Exabytes All Words Ever Spoken Notes from the Observation Deck: A Data Revoltion 10
  11. 11. OPINION Predictive Personalization JESSICA VAUGHN Senior Trends Strategist JWT
  12. 12. “ ait, how did you W figure that out?” Taking the creep factor out of Big Data application. Tapping into Big Data has obvious advantages for marketers, such as more accurate consumer insights and better-informed decision-making in real time. However, it will take time for consumers to get over the creep factor associated with the idea that so many of their actions and preferences are being tracked and analyzed as they go about their daily lives. The ability to process unstructured data combined with the proliferation of new data sources means marketers will become much more adept at getting the correct product or message in front of the right consumer, at the right moment. However, turning this new treasure trove of information into actionable efforts must be handled gently. Take The New York Times report on Target calculating “pregnancy prediction scores” that determine not only whether a woman is pregnant but her due date within a small margin of error. In one instance, Target mailed coupons for baby goods to a high school student whose father was unaware of her pregnancy. He stormed into Target in a rage, but soon learned his daughter was indeed expecting. Oops. Photo credit: Ford Archives. Consumers will take some time to fully accept that marketers can predict an unnerving amount of information about their lives. A 2012 survey among U.S. and U.K. consumers shows that the majority of people still feel “creeped out” if a retailer sends offers tied to an impending life event. Many say it feels like Big Brother is watching over them, and receiving customized offers based on personal habits and preferences makes them feel anxious. Current perceptions aside, smart use of Big Data can deeply enrich the brand experience, and there are consumers looking for a more personalized approach. Me-centric consumers, armed with information at their fingertips and an array of social media platforms where they can broadcast their thoughts, are accustomed to personalized recommendations from Netflix, Amazon and the like. They expect similarly relevant information across the board, but can’t always find highly personalized offerings and experiences in today’s environment. Marketers who fail to address individual needs will start to fall flat with these consumers, especially Millennials, who are more open to personal data collection and expect brands to use their personal data to tailor offerings. Proper application of data-based programs and common sense courtesy for privacy can go a long way in improving brand experiences and deepening consumer loyalty. As marketers learn the habits and sentiments of individual consumers, they’ll have to be smart about how they implement this knowledge, or risk coming off as invasive. Marketers will need to assure consumers that they’re to be trusted with personal information and explain the purpose behind collecting it. With open dialogue and increasing applications that make consumers’ lives easier, people will be more likely to warm to the idea of highly personalized targeting. Notes from the Observation Deck: A Data Revoltion 12
  13. 13. 3 Big Data in Action: Observations Sephora ColorIQ IBM SlamTracker Turning Big Data into a Beauty Tool Home Court Advantage Beauty mega-giant Sephora is pioneering a personalized service for its clients. In a partnership with the global color authority Pantone, Sephora created the most advanced foundation color-matching service available in North American beauty retail. IBM recently took advantage of its Big Data through a sponsorship of the US Open tennis championship, using data to create a new experience for tennis fans. The company created an app called SlamTracker, which provided live video, competition highlights and real time data and statistics that enhanced the fan experience in a way never done before. By leveraging the company’s own analytic algorithms and real time statistics of the players, the app predicted what each player needed to do to improve their chances of winning against opponents. For example, IBM demonstrated how a player like Andy Murray’s best chances of winning the tournament were in his ability to win more than 43% of three to eight shot rallies. Andy Murray went on to accomplish this 49% of the time en route to his first US Open title. Sephora’s ColorIQ utilizes two devices during the foundation consultation with the client. First, the client’s skin tone is captured with the Pantone CAPSURE,™ a handheld device that evaluates the client’s tone in less than two seconds. From that device, an official PANTONE SkinTone number is assigned to the client. Then, the color consultant inputs the number into Sephora’s Universal SkinTone Library (on an iPad®), which holds over 1,000 foundation SKUs across all of Sephora’s brands to determine which products offer a precise match. Sephora has collected data from women nationwide and combined it with the new technology. As a result, clients are matched with the perfect shade of foundation based on thousands of choices. Choosy shoppers can filter options even further by indicating a brand preference, foundation type and more. Sephora plans to have ColorIQ technology in all its stores by the end of 2013 – turning Big Data into a beauty tool. Image Credit: Alex Leonard The IBM SlamTracker brought fans closer to the matches, their favorite players, and justified its sponsorship presence in the eyes of fans and consumers at the event. The key to IBM’s success was its ability to communicate the brand’s capabilities in a meaningful and engaging way. The same technology used by SlamTracker is representative of IBM’s business processes, to leverage and effectively use data to make smarter business decisions. By communicating at an event like the US Open, IBM demonstrated the company’s ability to translate its data in a more human-friendly way. Notes from the Observation Deck: A Data Revoltion 14
  14. 14. Echo Nest Music Intelligence for Smarter Listening “Billy Jean” Michael Jackson It has been estimated that 97 million songs exist in the world today, and new songs are being written all the time, creating infinite amounts of musical information. Managing that kind of data is a daunting task, but music intelligence company Echo Nest has positioned itself as an industry leader. Echo Nest uses Big Data analytics to aggregate and index massive amounts of music data, helping application developers build smarter music apps. The company has the world’s largest repository of music data with over 30 million songs and more than a trillion data points. Start infinite Unlike Pandora® internet radio, which relies on human input to categorize songs, Echo Nest’s song analysis is done automatically using the Echo Nest analyzer. The platform constantly searches the Web for music and related content, gathering data about a song’s key, tempo and style, as well as reviews and even consumers’ tweets. Echo Nest markets its data to power popular music services’ custom playlists and radio stations such as iHeartRadio,® Spotify, Nokia and VEVO. Infinite Jukebox Songs are broken down beat-by-beat, looking at pitch, timbre, loudness, duration and the position of the beat within the song. An analysis is run on all the beats, finding each one’s nearest similar-sounding neighbor. As the song plays, the algorithm determines whether it will play as normal, or if it will jump to a different part of the song that sounds similar to the current beats. The result is an infinitely looping version of the song. Each track is turned into an organic, circular visualization. “Take 5” Dave Brubeck
  15. 15. Notes from the Observation Deck: A Data Revoltion 16
  16. 16. OPINION Data in the Driver’s Seat FIAT AND VOLT RUSS HOPKINSON Cross-Channel Strategist In-vehicle technology is consistently ranked as one of the top desires for potential automotive buyers. Much of the data and sensors that can improve the future of driving and enable more human-computer interaction already exist. A car takes inputs in the form of personal and environmental data, processes it, presents options and makes changes to the driving experience based on the driver’s response. What’s remarkable for the auto industry is that cars had onboard computers as early as 1968. The car in your driveway probably knows if there is a passenger in the passenger seat, the oxygen levels in the air, the degree of turn in the steering wheel, the location of the vehicle, the temperature in the engine, in the passenger compartment and outside the vehicle, if it is raining, how far you are from the car in front
  17. 17. On the one hand, information wants to be expensive, because it’s so valuable. The right information in the right place just changes your life. On the other hand, information wants to be free, because the cost of getting it out is getting lower and lower all the time. So you have these two fighting against each other.” – Stewart Brand, Author, “Whole Earth Discipline” of you or behind you, and if the road is bumpy. Up until recently, all of these sensor outputs were mostly invisible to users, and were for vehicle performance and diagnostics. But marketers have started to grasp the importance of adding value to customer experiences and have been playing with data as a way to do that. Case in point: Fiat Eco Drive Using a little bit of vehicle data, Fiat analyzed personal driving habits and offered tips on how to change driving behavior to optimize fuel efficiency. The results – 30,000 registrants reduced CO2 emissions by 2.6 million kg, and AKQA won a Grand Prix Cyber Lion at Cannes. Customers have started to realize that any data they produce is their own and has value. If companies do not meet this customer need, someone else will. Case in point: is a site built by a Volt fan, allowing owners to be included on a fuel efficiency leaderboard. This is enabled by tapping into the OnStar®-powered Chevy Volt API. The real time data shows miles driven, miles driven in electric- only mode, miles per tank, an anonymous map of owners and other data. The site is so popular that approximately 27% of all Volt drivers have signed up to share their vehicle data. Although most OEMs are not making it easy for developers to get vehicle data, it has not stopped developers from building vehicle-related apps. A few examples include: ● aze – a navigation app that leverages data from its W users to identify traffic and reroute you around it ● PS Tracks – allows you to track, record and map G your location ● ev – taps directly into your car’s engine control R unit through the onboard diagnostics port to provide vehicle diagnostics, data visualizations of performance and other tools for performance enthusiasts The bottom line is that your car is a sensor-laden computer from which data streams. As it becomes easier to access this data through application programming interfaces (APIs) provided by automotive OEMs, developers, marketers and data scientists will harness it to add value to customers. The opportunity is for an enhanced customer experience, a deeper relationship and competitive differentiation to first movers. Notes from the Observation Deck: A Data Revoltion 18
  19. 19. BIG DATA IN THE MOBILE WORLD Data Street: How the Communication Channel Between Phone and User Has Evolved 1 The Early Cell Phone ● communication device a between two parties Your phone starts your car’s engine for you. Your phone reminds you to wear a hat. “It’s cold!” A detour to the hardware store is automatically mapped. Your “to-do” list includes buying a hammer. Your phone says, “You have time to stop for coffee.” “And the cheapest gas in the city is on the way too.” As we venture toward the mobile 3.0 era, the userto-phone communication channel is evolving. Embedded sensors in the user’s smartphone are constantly collecting data, creating streams of information about users’ behaviors, habits and routines. You order a pepperoni pizza with your phone. Automatic check-in at all stops including the pizzeria. “Passed by 2 friends en-route, maybe they’re hungry.” 2 Greater Options ● oice-recognition and v touch-screen interfaces ● hone offers location and p filtered preferences ● ata begins to flow back to d the user through software 3 A Multichannel Device ● constant data exchange a between user and device ● llows developers to tap into a the consumer’s lifestyle ● hone can “predict” what p user will do ● ill be able to offer content w and suggestions in real time ● ata will be pushed out as fast d as it comes in Notes From The Observation Deck: A Data Revoltion 20
  20. 20. OPINION Privacy: Walking a Fine Line KEVIN CONLON Emerging Technologies Campaign Manager There is a growing awareness of the importance of balancing personally identifiable information (PII) and the desire to improve user experience and relevancy through data usage. The collection of user history is valuable because it allows marketers to identify consumer patterns as opposed to recording a single site visit, which doesn’t identify the entire consumer story. T he collection of Big Data on individual users within mobile advertising is limited today, and is likely to continue to be the case until privacy issues are solidified. Online privacy is a complex issue and there is no one-size-fits-all answer, but consumers are willing to share data when there is an equitable value exchange (Robert Cell, CEO of MyBuys). As the industry develops overall standards and methods, many of the inconsistencies surrounding data collection will begin to level off and be standardized. A s advertisers develop more sophisticated online consumer tracking tools, federal agencies and consumer interest groups are pushing for stricter privacy standards. Do Not Track, the latest Webtracking opt-out movement, is gaining ground with browsers and companies alike being pressured into providing simple opt-out measures. Do Not Track continues to be an ongoing part of the discussion around overall privacy and tracking cookies, and mobile is no exception. Mobile tracking is accomplished in three ways: 1. Session Tracking: The time spent by a single user on a website. The problem with session tracking is the moment the session is over, the data used to identify the customer is immediately wiped out. Without tracking a consumer for a historical period of time, advertisers are prohibited from understanding true consumer behavior. 2. Cookie Tracking: The collection of consumer behavior throughout a whole mobile experience is never erased. Many marketers prefer cookie tracking through ad servers and conversion tracking companies, but scale is still a factor. Publishers are moving to this format in lieu of session-bysession tracking, because it allows consumers to be measured from first impression through to the end of their mobile session. However, cookie tracking today is limited to certain mobile phone operating systems and is not considered reliable in mobile and tablet marketing, like it is on desktop. 3. Registration Data: Many content companies – such as Google, Amazon and Microsoft® – are now relying on consumer registration data and common log-in data across multiple devices, to provide relevant and timely advertising messages and content.
  21. 21. Internet users who said they knew how to limit information about themselves that is collected by an online company. Internet users who said they would not allow an online search engine to keep track of their queries, even if the data provided personalized results in the future. Internet users who said they have noticed online advertising that was directly targeted to them. Internet users who said they have an unfavorable view of targeted advertising. Internet users who said they allow targeted advertising because they see advertisments pertaining to their interests. Social networking site users who have deleted comments made by others on their profiles. (Source: Pew, Search Engine Use, Feb. 2012) Notes from the Observation Deck: A Data Revoltion 22
  22. 22. 5 Forward Thinking: Actionable Insights W R A P U P Big Data is here to stay; it’s interesting, it’s complex, and it can be controversial. Considering there is no longer a one-size-fits-all approach, what opportunities do we need to embrace?
  23. 23. Glossary Algorithm: A progressive procedure for calculations used for data processing and automated reasoning. Big Data: A collection of information that’s too big to be handled by common computers. new paths. Finally, action is taken based on the data triggering another data pull. Automatic Programming Interface [API]: Interface used by software components to communicate with each other. An API allows the software element to talk to the hardware device, and vice versa. Feedback Loop: In the first stage, a behavior is measured, captured and stored. Then, the raw data is relayed in an emotionally resonant context. The third stage is that the relayed information shines light on Hadoop: Free, Java-based programming framework that supports the processing of large data sets in a computing environment. [The term] Big Data...will fade, over time, into the pantheon of other tech buzzwords. But the data it represents won’t – it exists now, and continues to grow. So it’s OK to allow the term for now, learn the concepts it presents, and bake it into what you do today. Big Data will only get bigger. And that’s not just hype.” Big Data, like the PC revolution of the ’80s, the emergence of the Internet in the ’90s, and Web 2.0 in the 2000s, represents a severalhundred-billion-dollar wealth creation opportunity.” Data is power. Once you have that, you can understand where you have problems, how to reroute and how to redirect people to modes of transport they weren’t even thinking about when they started their day.” – Dow Corporate Venture Capital – Barb Samardzich, vicepresident of product development at Ford of Europe – Buck Woody, Simple Talk Notes from the Observation Deck: A Data Revoltion 24
  24. 24. YOUTUBE LOGO SPECS PRINT on light backgrounds on dark backgrounds standard no gradients no gradients watermark stacked logo (for sharing only) stacked logo (for sharing only) gradient bottom C0 M96 Y90 K2 standard watermark main red C13 M96 Y81 K54 PMS 1795C PMS 1815C white black C0 M0 Y0 K0 C100 M100 Y100 K100 WHITE BLACK 550 Town Center Dr, Dearborn, MI 48126 All rights reserved. © 2013 Notes from the Observation Deck