Technology Forecast: Reshaping the workforce with the new analytics


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В обзоре Technology Forecast: Reshaping the workforce with the new analytics исследуется воздействие новых аналитических инструментов и культуры работы с данными, которую организации могут создать с помощью новых инструментов и услуг по анализу данных.

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Technology Forecast: Reshaping the workforce with the new analytics

  1. 1. A quarterly journal 06 30 44 582012 The third wave of The art and science Natural language Building the foundationIssue 1 customer analytics of new analytics processing and social for a data science culture technology media intelligence Reshaping the workforce with the new analytics Mike Driscoll CEO, Metamarkets
  2. 2. Acknowledgments Advisory Center for Technology Principal & Technology Leader & Innovation Tom DeGarmo Managing Editor Bo Parker US Thought Leadership Partner-in-Charge Editors Tom Craren Vinod Baya Alan Morrison Strategic Marketing Natalie Kontra Contributors Jordana Marx 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 Regan02 PwC Technology Forecast 2012 Issue 1
  3. 3. US studio Industry perspectives Jonathan NewmanDesign Lead During the preparation of this Senior Director, Enterprise Web & EMEATatiana Pechenik publication, we benefited greatly eSolutions from interviews and conversations Ingram MicroDesigner with the following executives:Peggy Fresenburg Ashwin Rangan Kurt J. Bilafer Chief Information OfficerIllustrators Regional Vice President, Analytics, Edwards LifesciencesDon Bernhardt Asia Pacific JapanJames Millefolie SAP Seth Redmore Vice President, Marketing and Product Jonathan Chihorek ManagementProduction Vice President, Global Supply Chain LexalyticsJeff Ginsburg Systems Ingram Micro Vince SchiavoneOnline Co-founder and Executive ChairmanManaging Director Online Marketing Zach Devereaux ListenLogicJack Teuber Chief Analyst Nexalogy Environics Jon SladeDesigner and Producer Global Online and Strategic AdvertisingScott Schmidt Mike Driscoll Sales Director Chief Executive Officer Financial TimesAnimator MetamarketsRoger Sano Claude Théoret Elissa Fink PresidentReviewers Chief Marketing Officer Nexalogy EnvironicsJeff Auker Tableau SoftwareKen Campbell Saul ZambranoMurali Chilakapati Kaiser Fung Senior Director,Oliver Halter Adjunct Professor Customer Energy SolutionsMatt Moore New York University Pacific Gas & ElectricRick Whitney Kent KusharSpecial thanks Chief Information OfficerCate Corcoran E. & J. Gallo WineryWIT Strategy Josée LatendresseNisha Pathak OwnerMetamarkets Latendresse Groupe ConseilLisa Sheeran Mario LeoneSheeran/Jager Communication Chief Information Officer Ingram Micro Jock Mackinlay Director, Visual Analysis Tableau Software Reshaping the workforce with the new analytics 03
  4. 4. The right data + the right resolution = a new culture of inquiry Message from the editor disease sit at the other end of the size James Balog1 may have more influence spectrum. Scientists’ understanding on the global warming debate than of the role of amyloid particles in any scientist or politician. By using Alzheimer’s has relied heavily on time-lapse photographic essays of technologies such as scanning tunneling shrinking glaciers, he brings art and microscopes.2 These devices generate science together to produce striking visual data at sufficient resolution visualizations of real changes to so that scientists can fully explore the planet. In 60 seconds, Balog the physical geometry of amyloid shows changes to glaciers that take particles in relation to the brain’s place over a period of many years— neurons. Once again, data at the right introducing forehead-slapping resolution together with the ability to insight to a topic that can be as visually understand a phenomenon difficult to see as carbon dioxide. are moving science forward. Part of his success can be credited to creating the right perspective. If the Science has long focused on data-driven photographs had been taken too close understanding of phenomenon. It’sTom DeGarmo to or too far away from the glaciers, called the scientific method. EnterprisesUS Technology Consulting Leader the insight would have been lost. Data also use data for the purposes at the right resolution is the key. understanding their business outcomes and, more recently, the effectiveness and Glaciers are immense, at times more efficiency of their business processes. than a mile deep. Amyloid particles But because running a business is not the that are the likely cause of Alzheimer’s same as running a science experiment, 1 2 Davide Brambilla, et al., “Nanotechnologies for Alzheimer’s disease: diagnosis, therapy, and safety issues,” Nanomedicine: Nanotechnology, Biology and Medicine 7, no. 5 (2011): 521–540.04 PwC Technology Forecast 2012 Issue 1
  5. 5. there has long been a divergence with big data techniques (including This issue also includes interviewsbetween analytics as applied to science NoSQL and in-memory databases), with executives who are using newand the methods and processes that through advanced statistical packages analytics technologies and with subjectdefine analytics in the enterprise. (from the traditional SPSS and SAS matter experts who have been at the to open source offerings such as R), forefront of development in this area:This difference partly has been a to analytic visualization tools that putquestion of scale and instrumentation. interactive graphics in the control of • Mike Driscoll of MetamarketsEven a large science experiment (setting business unit specialists. This arc is considers how NoSQL and otheraside the Large Hadron Collider) will positioning the enterprise to establish analytics methods are improvingintroduce sufficient control around the a new culture of inquiry, where query speed and providinginquiry of interest to limit the amount of decisions are driven by analytical greater freedom to collected and analyzed. Any large precision that rivals scientific insight.enterprise comprises tens of thousands • Jon Slade of the Financial Timesof moving parts, from individual The first article, “The third wave of ( discusses the benefitsemployees to customers to suppliers to customer analytics,” on page 06 reviews of cloud analytics for onlineproducts and services. Measuring and the impact of basic computing trends ad placement and pricing.retaining the data on all aspects of an on emerging analytics technologies.enterprise over all relevant periods of Enterprises have an unprecedented • Jock Mackinlay of Tableau Softwaretime are still extremely challenging, opportunity to reshape how business describes the techniques behindeven with today’s IT capacities. gets done, especially when it comes interactive visualization and to customers. The second article, how more of the workforce canBut targeting the most important “The art and science of new analytics become engaged in analytics.determinants of success in an enterprise technology,” on page 30 explores thecontext for greater instrumentation— mix of different techniques involved • Ashwin Rangan of Edwardsoften customer information—can be and in making the insights gained from Lifesciences highlights newis being done today. And with Moore’s analytics more useful, relevant, and ways that medical devices canLaw continuing to pay dividends, this visible. Some of these techniques are be instrumented and how newinstrumentation will expand in the clearly in the data science realm, while business models can evolve.future. In the process, and with careful others are more art than science. Theattention to the appropriate resolution article, “Natural language processing Please visit the data being collected, enterprises and social media intelligence,” on to find these articles and other issuesthat have relied entirely on the art of page 44 reviews many different of the Technology Forecast will increasingly blend in language analytics techniques in use If you would like to receive futurethe science of advanced analytics. Not for social media and considers how issues of this quarterly publication assurprisingly, the new role emerging in combinations of these can be most a PDF attachment, you can sign up atthe enterprise to support these efforts effective.“How CIOs can build the often called a “data scientist.” foundation for a data science culture” on page 58 considers new analytics as As always, we welcome your feedbackThis issue of the Technology Forecast an unusually promising opportunity and your ideas for future researchexamines advanced analytics through for CIOs. In the best case scenario, and analysis topics to cover.this lens of increasing instrumentation. the IT organization can become thePwC’s view is that the flow of data go-to group, and the CIO can becomeat this new, more complete level of the true information leader again.resolution travels in an arc beginning Reshaping the workforce with the new analytics 05
  6. 6. Bahrain World Trade Center gets approximately 15% of its power from these wind turbines06 PwC Technology Forecast 2012 Issue 1
  7. 7. The third wave ofcustomer analyticsThese days, there’s only one way to scale theanalysis of customer-related information toincrease sales and profits—by tapping the dataand human resources of the extended enterprise.By Alan Morrison and Bo ParkerAs director of global online and strategic issues. The parallel processing,strategic advertising sales for, in-memory technology, the interface,the online face of the Financial Times, and many other enhancements led toJon Slade says he “looks at the 6 billion better business results, including double-ad impressions [that offers] digit growth in ad yields and 15 to 20each year and works out which one percent accuracy improvement in theis worth the most for any particular metrics for its ad impression supply.client who might buy.” This activitypreviously required labor-intensive The technology trends behindextraction methods from a multitude’s improvements in advertisingof databases and spreadsheets. Slade operations—more accessible data;made the process much faster and faster, less-expensive computing; newvastly more effective after working software tools; and improved userwith Metamarkets, a company that interfaces—are driving a new era inoffers a cloud-based, in-memory analytics use at large companies aroundanalytics service called Druid. the world, in which enterprises make decisions with a precision comparable“Before, the sales team would send to scientific insight. The new analyticsan e-mail to ad operations for an uses a rigorous scientific method,inventory forecast, and it could take including hypothesis formation anda minimum of eight working hours testing, with science-oriented statisticaland as long as two business days to packages and visualization tools. It isget an answer,” Slade says. Now, with spawning business unit “data scientists”a direct interface to the data, it takes who are replacing the centralizeda mere eight seconds, freeing up the analytics units of the past. These trendsad operations team to focus on more will accelerate, and business leaders Reshaping the workforce with the new analytics 07
  8. 8. Figure 1: How better customer analytics capabilities are affecting enterprises Processing power and memory keep increasing, the More computing speed, ability to leverage massive parallelization continues to storage, and ability to scale expand in the cloud, and the cost per processed bit keeps falling. Leads to Data scientists are seeking larger data sets and iterating More time and better tools 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 More data sources 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 More focus on key metrics 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 Better access to results company that can instrument its own digital environ- ment, or a smart grid utility that generates, slices, dices, and shares energy consumption analytics for its customers and partners, better analytics are going Leads to 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 A broader culture of inquiry 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 Leads 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 Less guesswork opportunities, a workforce that shares a better under- standing of customer needs to be able to capitalize on Less bias the opportunities, and reduced risk. Enterprises that More awareness understand the trends described here and capitalize Better decisions on them will be able to change company culture and improve how they attract and retain customers. who embrace the new analytics will be in this issue focus on the technologies able to create cultures of inquiry that behind these capabilities (see the lead to better decisions throughout article, “The art and science of new their enterprises. (See Figure 1.) analytics technology,” on page 30) and identify the main elements of a This issue of the Technology Forecast CIO strategic framework for effectively explores the impact of the new taking advantage of the full range of analytics and this culture of inquiry. analytics capabilities (see the article, This first article examines the essential “How CIOs can build the foundation for ingredients of the new analytics, using a data science culture,” on page 58). several examples. The other articles08 PwC Technology Forecast 2012 Issue 1
  9. 9. More computing speed, decision-making capabilities. “Becausestorage, and ability to scale our technology is optimized for theBasic computing trends are providing cloud, we can harness the processingthe momentum for a third wave power of tens, hundreds, or thousandsin analytics that PwC calls the new of servers depending on our customers’analytics. Processing power and data and their specific needs,” statesmemory keep increasing, the ability Mike Driscoll, CEO of leverage massive parallelization “We can ask questions over billionscontinues to expand in the cloud, and of rows of data in milliseconds. Thatthe cost per processed bit keeps falling. kind of speed combined with data science and visualization helps benefited from all of these users understand and consumetrends. Slade needs multiple computer information on top of big data sets.”screens on his desk just to keep up. Hisjob requires a deep understanding of Decades ago, in the first wave ofthe readership and which advertising analytics, small groups of specialistssuits them best. Ad impressions— managed computer systems, and evenappearances of ads on web pages— smaller groups of specialists looked forare the currency of high-volume media answers in the data. Businesspeopleindustry websites. The impressions typically needed to ask the specialistsneed to be priced based on the reader to query and analyze the data. Assegments most likely to see them and enterprise data grew, collected fromclick through. Chief executives in enterprise resource planning (ERP)France, for example, would be a reader systems and other sources, IT stored thesegment would value highly. more structured data in warehouses so analysts could assess it in an integrated“The trail of data that users create form. When business units began towhen they look at content on a website ask for reports from collections of datalike ours is huge,” Slade says. “The relevant to them, data marts were born,real challenge has been trying to but IT still controlled all the sources.understand what information is usefulto us and what we do about it.” The second wave of analytics saw variations of centralized top-down’s analytics capabilities were collection, reporting, and analysis. Ina challenge, too. “The way that data the 1980s, grassroots decentralizationwas held—the demographics data, the began to counter that trend as the PCbehavior data, the pricing, the available era ushered in spreadsheets and otherinventory—was across lots of different methods that quickly gained widespreaddatabases and spreadsheets,” Slade use—and often a reputation for misuse.says. “We needed an almost witchcraft- Data warehouses and marts continuelike algorithm to provide answers to to store a wealth of helpful data.‘How many impressions do I have?’ and‘How much should I charge?’ It was an In both waves, the challenge forextremely labor-intensive process.” centralized analytics was to respond to business needs when the business saw a possible solution when themselves weren’t sure what findingsit first talked to Metamarkets about they wanted or clues they were initial concept, which evolved asthey collaborated. Using Metamarkets’ The third wave does that by givinganalytics platform, could access and tools to those who actquickly iterate and investigate on the findings. New analytics tapsnumerous questions to improve its the expertise of the broad business Reshaping the workforce with the new analytics 09
  10. 10. Figure 2: The three waves of analytics and the impact of decentralization Cloud computing accelerates decentralization of the analytics function. Cloud co-creation Self-service Data in theTrend toward decentralization cloud Central IT generated C B A 1 2 3 4 The trend toward 5 decentralization continues as 6 7 business units, customers, and other stakeholders collaborate to diagnose and work on PCs and then the web and an problems of mutual interest in increasingly interconnected the cloud. business ecosystem have provided Analytics functions in enterprises more responsive alternatives. were all centralized in the beginning, but not always responsive to business needs. ecosystem to address the lack of More time and better tools responsiveness from central analytics Big data techniques—including NoSQL1 units. (See Figure 2.) Speed, storage, and in-memory databases, advanced and scale improvements, with the statistical packages (from SPSS and help of cloud co-creation, have SAS to open source offerings such as R), made this decentralized analytics visualization tools that put interactive possible. The decentralized analytics graphics in the control of business innovation has evolved faster than unit specialists, and more intuitive the centralized variety, and PwC user interfaces—are crucial to the new expects this trend to continue. analytics. They make it possible for many people in the workforce to do “In the middle of looking at some data, some basic exploration. They allow you can change your mind about what business unit data scientists to use larger question you’re asking. You need to be data sets and to iterate more as they test able to head toward that new question hypotheses, refine questions, and find on the fly,” says Jock Mackinlay, better answers to business problems. director of visual analysis at Tableau Software, one of the vendors of the new Data scientists are nonspecialists visualization front ends for analytics. who follow a scientific method of “No automated system is going to keep iterative and recursive analysis with a up with the stream of human thought.” 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, forecast/2010/issue3/index.jhtml, for more information on Hadoop and other NoSQL databases. 10 PwC Technology Forecast 2012 Issue 1
  11. 11. Case study How the E. & J. Gallo Winery matches outbound shipments to retail customers E. & J. Gallo Winery, one of the world’s Years ago, Gallo’s senior management largest producers and distributors of understood that customer analytics wines, recognizes the need to precisely would be increasingly important. The identify its customers for two reasons: company’s most recent investments are some local and state regulations mandate extensions of what it wanted to do 25 restrictions on alcohol distribution, years ago but was limited by availability and marketing brands to individuals of data and tools. Since 1998, Gallo requires knowing customer preferences. IT has been working on advanced data warehouses, analytics tools, and “The majority of all wine is consumed visualization. Gallo was an early adopter within four hours and five miles of visualization tools and created IT of being purchased, so this makes subgroups within brand marketing to it critical that we know which leverage the information gathered. products need to be marketed and distributed by specific destination,” The success of these early efforts has says Kent Kushar, Gallo’s CIO. spurred Gallo to invest even more in analytics. “We went from step Gallo knows exactly how its products function growth to logarithmic growth move through distributors, but of analytics; we recently reinvested tracking beyond them is less clear. heavily in new appliances, a new Some distributors are state liquor system architecture, new ETL [extract, control boards, which supply the transform, and load] tools, and new wine products to retail outlets and ways our SQL calls were written; and other end customers. Some sales are we began to coalesce unstructured through military post exchanges, and data with our traditional structured in some cases there are restrictions and consumer data,” says Kushar. regulations because they are offshore. “Recognizing the power of these Gallo has a large compliance capabilities has resulted in our taking a department to help it manage the 10-year horizon approach to analytics,” regulatory environment in which Gallo he adds. “Our successes with analytics products are sold, but Gallo wants to date have changed the way we to learn more about the customers think about and use analytics.” who eventually buy and consume those products, and to learn from The result is that Gallo no longer relies them information to help create on a single instance database, but has new products that localize tastes. created several large purpose-specific databases. “We have also created Gallo sometimes cannot obtain point of new service level agreements for our sales data from retailers to complete the internal customers that give them match of what goes out to what is sold. faster access and more timely analytics Syndicated data, from sources such as and reporting,” Kushar says. Internal Information Resources, Inc. (IRI), serves customers for Gallo IT include supply as the matching link between distribution chain, sales, finance, distribution, and actual consumption. This results and the web presence design team. in the accumulation of more than 1GB of data each day as source information for compliance and marketing. Reshaping the workforce with the new analytics 11
  12. 12. already have the skills, experience, Analytics tools were once the province and mind-set to be data scientists. of experts. They weren’t intuitive, Others can be trained. The teaching of and they took a long time to learn. the discipline is an obvious new focus Those who were able to use them for the CIO. (See the article,”How tended to have deep backgrounds CIOs can build the foundation for a in mathematics, statistical analysis, data science culture” on page 58.) or some scientific discipline. Only companies with dedicated teams of Visualization tools have been especially specialists could make use of these useful for Ingram Micro, a technology tools. Over time, academia and the products distributor, which uses them business software community have to choose optimal warehouse locations collaborated to make analytics tools around the globe. Warehouse location is more user-friendly and more accessible a strategic decision, and Ingram Micro to people who aren’t steeped in the can run many what-if scenarios before it mathematical expressions needed to decides. One business result is shorter- query and get good answers from data. term warehouse leases that give Ingram Micro more flexibility as supply chain Products from QlikTech, Tableau requirements shift due to cost and time. Software, and others immerse users in fully graphical environments because “Ensuring we are at the efficient frontier most people gain understanding more for our distribution is essential in this quickly from visual displays of numbers fast-paced and tight-margin business,” rather than from tables. “We allowOver time, academia says Jonathan Chihorek, vice president users to get quickly to a graphical viewand the business of global supply chain systems at Ingram of the data,” says Tableau Software’s Micro. “Because of the complexity, Mackinlay. “To begin with, they’resoftware community size, and cost consequences of these using drag and drop for the fieldshave collaborated warehouse location decisions, we run in the various blended data sources extensive models of where best to they’re working with. The softwareto make analytics locate our distribution centers at least interprets the drag and drop as algebraictools more user- once a year, and often twice a year.” expressions, and that gets compiled into a query database. But users don’tfriendly and more Modeling has become easier thanks need to know all that. They just needaccessible to people to mixed integer, linear programming to know that they suddenly get to optimization tools that crunch large see their data in a visual form.”who aren’t steeped and diverse data sets encompassingin the mathematical many factors. “A major improvement Tableau Software itself is a prime came from the use of fast 64-bit example of how these tools areexpressions needed to processors and solid-state drives that changing the enterprise. “Insidequery and get good reduced scenario run times from Tableau we use Tableau everywhere, six to eight hours down to a fraction from the receptionist who’s keepinganswers from data. of that,” Chihorek says. “Another track of conference room utilization breakthrough for us has been improved to the salespeople who are monitoring visualization tools, such as spider and their pipelines,” Mackinlay says. bathtub diagrams that help our analysts choose the efficient frontier curve These tools are also enabling from a complex array of data sets that more finance, marketing, and otherwise look like lists of numbers.” operational executives to become data scientists, because they help them navigate the data thickets.12 PwC Technology Forecast 2012 Issue 1
  13. 13. Figure 3: Improving the signal-to-noise ratio in social media monitoringSocial media is a high-noise environment But there are ways to reduce the noise And focus on significant conversations work boots Illuminating and helpful dialogue leather heel heel boots color fashion color fashion construction safety style style rugged leather cool leather cool shoes toe shoes toe boots boots price safety price safety store value store value rugged rugged wear wear construction constructionAn initial set of relevant terms is used to cut With proper guidance, machines can do Visualization tools present “lexical maps” toback on the noise dramatically, a first step millions of correlations, clustering words by help the enterprise unearth instances oftoward uncovering useful conversations. context and meaning. useful customer dialog.Source: Nexalogy Environics and PwC, 2012More data sources of shoes and boots. The manufacturerThe huge quantities of data in the was mining conventional business datacloud and the availability of enormous for insights about brand status, butlow-cost processing power can help it had not conducted any significantenterprises analyze various business analysis of social media conversationsproblems—including efforts to about its products, according to Joséeunderstand customers better, especially Latendresse, who runs Latendressethrough social media. These external Groupe Conseil, which was advisingclouds augment data that business units the company on its repositioningalready have direct access to internally. effort. “We were neglecting the wealth of information that we couldIngram Micro uses large, diverse data find via social media,” she says.sets for warehouse location modeling,Chihorek says. Among them: size, To expand the analysis, Latendresseweight, and other physical attributes brought in technology and expertiseof products; geographic patterns of from Nexalogy Environics, a companyconsumers and anticipated demand that analyzes the interest graph impliedfor product categories; inbound and in online conversations—that is, theoutbound transportation hubs, lead connections between people, places, andtimes, and costs; warehouse lease and things. (See “Transforming collaborationoperating costs, including utilities; with social tools,” Technology Forecastand labor costs—to name a few. 2011, Issue 3, for more on interest graphs.) Nexalogy Environics studiedSocial media can also augment millions of correlations in the interestinternal data for enterprises willing to graph and selected fewer than 1,000learn how to use it. Some companies relevant conversations from 90,000 thatignore social media because so much mentioned the products. In the process,of the conversation seems trivial, Nexalogy Environics substantiallybut they miss opportunities. increased the “signal” and reduced the “noise” in the social media aboutConsider a North American apparel the manufacturer. (See Figure 3.)maker that was repositioning a brand Reshaping the workforce with the new analytics 13
  14. 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 Adding SMA techniques change with the addition of social media analysis. One apparel maker started with its conventional BI analysis cycle. Conventional BI techniques 1 1. Develop questions used by an apparel 2. Collect data company client ignored 5 2 3. Clean data social media and required lots of data cleansing. The 4. Analyze data results often lacked insight. 5. Present results 4 3 Then it added social media and targeted focus groups to the mix. The company’s revised approach 1. Develop questions 1 added several elements such as 2. Refine conventional BI social media analysis and 6 2 - Collect data expanded others, but kept the - Clean data focus group phase near the - Analyze data beginning of the cycle. The 3. Conduct focus groups company was able to mine new 5 3 (retailers and end users) insights from social media 4 4. Select conversations conversations about market segments that hadn’t occurred to 5. Analyze social media the company to target before. 6. Present results Then it tuned the process for maximum impact. The company’s current 1. Develop questions 1 approach places focus 2. Refine conventional BI groups near the end, where 7 2 - Collect data they can inform new - Clean data questions more directly. This - Analyze data approach also stresses how 6 3 3. Select conversations the results get presented to 4. Analyze social media executive leadership. 5 4 5. Present results 6. Tailor results to audience 7. Conduct focus groups New step added (retailers and end users) What Nexalogy Environics discovered generally. “The key step,” she says, suggested the next step for the brand “is to define the questions that you repositioning. “The company wasn’t want to have answered. You will marketing to people who were blogging definitely be surprised, because about its stuff,” says Claude Théoret, the system will reveal customer president of Nexalogy Environics. attitudes you didn’t anticipate.” The shoes and boots were designed for specific industrial purposes, but Following the social media analysis the blogging influencers noted their (SMA), Latendresse saw the retailer fashion appeal and their utility when and its user focus groups in a new riding off-road on all-terrain vehicles light. The analysis “had more complete and in other recreational settings. results than the focus groups did,” she “That’s a whole market segment says. “You could use the focus groups the company hadn’t discovered.” afterward to validate the information evident in the SMA.” The revised Latendresse used the analysis to intelligence development process help the company expand and now places focus groups closer to the refine its intelligence process more end of the cycle. (See Figure 4.)14 PwC Technology Forecast 2012 Issue 1
  15. 15. Figure 5: The benefits of big data analytics: A carrier exampleBy 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 The initial analysis debunks some Carrier’s in churn.* analyze all call records. myths and raises new questions prime hypothesis discussed with the carrier. disproved Dropped calls/poor service? Merged to family plan? 14 billion Preferred phone unavailable? Offer by competitor? call data records analyzed Financial trouble? Dropped dead? Incarcerated? Friend dropped recently! Pattern spotted: Those with a relationship to a dropped customer $ $ DON’T GO! (calls lasting longer than two minutes, We’ll miss you! more than twice in the previous $ $ month) are 500% more likely to drop. 6 Marketers begin 5 Data group deploys a call 4 Further analysis confirms that friends influence campaigns that target record monitoring system that other friends’ propensity to switch services. at-risk subscriber groups issues an alert that identifies with special offers. at-risk subscribers. * Churn: the proportion of contractual subscribers who leave during a given time periodSource: Metamarkets and PwC, 2012Third parties such as Nexalogy A telecom provider illustrates theEnvironics are among the first to point. The carrier was concernedtake advantage of cloud analytics. about big peaks in churn—customersEnterprises like the apparel maker may moving to another carrier—but hadn’thave good data collection methods methodically mined the whole range ofbut have overlooked opportunities to its call detail records to understand themine data in the cloud, especially social issue. Big data analysis methods mademedia. As cloud capabilities evolve, a large-scale, iterative analysis are learning to conduct more The carrier partnered with Dataspora, aiteration, to question more assumptions, consulting firm run by Driscoll before heand to discover what else they can founded Metamarkets. (See Figure 5.)2learn from data they already have. “We analyzed 14 billion call dataMore focus on key metrics records,” Driscoll recalls, “and built aOne way to start with new analytics is high-frequency call graph of customersto rally the workforce around a single who were calling each other. We foundcore metric, especially when that core that if two subscribers who were friendsmetric is informed by other metrics spoke more than once for more thangenerated with the help of effective two minutes in a given month and themodeling. The core metric and the first subscriber cancelled their contractmodel that helps everyone understand in October, then the second subscriberit can steep the culture in the language, became 500 percent more likely tomethods, and tools around the cancel their contract in November.”process of obtaining that goal. 2 For more best practices on methods to address churn, see Curing customer churn, PwC white paper, http:// publications/curing-customer-churn.jhtml, accessed April 5, 2012. Reshaping the workforce with the new analytics 15
  16. 16. Data mining on that scale required that policymakers are encouraging distributed computing across hundreds more third-party access to the usage of servers and repeated hypothesis data from the meters. “One of the big testing. The carrier assumed that policy pushes at the regulatory level dropped calls might be one reason is to create platforms where third why clusters of subscribers were parties can—assuming all privacy cancelling contracts, but the Dataspora guidelines are met—access this data analysis disproved that notion, to build business models they can finding no correlation between drive into the marketplace,” says dropped calls and cancellation. Zambrano. “Grid management and energy management will be supplied “There were a few steps we took. One by both the utilities and third parties.” was to get access to all the data and next do some engineering to build a social Zambrano emphasizes the importance graph and other features that might of customer participation to the energy be meaningful, but we also disproved efficiency push. The issue he raises is some other hypotheses,” Driscoll says. the extent to which blended operational Watching what people actually did and customer data can benefit the confirmed that circles of friends were larger ecosystem, by involving millions cancelling in waves, which led to the of residential and business customers. peaks in churn. Intense focus on the key “Through the power of information metric illustrated to the carrier and its and presentation, you can start to show workforce the power of new analytics. customers different ways that they can“Through the power become stewards of energy,” he says. of information and Better access to results The more pervasive the online As a highly regulated business, the presentation, you can environment, the more common the utility industry has many obstacles to start to show customers sharing of information becomes. overcome to get to the point where Whether an enterprise is a gaming smart grids begin to reach their different ways that they or an e-commerce company that potential, but the vision is clear: can become stewards can instrument its own digital environment, or a smart grid utility • Show customers a few key of energy.” that generates, slices, dices, and metrics and seasonal trends in shares energy consumption analytics an easy-to-understand form. —Saul Zambrano, PG&E for its customers and partners, better analytics are going direct to the • Provide a means of improving those customer as well as other stakeholders. metrics with a deeper dive into where And they’re being embedded where they’re spending the most on energy. users can more easily find them. • Allow them an opportunity to For example, energy utilities preparing benchmark their spending by for the smart grid are starting to providing comparison data. invite the help of customers by putting better data and more broadly This new kind of data sharing could be a shared operational and customer chance to stimulate an energy efficiency analytics at the center of a co-created competition that’s never existed between energy efficiency collaboration. homeowners and between business property owners. It is also an example of Saul Zambrano, senior director of how broadening access to new analytics customer energy solutions at Pacific can help create a culture of inquiry Gas & Electric (PG&E), an early throughout the extended enterprise. installer of smart meters, points out16 PwC Technology Forecast 2012 Issue 1
  17. 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 what the data reveal (for underlying Winery information architecture is for trends of specific brands by location), production and quality control, not just or to conduct R&D in a test market, customer analytics. More recently, Gallo or to listen to the web platforms. has adopted complex event processing methods on the source information, These insights inform a specific design so it can look at successes and failures for “smart shelving,” which is the early in its manufacturing execution placement of products by geography system, sales order management, and location within the store. Gallo and the accounting system that offers a virtual wine shelf design front ends the general ledger. schematic to retailers, which helps the retailer design the exact details Information and information flow are of how wine will be displayed—by the lifeblood of Gallo, but it is clearly brand, by type, and by price. Gallo’s a team effort to make the best use wine shelf design schematic will help of the information. In this team: the retailer optimize sales, not just for Gallo brands but for all wine offerings. • Supply chain looks at the flows. Before Gallo’s wine shelf design • ales determines what information is S schematic, wine sales were not a major needed to match supply and demand. source of retail profits for grocery stores, but now they are the first or second • &D undertakes the heavy-duty R highest profit generators in those stores. customer data integration, and it “Because of information models such as designs pilots for brand consumption. the wine shelf design schematic, Gallo has been the wine category captain for • T provides the data and consulting I some grocery stores for 11 years in a row on how to use the information. 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. 18. Conclusion: A broader have found. The return on investment culture of inquiry for finding a new market segment can This article has explored how be the difference between long-term enterprises are embracing the big data, viability and stagnation or worse. tools, and science of new analytics along a path that can lead them to a Tackling the new kinds of data being broader culture of inquiry, in which generated is not the only analytics task improved visualization and user ahead. Like the technology distributor, interfaces make it possible to spread ad enterprises in all industries have hoc analytics capabilities to every user concerns about scaling the analytics role. This culture of inquiry appears for data they’re accustomed to having likely to become the age of the data and now have more. Publishers can scientists—workers who combine serve readers better and optimize ad a creative ability to generate useful sales revenue by tuning their engines hypotheses with the savvy to simulate for timing, pricing, and pinpointing and model a business as it’s changing. ad campaigns. Telecom carriers can mine all customer data more effectively It’s logical that utilities are to be able to reduce the expense instrumenting their environments as of churn and improve margins. a step toward smart grids. The data they’re generating can be overwhelming, What all of these examples suggest is a but that data will also enable the greater need to immerse the extended analytics needed to reduce energy workforce—employees, partners, and consumption to meet efficiency and customers—in the data and analytical environmental goals. It’s also logical methods they need. Without a view that enterprises are starting to hunt into everyday customer behavior, for more effective ways to filter social there’s no leverage for employees to media conversations, as apparel makers 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. 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 Set the tone for the rest of the organization with opinion or proposed action and using interactive examples visualization tools themselves Data availability Cloud architecture (whether private or public) and Find good ideas from any source semantically rich data integration methods Analytics tools Higher-profile data scientists embedded in the Identify hidden opportunities business units Interactive visualization Visual user interfaces and the right tool for the right Encourage a culture of inquiry person 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 Prove that the culture of inquiry is real to publish and discuss inquiries and resultsmarkets shift and there are no insights would be to designate, train, andinto improving customer satisfaction. compensate the more enthusiastic usersComputing speed, storage, and scale in all units—finance, product groups,make those insights possible, and it is supply chain, human resources, andup to management to take advantage so forth—as data scientists. Table 1of what is becoming a co-creative presents examples of approaches towork environment in all industries— fostering a culture of create a culture of inquiry. The arc of all the trends exploredOf course, managing culture change is in this article is leading enterprisesa much bigger challenge than simply toward establishing these culturesrolling out more powerful analytics of inquiry, in which decisions can besoftware. It is best to have several informed by an analytical precisionstarting points and to continue to find comparable to scientific insight. Newways to emphasize the value of analytics market opportunities, an energizedin new scenarios. One way to raise workforce with a stake in helping toawareness about the power of new achieve a better understanding ofanalytics comes from articulating the customer needs, and reduced risk areresults in a visual form that everyone just some of the benefits of a culture ofcan understand. Another is to enable inquiry. Enterprises that understandthe broader workforce to work with the trends described here and capitalizethe data themselves and to ask them to on them will be able to improve howdevelop and share the results of their they attract and retain customers.own analyses. Still another approach Reshaping the workforce with the new analytics 19
  20. 20. PwC: What’s your background,The nature of cloud- and how did you end up running a data science startup? MD: I came to Silicon Valley afterbased data science studying computer science and biology for five years, and trying to reverse engineer the genome network forMike Driscoll of Metamarkets talks about uranium-breathing bacteria. That was my thesis work in grad school.the analytics challenges and opportunities There was lots of modeling and causalthat businesses moving to the cloud face. inference. If you were to knock this gene out, could you increase the uptake of the reduction of uranium from a soluble toInterview conducted by Alan Morrison and Bo Parker 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? Mike Driscoll MD: Yes. The Department of Mike Driscoll is CEO of Metamarkets, Energy funded the research work a cloud-based analytics company he I did. Then I came out here and I co-founded in San Francisco in 2010. 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 better20 PwC Technology Forecast 2012 Issue 1
  21. 21. Some companies don’t have all the capabilities Critical businessthey need to create data science value. questionsCompanies need these three capabilitiesto excel in creating data science value. Value and change Good Data data sciencevisibility into their operations. They expensive relational database. There PwC: How are companies that dorealize the problem with being walking needs to be different temperatures have data science groups meetingamnesiacs, knowing what happened of data, and companies need to the challenge? Take the exampleto their customers in the last 30 days put different values on the data— of an orphan drug that is provenand then forgetting every 30 days. whether it’s hot or cold, whether it’s to be safe but isn’t particularly active. Most companies have only one effective for the application itMost businesses are just now temperature: they either keep it hot in was designed for. Data scientistsfiguring out that they have this a database, or they don’t keep it at all. won’t know enough about a broadwealth of information about their range of potential biologicalcustomers and how their customers PwC: So they could just systems for which that drug mightinteract with their products. keep it in the cloud? be applicable, but the people MD: Absolutely. We’re starting to who do have that knowledgePwC: On its own, the new see the emergence of cloud-based don’t know the first thing aboutavailability of data creates databases where you say, “I don’t data science. How do you bringdemand for analytics. need to maintain my own database those two groups together?MD: Yes. The absolute number-one on the premises. I can just rent some MD: My data science Venn diagramthing driving the current focus in boxes in the cloud and they can helps illustrate how you bring thoseanalytics is the increase in data. What’s persist our customer data that way.” groups together. The diagram has threedifferent now from what happened 30 circles. [See above.] The first circle isyears ago is that analytics is the province Metamarkets is trying to deliver data science. Data scientists are goodof people who have data to crunch. DaaS—data science as a service. If a at this. They can take data strings, company doesn’t have analytics as a perform processing, and transformWhat’s causing the data growth? I’ve core competency, it can use a service them into data structures. They havecalled it the attack of the exponentials— like ours instead. There’s no reason for great modeling skills, so they can usethe exponential decline in the cost of companies to be doing a lot of tasks something like R or SAS and start tocompute, storage, and bandwidth, that they are doing in-house. You need build a hypothesis that, for example,and the exponential increase in the to pick and choose your battles. if a metric is three standard deviationsnumber of nodes on the Internet. above or below the specific thresholdSuddenly the economics of computing We will see a lot of IT functions then someone may be more likely toover data has shifted so that almost all being delivered as cloud-based cancel their membership. And datathe data that businesses generate is services. And now inside of those scientists are great at visualization.worth keeping around for its analysis. cloud-based services, you often will find an open source stack. But companies that have the tools andPwC: And yet, companies are expertise may not be focused on astill throwing data away. Here at Metamarkets, we’ve drawn critical business question. A companyMD: So many businesses keep only heavily on open source. We have is trying to build what it calls the60 days’ worth of data. The storage Hadoop on the bottom of our stack, technology genome. If you give themcost is so minimal! Why would you and then at the next layer we have our a list of parts in the iPhone, they canthrow it away? This is the shift at the own in-memory distributed database. look and see how all those differentbig data layer; when these companies We’re running on Amazon Web Services parts are related to other parts instore data, they store it in a very and have hundreds of nodes there. camcorders and laptops. They built this amazingly intricate graph of the Reshaping the workforce with the new analytics 21
  22. 22. “[Companies] realize the problem with being walking amnesiacs, knowing what happened to their customers in the last 30 days and then forgetting every 30 days.”actual makeup. They’ve collected large shopping carts?” Well, the company PwC: In many cases, the dataamounts of data. They have PhDs from has 600 million shopping cart flows is going to be fresh enough,Caltech; they have Rhodes scholars; that it has collected in the last six because the nature of the businessthey have really brilliant people. years. So the company says, “All right, doesn’t change that fast.But they don’t have any real critical data science group, build a sequential MD: Real time actually means twobusiness questions, like “How is this model that shows what we need to things. The first thing has to do withgoing to make me more money?” do to intervene with people who have the freshness of data. The second abandoned their shopping carts and has to do with the query speed.The second circle in the diagram is get them to complete the purchase.”critical business questions. Some By query speed, I mean that if you havecompanies have only the critical business PwC: The questioning nature of a question, how long it takes to answerquestions, and many enterprises fall business—the culture of inquiry— a question such as, “What were your topin this category. For instance, the CEO seems important here. Some products in Malaysia around Ramadan?”says, “We just released a new product who lack the critical businessand no one is buying it. Why?” questions don’t ask enough PwC: There’s a third one also, questions to begin with. which is the speed to knowledge.The third circle is good data. A beverage MD: It’s interesting—a lot of businesses The data could be staring youcompany or a retailer has lots of POS have this focus on real-time data, in the face, and you could have[point of sale] data, but it may not have and yet it’s not helping them get incredibly insightful things inthe tools or expertise to dig in and figure answers to critical business questions. the data, but you’re sitting thereout fast enough where a drink was Some companies have invested a with your eyes saying, “I don’tselling and what demographics it was lot in getting real-time monitoring know what the message is here.”selling to, so that the company can react. of their systems, and it’s expensive. MD: That’s right. This is about how fast It’s harder to do and more fragile. can you pull the data and how fast canOn the other hand, sometimes some you actually develop an insight from it.web companies or small companies A friend of mine worked on the datahave critical business questions and team at a web company. That company For learning about things quicklythey have the tools and expertise. developed, with a real effort, a real-time enough after they happen, query speedBut because they have no customers, log monitoring framework where they is really important. This becomesthey don’t have any data. can see how many people are logging a challenge at scale. One of the in every second with 15-second latency problems in the big data space is thatPwC: Without the data, they across the ecosystem. It was hard to keep databases used to be fast. You usedneed to do a simulation. up and it was fragile. It broke down and to be able to ask a question of yourMD: Right. The intersection in the Venn they kept bringing it up, and then they inventory and you’d get an answerdiagram is where value is created. When realized that they take very few business in seconds. SQL was quick when theyou think of an e-commerce company actions in real time. So why devote scale wasn’t large; you could have anthat says, “How do we upsell people all this effort to a real-time system? interactive dialogue with your data.and reduce the number of abandoned22 PwC Technology Forecast 2012 Issue 1