How To Improve Your Business With Analytics

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1 A quarterly journal 2012 Issue 1 06 The third wave of customer analytics 30 The art and science of new analytics technology 44 Natural language processing and social media intelligence 58 Building the foundation for a data science culture Reshaping the workforce with the new analytics Mike Driscoll CEO, Metamarkets

2 Acknowledgments Advisory Principal & Technology Leader Tom DeGarmo US Thought Leadership Partner-in-Charge Tom Craren Strategic Marketing Natalie Kontra Jordana Marx Center for Technology & Innovation Managing Editor Bo Parker Editors Vinod Baya Alan Morrison Contributors Galen Gruman Steve Hamby and Orbis Technologies Bud Mathaisel Uche Ogbuji Bill Roberts Brian Suda Editorial Advisors Larry Marion Copy Editor Lea Anne Bantsari Transcriber Dawn Regan 02 PwC Technology Forecast 2012 Issue 1

3 US studio Design Lead Tatiana Pechenik Designer Peggy Fresenburg Illustrators Don Bernhardt James Millefolie Production Jeff Ginsburg Online Managing Director Online Marketing Jack Teuber Designer and Producer Scott Schmidt Animator Roger Sano Reviewers Jeff Auker Ken Campbell Murali Chilakapati Oliver Halter Matt Moore Rick Whitney Special thanks Cate Corcoran WIT Strategy Nisha Pathak Metamarkets Lisa Sheeran Sheeran/Jager Communication Industry perspectives During the preparation of this publication, we benefited greatly from interviews and conversations with the following executives: Kurt J. Bilafer Regional Vice President, Analytics, Asia Pacific Japan SAP Jonathan Chihorek Vice President, Global Supply Chain Systems Ingram Micro Zach Devereaux Chief Analyst Nexalogy Environics Mike Driscoll Chief Executive Officer Metamarkets Elissa Fink Chief Marketing Officer Tableau Software Kaiser Fung Adjunct Professor New York University Kent Kushar Chief Information Officer E. & J. Gallo Winery Josée Latendresse Owner Latendresse Groupe Conseil Mario Leone Chief Information Officer Ingram Micro Jock Mackinlay Director, Visual Analysis Tableau Software Jonathan Newman Senior Director, Enterprise Web & EMEA esolutions Ingram Micro Ashwin Rangan Chief Information Officer Edwards Lifesciences Seth Redmore Vice President, Marketing and Product Management Lexalytics Vince Schiavone Co-founder and Executive Chairman ListenLogic Jon Slade Global Online and Strategic Advertising Sales Director Financial Times Claude Théoret President Nexalogy Environics Saul Zambrano Senior Director, Customer Energy Solutions Pacific Gas & Electric Reshaping the workforce with the new analytics 03

4 The right data + the right resolution = a new culture of inquiry Tom DeGarmo US Technology Consulting Leader thomas.p.degarmo@us.pwc.com Message from the editor James Balog 1 may have more influence on the global warming debate than any scientist or politician. By using time-lapse photographic essays of shrinking glaciers, he brings art and science together to produce striking visualizations of real changes to the planet. In 60 seconds, Balog shows changes to glaciers that take place over a period of many years introducing forehead-slapping insight to a topic that can be as difficult to see as carbon dioxide. Part of his success can be credited to creating the right perspective. If the photographs had been taken too close to or too far away from the glaciers, the insight would have been lost. Data at the right resolution is the key. Glaciers are immense, at times more than a mile deep. Amyloid particles that are the likely cause of Alzheimer s disease sit at the other end of the size spectrum. Scientists understanding of the role of amyloid particles in Alzheimer s has relied heavily on technologies such as scanning tunneling microscopes. 2 These devices generate visual data at sufficient resolution so that scientists can fully explore the physical geometry of amyloid particles in relation to the brain s neurons. Once again, data at the right resolution together with the ability to visually understand a phenomenon are moving science forward. Science has long focused on data-driven understanding of phenomenon. It s called the scientific method. Enterprises also use data for the purposes of understanding their business outcomes and, more recently, the effectiveness and efficiency of their business processes. But because running a business is not the same as running a science experiment, Davide Brambilla, et al., Nanotechnologies for Alzheimer s disease: diagnosis, therapy, and safety issues, Nanomedicine: Nanotechnology, Biology and Medicine 7, no. 5 (2011): PwC Technology Forecast 2012 Issue 1

5 there has long been a divergence between analytics as applied to science and the methods and processes that define analytics in the enterprise. This difference partly has been a question of scale and instrumentation. Even a large science experiment (setting aside the Large Hadron Collider) will introduce sufficient control around the inquiry of interest to limit the amount of data collected and analyzed. Any large enterprise comprises tens of thousands of moving parts, from individual employees to customers to suppliers to products and services. Measuring and retaining the data on all aspects of an enterprise over all relevant periods of time are still extremely challenging, even with today s IT capacities. But targeting the most important determinants of success in an enterprise context for greater instrumentation often customer information can be and is being done today. And with Moore s Law continuing to pay dividends, this instrumentation will expand in the future. In the process, and with careful attention to the appropriate resolution of the data being collected, enterprises that have relied entirely on the art of management will increasingly blend in the science of advanced analytics. Not surprisingly, the new role emerging in the enterprise to support these efforts is often called a data scientist. This issue of the Technology Forecast examines advanced analytics through this lens of increasing instrumentation. PwC s view is that the flow of data at this new, more complete level of resolution travels in an arc beginning with big data techniques (including NoSQL and in-memory databases), through advanced statistical packages (from the traditional SPSS and SAS to open source offerings such as R), to analytic visualization tools that put interactive graphics in the control of business unit specialists. This arc is positioning the enterprise to establish a new culture of inquiry, where decisions are driven by analytical precision that rivals scientific insight. The first article, The third wave of customer analytics, on page 06 reviews the impact of basic computing trends on emerging analytics technologies. Enterprises have an unprecedented opportunity to reshape how business gets done, especially when it comes to customers. The second article, The art and science of new analytics technology, on page 30 explores the mix of different techniques involved in making the insights gained from analytics more useful, relevant, and visible. Some of these techniques are clearly in the data science realm, while others are more art than science. The article, Natural language processing and social media intelligence, on page 44 reviews many different language analytics techniques in use for social media and considers how combinations of these can be most effective. How CIOs can build the foundation for a data science culture on page 58 considers new analytics as an unusually promising opportunity for CIOs. In the best case scenario, the IT organization can become the go-to group, and the CIO can become the true information leader again. This issue also includes interviews with executives who are using new analytics technologies and with subject matter experts who have been at the forefront of development in this area: Mike Driscoll of Metamarkets considers how NoSQL and other analytics methods are improving query speed and providing greater freedom to explore. Jon Slade of the Financial Times (FT.com) discusses the benefits of cloud analytics for online ad placement and pricing. Jock Mackinlay of Tableau Software describes the techniques behind interactive visualization and how more of the workforce can become engaged in analytics. Ashwin Rangan of Edwards Lifesciences highlights new ways that medical devices can be instrumented and how new business models can evolve. Please visit pwc.com/techforecast to find these articles and other issues of the Technology Forecast online. If you would like to receive future issues of this quarterly publication as a PDF attachment, you can sign up at pwc.com/techforecast/subscribe. As always, we welcome your feedback and your ideas for future research and analysis topics to cover. Reshaping the workforce with the new analytics 05

6 Bahrain World Trade Center gets approximately 15% of its power from these wind turbines 06 PwC Technology Forecast 2012 Issue 1

7 The third wave of customer analytics These days, there s only one way to scale the analysis of customer-related information to increase sales and profits by tapping the data and human resources of the extended enterprise. By Alan Morrison and Bo Parker As director of global online and strategic advertising sales for FT.com, the online face of the Financial Times, Jon Slade says he looks at the 6 billion ad impressions [that FT.com offers] each year and works out which one is worth the most for any particular client who might buy. This activity previously required labor-intensive extraction methods from a multitude of databases and spreadsheets. Slade made the process much faster and vastly more effective after working with Metamarkets, a company that offers a cloud-based, in-memory analytics service called Druid. Before, the sales team would send an to ad operations for an inventory forecast, and it could take a minimum of eight working hours and as long as two business days to get an answer, Slade says. Now, with a direct interface to the data, it takes a mere eight seconds, freeing up the ad operations team to focus on more strategic issues. The parallel processing, in-memory technology, the interface, and many other enhancements led to better business results, including doubledigit growth in ad yields and 15 to 20 percent accuracy improvement in the metrics for its ad impression supply. The technology trends behind FT.com s improvements in advertising operations more accessible data; faster, less-expensive computing; new software tools; and improved user interfaces are driving a new era in analytics use at large companies around the world, in which enterprises make decisions with a precision comparable to scientific insight. The new analytics uses a rigorous scientific method, including hypothesis formation and testing, with science-oriented statistical packages and visualization tools. It is spawning business unit data scientists who are replacing the centralized analytics units of the past. These trends will accelerate, and business leaders Reshaping the workforce with the new analytics 07

8 Figure 1: How better customer analytics capabilities are affecting enterprises More computing speed, storage, and ability to scale Leads to More time and better tools More data sources More focus on key metrics Better access to results Leads to A broader culture of inquiry Leads to Less guesswork Less bias More awareness Better decisions Processing power and memory keep increasing, the ability to leverage massive parallelization continues to expand in the cloud, and the cost per processed bit keeps falling. Data scientists are seeking larger data sets and iterating more to refine their questions and find better answers. Visualization capabilities and more intuitive user interfaces are making it possible for most people in the workforce to do at least basic exploration. Social media data is the most prominent example of the many large data clouds emerging that can help enterprises understand their customers better. These clouds augment data that business units have direct access to internally now, which is also growing. A core single metric can be a way to rally the entire organization s workforce, especially when that core metric is informed by other metrics generated with the help of effective modeling. Whether an enterprise is a gaming or an e-commerce company that can instrument its own digital environment, or a smart grid utility that generates, slices, dices, and shares energy consumption analytics for its customers and partners, better analytics are going direct to the customer as well as other stakeholders. And they re being embedded where users can more easily find them. Visualization and user interface improvements have made it possible to spread ad hoc analytics capabilities across the workplace to every user role. At the same time, data scientists people who combine a creative ability to generate useful hypotheses with the savvy to simulate and model a business as it s changing have never been in more demand than now. The benefits of a broader culture of inquiry include new opportunities, a workforce that shares a better understanding of customer needs to be able to capitalize on the opportunities, and reduced risk. Enterprises that understand the trends described here and capitalize on them will be able to change company culture and improve how they attract and retain customers. who embrace the new analytics will be able to create cultures of inquiry that lead to better decisions throughout their enterprises. (See Figure 1.) This issue of the Technology Forecast explores the impact of the new analytics and this culture of inquiry. This first article examines the essential ingredients of the new analytics, using several examples. The other articles in this issue focus on the technologies behind these capabilities (see the article, The art and science of new analytics technology, on page 30) and identify the main elements of a CIO strategic framework for effectively taking advantage of the full range of analytics capabilities (see the article, How CIOs can build the foundation for a data science culture, on page 58). 08 PwC Technology Forecast 2012 Issue 1

9 More computing speed, storage, and ability to scale Basic computing trends are providing the momentum for a third wave in analytics that PwC calls the new analytics. Processing power and memory keep increasing, the ability to leverage massive parallelization continues to expand in the cloud, and the cost per processed bit keeps falling. FT.com benefited from all of these trends. Slade needs multiple computer screens on his desk just to keep up. His job requires a deep understanding of the readership and which advertising suits them best. Ad impressions appearances of ads on web pages are the currency of high-volume media industry websites. The impressions need to be priced based on the reader segments most likely to see them and click through. Chief executives in France, for example, would be a reader segment FT.com would value highly. The trail of data that users create when they look at content on a website like ours is huge, Slade says. The real challenge has been trying to understand what information is useful to us and what we do about it. FT.com s analytics capabilities were a challenge, too. The way that data was held the demographics data, the behavior data, the pricing, the available inventory was across lots of different databases and spreadsheets, Slade says. We needed an almost witchcraftlike algorithm to provide answers to How many impressions do I have? and How much should I charge? It was an extremely labor-intensive process. FT.com saw a possible solution when it first talked to Metamarkets about an initial concept, which evolved as they collaborated. Using Metamarkets analytics platform, FT.com could quickly iterate and investigate numerous questions to improve its decision-making capabilities. Because our technology is optimized for the cloud, we can harness the processing power of tens, hundreds, or thousands of servers depending on our customers data and their specific needs, states Mike Driscoll, CEO of Metamarkets. We can ask questions over billions of rows of data in milliseconds. That kind of speed combined with data science and visualization helps business users understand and consume information on top of big data sets. Decades ago, in the first wave of analytics, small groups of specialists managed computer systems, and even smaller groups of specialists looked for answers in the data. Businesspeople typically needed to ask the specialists to query and analyze the data. As enterprise data grew, collected from enterprise resource planning (ERP) systems and other sources, IT stored the more structured data in warehouses so analysts could assess it in an integrated form. When business units began to ask for reports from collections of data relevant to them, data marts were born, but IT still controlled all the sources. The second wave of analytics saw variations of centralized top-down data collection, reporting, and analysis. In the 1980s, grassroots decentralization began to counter that trend as the PC era ushered in spreadsheets and other methods that quickly gained widespread use and often a reputation for misuse. Data warehouses and marts continue to store a wealth of helpful data. In both waves, the challenge for centralized analytics was to respond to business needs when the business units themselves weren t sure what findings they wanted or clues they were seeking. The third wave does that by giving access and tools to those who act on the findings. New analytics taps the expertise of the broad business Reshaping the workforce with the new analytics 09

10 Figure 2: The three waves of analytics and the impact of decentralization Cloud computing accelerates decentralization of the analytics function. Cloud co-creation Trend toward decentralization Central IT generated Analytics functions in enterprises were all centralized in the beginning, but not always responsive to business needs Self-service A B C PCs and then the web and an increasingly interconnected business ecosystem have provided more responsive alternatives. Data in the cloud The trend toward decentralization continues as business units, customers, and other stakeholders collaborate to diagnose and work on problems of mutual interest in the cloud. ecosystem to address the lack of responsiveness from central analytics units. (See Figure 2.) Speed, storage, and scale improvements, with the help of cloud co-creation, have made this decentralized analytics possible. The decentralized analytics innovation has evolved faster than the centralized variety, and PwC expects this trend to continue. In the middle of looking at some data, you can change your mind about what question you re asking. You need to be able to head toward that new question on the fly, says Jock Mackinlay, director of visual analysis at Tableau Software, one of the vendors of the new visualization front ends for analytics. No automated system is going to keep up with the stream of human thought. More time and better tools Big data techniques including NoSQL 1 and in-memory databases, advanced statistical packages (from SPSS and SAS to open source offerings such as R), visualization tools that put interactive graphics in the control of business unit specialists, and more intuitive user interfaces are crucial to the new analytics. They make it possible for many people in the workforce to do some basic exploration. They allow business unit data scientists to use larger data sets and to iterate more as they test hypotheses, refine questions, and find better answers to business problems. Data scientists are nonspecialists who follow a scientific method of iterative and recursive analysis with a practical result in mind. Even without formal training, some business users in finance, marketing, operations, human capital, or other departments 1 See Making sense of Big Data, Technology Forecast 2010, Issue 3, for more information on Hadoop and other NoSQL databases. 10 PwC Technology Forecast 2012 Issue 1

11 Case study How the E. & J. Gallo Winery matches outbound shipments to retail customers E. & J. Gallo Winery, one of the world s largest producers and distributors of wines, recognizes the need to precisely identify its customers for two reasons: some local and state regulations mandate restrictions on alcohol distribution, and marketing brands to individuals requires knowing customer preferences. The majority of all wine is consumed within four hours and five miles of being purchased, so this makes it critical that we know which products need to be marketed and distributed by specific destination, says Kent Kushar, Gallo s CIO. Gallo knows exactly how its products move through distributors, but tracking beyond them is less clear. Some distributors are state liquor control boards, which supply the wine products to retail outlets and other end customers. Some sales are through military post exchanges, and in some cases there are restrictions and regulations because they are offshore. Gallo has a large compliance department to help it manage the regulatory environment in which Gallo products are sold, but Gallo wants to learn more about the customers who eventually buy and consume those products, and to learn from them information to help create new products that localize tastes. Gallo sometimes cannot obtain point of sales data from retailers to complete the match of what goes out to what is sold. Syndicated data, from sources such as Information Resources, Inc. (IRI), serves as the matching link between distribution and actual consumption. This results in the accumulation of more than 1GB of data each day as source information for compliance and marketing. Years ago, Gallo s senior management understood that customer analytics would be increasingly important. The company s most recent investments are extensions of what it wanted to do 25 years ago but was limited by availability of data and tools. Since 1998, Gallo IT has been working on advanced data warehouses, analytics tools, and visualization. Gallo was an early adopter of visualization tools and created IT subgroups within brand marketing to leverage the information gathered. The success of these early efforts has spurred Gallo to invest even more in analytics. We went from step function growth to logarithmic growth of analytics; we recently reinvested heavily in new appliances, a new system architecture, new ETL [extract, transform, and load] tools, and new ways our SQL calls were written; and we began to coalesce unstructured data with our traditional structured consumer data, says Kushar. Recognizing the power of these capabilities has resulted in our taking a 10-year horizon approach to analytics, he adds. Our successes with analytics to date have changed the way we think about and use analytics. The result is that Gallo no longer relies on a single instance database, but has created several large purpose-specific databases. We have also created new service level agreements for our internal customers that give them faster access and more timely analytics and reporting, Kushar says. Internal customers for Gallo IT include supply chain, sales, finance, distribution, and the web presence design team. Reshaping the workforce with the new analytics 11

12 Over time, academia and the business software community have collaborated to make analytics tools more userfriendly and more accessible to people who aren t steeped in the mathematical expressions needed to query and get good answers from data. already have the skills, experience, and mind-set to be data scientists. Others can be trained. The teaching of the discipline is an obvious new focus for the CIO. (See the article, How CIOs can build the foundation for a data science culture on page 58.) Visualization tools have been especially useful for Ingram Micro, a technology products distributor, which uses them to choose optimal warehouse locations around the globe. Warehouse location is a strategic decision, and Ingram Micro can run many what-if scenarios before it decides. One business result is shorterterm warehouse leases that give Ingram Micro more flexibility as supply chain requirements shift due to cost and time. Ensuring we are at the efficient frontier for our distribution is essential in this fast-paced and tight-margin business, says Jonathan Chihorek, vice president of global supply chain systems at Ingram Micro. Because of the complexity, size, and cost consequences of these warehouse location decisions, we run extensive models of where best to locate our distribution centers at least once a year, and often twice a year. Modeling has become easier thanks to mixed integer, linear programming optimization tools that crunch large and diverse data sets encompassing many factors. A major improvement came from the use of fast 64-bit processors and solid-state drives that reduced scenario run times from six to eight hours down to a fraction of that, Chihorek says. Another breakthrough for us has been improved visualization tools, such as spider and bathtub diagrams that help our analysts choose the efficient frontier curve from a complex array of data sets that otherwise look like lists of numbers. Analytics tools were once the province of experts. They weren t intuitive, and they took a long time to learn. Those who were able to use them tended to have deep backgrounds in mathematics, statistical analysis, or some scientific discipline. Only companies with dedicated teams of specialists could make use of these tools. Over time, academia and the business software community have collaborated to make analytics tools more user-friendly and more accessible to people who aren t steeped in the mathematical expressions needed to query and get good answers from data. Products from QlikTech, Tableau Software, and others immerse users in fully graphical environments because most people gain understanding more quickly from visual displays of numbers rather than from tables. We allow users to get quickly to a graphical view of the data, says Tableau Software s Mackinlay. To begin with, they re using drag and drop for the fields in the various blended data sources they re working with. The software interprets the drag and drop as algebraic expressions, and that gets compiled into a query database. But users don t need to know all that. They just need to know that they suddenly get to see their data in a visual form. Tableau Software itself is a prime example of how these tools are changing the enterprise. Inside Tableau we use Tableau everywhere, from the receptionist who s keeping track of conference room utilization to the salespeople who are monitoring their pipelines, Mackinlay says. These tools are also enabling more finance, marketing, and operational executives to become data scientists, because they help them navigate the data thickets. 12 PwC Technology Forecast 2012 Issue 1

13 Figure 3: Improving the signal-to-noise ratio in social media monitoring Social media is a high-noise environment But there are ways to reduce the noise And focus on significant conversations work boots leather boots construction rugged safety leather heel color fashion style cool Illuminating and helpful dialogue heel color fashion style leather cool shoes price store boots wear toe safety value rugged shoes price store boots wear toe safety value rugged construction construction An initial set of relevant terms is used to cut back on the noise dramatically, a first step toward uncovering useful conversations. With proper guidance, machines can do millions of correlations, clustering words by context and meaning. Visualization tools present lexical maps to help the enterprise unearth instances of useful customer dialog. Source: Nexalogy Environics and PwC, 2012 More data sources The huge quantities of data in the cloud and the availability of enormous low-cost processing power can help enterprises analyze various business problems including efforts to understand customers better, especially through social media. These external clouds augment data that business units already have direct access to internally. Ingram Micro uses large, diverse data sets for warehouse location modeling, Chihorek says. Among them: size, weight, and other physical attributes of products; geographic patterns of consumers and anticipated demand for product categories; inbound and outbound transportation hubs, lead times, and costs; warehouse lease and operating costs, including utilities; and labor costs to name a few. Social media can also augment internal data for enterprises willing to learn how to use it. Some companies ignore social media because so much of the conversation seems trivial, but they miss opportunities. Consider a North American apparel maker that was repositioning a brand of shoes and boots. The manufacturer was mining conventional business data for insights about brand status, but it had not conducted any significant analysis of social media conversations about its products, according to Josée Latendresse, who runs Latendresse Groupe Conseil, which was advising the company on its repositioning effort. We were neglecting the wealth of information that we could find via social media, she says. To expand the analysis, Latendresse brought in technology and expertise from Nexalogy Environics, a company that analyzes the interest graph implied in online conversations that is, the connections between people, places, and things. (See Transforming collaboration with social tools, Technology Forecast 2011, Issue 3, for more on interest graphs.) Nexalogy Environics studied millions of correlations in the interest graph and selected fewer than 1,000 relevant conversations from 90,000 that mentioned the products. In the process, Nexalogy Environics substantially increased the signal and reduced the noise in the social media about the manufacturer. (See Figure 3.) Reshaping the workforce with the new analytics 13

14 Figure 4: Adding social media analysis techniques suggests other changes to the BI process Here s one example of how the larger business intelligence (BI) process might change Adding with SMA the techniques addition of social media analysis. One apparel maker started with its conventional BI analysis cycle. Conventional BI techniques used by an apparel company client ignored social media and required lots of data cleansing. The results often lacked insight Develop questions 2. Collect data 3. Clean data 4. Analyze data 5. Present results Then it added social media and targeted focus groups to the mix. The company s revised approach added several elements such as social media analysis and expanded others, but kept the focus group phase near the beginning of the cycle. The company was able to mine new insights from social media conversations about market segments that hadn t occurred to the company to target before Develop questions 2. Refine conventional BI - Collect data - Clean data - Analyze data 3. Conduct focus groups (retailers and end users) 4. Select conversations 5. Analyze social media 6. Present results Then it tuned the process for maximum impact. The company s current approach places focus groups near the end, where they can inform new questions more directly. This approach also stresses how the results get presented to executive leadership. New step added Develop questions 2. Refine conventional BI - Collect data - Clean data - Analyze data 3. Select conversations 4. Analyze social media 5. Present results 6. Tailor results to audience 7. Conduct focus groups (retailers and end users) What Nexalogy Environics discovered suggested the next step for the brand repositioning. The company wasn t marketing to people who were blogging about its stuff, says Claude Théoret, president of Nexalogy Environics. The shoes and boots were designed for specific industrial purposes, but the blogging influencers noted their fashion appeal and their utility when riding off-road on all-terrain vehicles and in other recreational settings. That s a whole market segment the company hadn t discovered. Latendresse used the analysis to help the company expand and refine its intelligence process more generally. The key step, she says, is to define the questions that you want to have answered. You will definitely be surprised, because the system will reveal customer attitudes you didn t anticipate. Following the social media analysis (SMA), Latendresse saw the retailer and its user focus groups in a new light. The analysis had more complete results than the focus groups did, she says. You could use the focus groups afterward to validate the information evident in the SMA. The revised intelligence development process now places focus groups closer to the end of the cycle. (See Figure 4.) 14 PwC Technology Forecast 2012 Issue 1

15 Figure 5: The benefits of big data analytics: A carrier example By analyzing billions of call records, carriers are able to obtain early warning of groups of subscribers likely to switch services. Here is how it works: 1 Carrier notes big peaks 2 Dataspora brought in to 3 in churn.* analyze all call records. 14 billion call data records analyzed The initial analysis debunks some myths and raises new questions discussed with the carrier. Dropped calls/poor service? Preferred phone unavailable? Financial trouble? Incarcerated? Carrier s prime hypothesis disproved Merged to family plan? Offer by competitor? Dropped dead? Friend dropped recently! $ $ DON T GO! We ll miss you! $ $ Pattern spotted: Those with a relationship to a dropped customer (calls lasting longer than two minutes, more than twice in the previous month) are 500% more likely to drop. 6 Marketers begin campaigns that target at-risk subscriber groups with special offers. 5 Data group deploys a call record monitoring system that issues an alert that identifies at-risk subscribers. 4 Further analysis confirms that friends influence other friends propensity to switch services. * Churn: the proportion of contractual subscribers who leave during a given time period Source: Metamarkets and PwC, 2012 Third parties such as Nexalogy Environics are among the first to take advantage of cloud analytics. Enterprises like the apparel maker may have good data collection methods but have overlooked opportunities to mine data in the cloud, especially social media. As cloud capabilities evolve, enterprises are learning to conduct more iteration, to question more assumptions, and to discover what else they can learn from data they already have. More focus on key metrics One way to start with new analytics is to rally the workforce around a single core metric, especially when that core metric is informed by other metrics generated with the help of effective modeling. The core metric and the model that helps everyone understand it can steep the culture in the language, methods, and tools around the process of obtaining that goal. A telecom provider illustrates the point. The carrier was concerned about big peaks in churn customers moving to another carrier but hadn t methodically mined the whole range of its call detail records to understand the issue. Big data analysis methods made a large-scale, iterative analysis possible. The carrier partnered with Dataspora, a consulting firm run by Driscoll before he founded Metamarkets. (See Figure 5.) 2 We analyzed 14 billion call data records, Driscoll recalls, and built a high-frequency call graph of customers who were calling each other. We found that if two subscribers who were friends spoke more than once for more than two minutes in a given month and the first subscriber cancelled their contract in October, then the second subscriber became 500 percent more likely to cancel their contract in November. 2 For more best practices on methods to address churn, see Curing customer churn, PwC white paper, publications/curing-customer-churn.jhtml, accessed April 5, Reshaping the workforce with the new analytics 15

16 Through the power of information and presentation, you can start to show customers different ways that they can become stewards of energy. Saul Zambrano, PG&E Data mining on that scale required distributed computing across hundreds of servers and repeated hypothesis testing. The carrier assumed that dropped calls might be one reason why clusters of subscribers were cancelling contracts, but the Dataspora analysis disproved that notion, finding no correlation between dropped calls and cancellation. There were a few steps we took. One was to get access to all the data and next do some engineering to build a social graph and other features that might be meaningful, but we also disproved some other hypotheses, Driscoll says. Watching what people actually did confirmed that circles of friends were cancelling in waves, which led to the peaks in churn. Intense focus on the key metric illustrated to the carrier and its workforce the power of new analytics. Better access to results The more pervasive the online environment, the more common the sharing of information becomes. Whether an enterprise is a gaming or an e-commerce company that can instrument its own digital environment, or a smart grid utility that generates, slices, dices, and shares energy consumption analytics for its customers and partners, better analytics are going direct to the customer as well as other stakeholders. And they re being embedded where users can more easily find them. For example, energy utilities preparing for the smart grid are starting to invite the help of customers by putting better data and more broadly shared operational and customer analytics at the center of a co-created energy efficiency collaboration. Saul Zambrano, senior director of customer energy solutions at Pacific Gas & Electric (PG&E), an early installer of smart meters, points out that policymakers are encouraging more third-party access to the usage data from the meters. One of the big policy pushes at the regulatory level is to create platforms where third parties can assuming all privacy guidelines are met access this data to build business models they can drive into the marketplace, says Zambrano. Grid management and energy management will be supplied by both the utilities and third parties. Zambrano emphasizes the importance of customer participation to the energy efficiency push. The issue he raises is the extent to which blended operational and customer data can benefit the larger ecosystem, by involving millions of residential and business customers. Through the power of information and presentation, you can start to show customers different ways that they can become stewards of energy, he says. As a highly regulated business, the utility industry has many obstacles to overcome to get to the point where smart grids begin to reach their potential, but the vision is clear: Show customers a few key metrics and seasonal trends in an easy-to-understand form. Provide a means of improving those metrics with a deeper dive into where they re spending the most on energy. Allow them an opportunity to benchmark their spending by providing comparison data. This new kind of data sharing could be a chance to stimulate an energy efficiency competition that s never existed between homeowners and between business property owners. It is also an example of how broadening access to new analytics can help create a culture of inquiry throughout the extended enterprise. 16 PwC Technology Forecast 2012 Issue 1

17 Case study Smart shelving: How the E. & J. Gallo Winery analytics team helps its retail partners Some of the data in the E. & J. Gallo Winery information architecture is for production and quality control, not just customer analytics. More recently, Gallo has adopted complex event processing methods on the source information, so it can look at successes and failures early in its manufacturing execution system, sales order management, and the accounting system that front ends the general ledger. Information and information flow are the lifeblood of Gallo, but it is clearly a team effort to make the best use of the information. In this team: Supply chain looks at the flows. Sales determines what information is needed to match supply and demand. R&D undertakes the heavy-duty customer data integration, and it designs pilots for brand consumption. IT provides the data and consulting on how to use the information. what the data reveal (for underlying trends of specific brands by location), or to conduct R&D in a test market, or to listen to the web platforms. These insights inform a specific design for smart shelving, which is the placement of products by geography and location within the store. Gallo offers a virtual wine shelf design schematic to retailers, which helps the retailer design the exact details of how wine will be displayed by brand, by type, and by price. Gallo s wine shelf design schematic will help the retailer optimize sales, not just for Gallo brands but for all wine offerings. Before Gallo s wine shelf design schematic, wine sales were not a major source of retail profits for grocery stores, but now they are the first or second highest profit generators in those stores. Because of information models such as the wine shelf design schematic, Gallo has been the wine category captain for some grocery stores for 11 years in a row so far, says Kent Kushar, CIO of Gallo. Mining the information for patterns and insights in specific situations requires the team. A key goal is what Gallo refers to as demand sensing to determine the stimulus that creates demand by brand and by product. This is not just a computer task, but is heavily based on human intervention to determine Reshaping the workforce with the new analytics 17

18 Conclusion: A broader culture of inquiry This article has explored how enterprises are embracing the big data, tools, and science of new analytics along a path that can lead them to a broader culture of inquiry, in which improved visualization and user interfaces make it possible to spread ad hoc analytics capabilities to every user role. This culture of inquiry appears likely to become the age of the data scientists workers who combine a creative ability to generate useful hypotheses with the savvy to simulate and model a business as it s changing. It s logical that utilities are instrumenting their environments as a step toward smart grids. The data they re generating can be overwhelming, but that data will also enable the analytics needed to reduce energy consumption to meet efficiency and environmental goals. It s also logical that enterprises are starting to hunt for more effective ways to filter social media conversations, as apparel makers have found. The return on investment for finding a new market segment can be the difference between long-term viability and stagnation or worse. Tackling the new kinds of data being generated is not the only analytics task ahead. Like the technology distributor, enterprises in all industries have concerns about scaling the analytics for data they re accustomed to having and now have more. Publishers can serve readers better and optimize ad sales revenue by tuning their engines for timing, pricing, and pinpointing ad campaigns. Telecom carriers can mine all customer data more effectively to be able to reduce the expense of churn and improve margins. What all of these examples suggest is a greater need to immerse the extended workforce employees, partners, and customers in the data and analytical methods they need. Without a view into everyday customer behavior, there s no leverage for employees to influence company direction when One way to raise awareness about the power of new analytics comes from articulating the results in a visual form that everyone can understand. Another is to enable the broader workforce to work with the data themselves and to ask them to develop and share the results of their own analyses. 18 PwC Technology Forecast 2012 Issue 1

19 Table 1: Key elements of a culture of inquiry Element How it is manifested within an organization Value to the organization Executive support Senior executives asking for data to support any opinion or proposed action and using interactive visualization tools themselves Set the tone for the rest of the organization with examples Data availability Cloud architecture (whether private or public) and semantically rich data integration methods Find good ideas from any source Analytics tools Higher-profile data scientists embedded in the business units Identify hidden opportunities Interactive visualization Visual user interfaces and the right tool for the right person Encourage a culture of inquiry Training Power users in individual departments Spread the word and highlight the most effective and user-friendly techniques Sharing Internal portals or other collaborative environments to publish and discuss inquiries and results Prove that the culture of inquiry is real markets shift and there are no insights into improving customer satisfaction. Computing speed, storage, and scale make those insights possible, and it is up to management to take advantage of what is becoming a co-creative work environment in all industries to create a culture of inquiry. Of course, managing culture change is a much bigger challenge than simply rolling out more powerful analytics software. It is best to have several starting points and to continue to find ways to emphasize the value of analytics in new scenarios. One way to raise awareness about the power of new analytics comes from articulating the results in a visual form that everyone can understand. Another is to enable the broader workforce to work with the data themselves and to ask them to develop and share the results of their own analyses. Still another approach would be to designate, train, and compensate the more enthusiastic users in all units finance, product groups, supply chain, human resources, and so forth as data scientists. Table 1 presents examples of approaches to fostering a culture of inquiry. The arc of all the trends explored in this article is leading enterprises toward establishing these cultures of inquiry, in which decisions can be informed by an analytical precision comparable to scientific insight. New market opportunities, an energized workforce with a stake in helping to achieve a better understanding of customer needs, and reduced risk are just some of the benefits of a culture of inquiry. Enterprises that understand the trends described here and capitalize on them will be able to improve how they attract and retain customers. Reshaping the workforce with the new analytics 19

20 The nature of cloudbased data science Mike Driscoll of Metamarkets talks about the analytics challenges and opportunities that businesses moving to the cloud face. Interview conducted by Alan Morrison and Bo Parker Mike Driscoll Mike Driscoll is CEO of Metamarkets, a cloud-based analytics company he co-founded in San Francisco in PwC: What s your background, and how did you end up running a data science startup? MD: I came to Silicon Valley after studying computer science and biology for five years, and trying to reverse engineer the genome network for uranium-breathing bacteria. That was my thesis work in grad school. There was lots of modeling and causal inference. If you were to knock this gene out, could you increase the uptake of the reduction of uranium from a soluble to an insoluble state? I was trying all these simulations and testing with the bugs to see whether you could achieve that. PwC: You wanted to clean up radiation leaks at nuclear plants? MD: Yes. The Department of Energy funded the research work I did. Then I came out here and I gave up on the idea of building a biotech company, because I didn t think there was enough commercial viability there from what I d seen. I did think I could take this toolkit I d developed and apply it to all these other businesses that have data. That was the genesis of the consultancy Dataspora. As we started working with companies at Dataspora, we found this huge gap between what was possible and what companies were actually doing. Right now the real shift is that companies are moving from this very high-latency-course era of reporting into one where they start to have lower latency, finer granularity, and better 20 PwC Technology Forecast 2012 Issue 1