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Starting analysis how to define Product Requirements that combines Cognitive Technology for Healthcare with Analytics
FALL 2016 ISSUE Thomas H. Davenport Stephan Kudyba Designing and Developing Analytics-Based Data Products An increasing number of companies are creating products that combine data with analytical capabilities. Creating an effective development process for these data products requires following well-established steps — and adding a few new ones, too. Vol. 58, No. 1 Reprint #58121 http://mitsmr.com/2cWDPtt THANKS TO SEVERAL WAVES of innovation in recent decades, the rise of information and technology is one of the dominant features of the current economy. The expansion of the information economy that began in the mid-1990s included enhanced hardware and software capabilities, abun- dant broadband Internet access, and increasingly widespread use of the Internet. These developments helped spur the creation of new products and industries and drove a significant increase in data re- sources. In an important 1996 article published in this journal, “The Design and Development of Information Products,” authors Marc H. Meyer and Michael H. Zack previewed the impact of these changes.1 (See “Revisiting ‘The Design and Development of Infor- mation Products,’” p. 84.) The past 20 years have brought several reconfigurations of the information and knowledge economy, as enhancements in computer processing and storage capabil- ities, new software and communication technologies, and the evolution of wire- less broadband and mobile computing have taken hold. Technological break- throughs have driven exponential growth in e-commerce and the emergence of a digital economy with vast data assets. The changes have been accompanied by ongo- ing attempts to make sense of all the data through the use of analytics. The original goal of computerized in- formation was to facilitate internal business Designing and Developing Analytics-Based Data Products DATA & ANALYTICS An increasing number of companies are creating products that combine data with analytical capabilities. Creating an effective development process for these data products requires following well-established steps — and adding a few new ones, too. BY THOMAS H. DAVENPORT AND STEPHAN KUDYBA PLEASE NOTE THAT GRAY AREAS REFLECT ARTWORK THAT HAS BEEN INTENTIONALLY REMOVED. THE SUBSTANTIVE CONTENT OF THE ARTICLE APPEARS AS ORIGINALLY PUBLISHED. FALL 2016 MIT SLOAN MANAGEMENT REVIEW 83 THE LEADING QUESTION How does the development of data prod- ucts differ from that of other informa- tion products? FINDINGS Data product devel- opment activities rarely take place in a traditional product development sequence. Product conceptual- ization should precede data acquisition. Developing data products requires the involvement of stakeholders such as subject matter experts. 84 MIT SLOAN MANAGEMENT REVIEW FALL 2016 SLOANREVIEW.MIT.EDU DATA & ANALYTICS transactions and improve decisions. However, the re- sulting digital assets are now of considerable value in themselves.2 According to a 2015 Organisation for Economic Co-operation and Development research report, “Data-driven innovation forms a key pillar in 21st-century sources of growth. . . . large data sets are becoming a core asset in the economy, fostering new industries, processes, and products and creating sig- nificant competitive advantages.”3 When digital assets are made available to cus- tomers, they are increasingly accompanied by analytics that provide insights and facilitate deci- sions. Analytics — which were not typically present in earlier information offerings — add substantial value to intangible assets by making them easier to understand and apply. In a world in which information alone has become ubiquitous and somewhat commoditized, analytics provide a means of making information more useful and valuable. In this article, we will focus on the combination of new analytical capabilities and burgeoning data assets that together form value-added information product offerings. In common parlance, these of- ferings are often called “data products.”4 For our research, we interviewed more than 40 companies that offer data products and specifically addressed their process (or the lack thereof) for developing them. (See “About the Research.”) In our work on this project, we seek to augment and update Meyer and Zack’s findings from 1996. The Value of Analytics For Data Products Data and analytics were historically employed for one purpose: improvement of internal decision making. Indeed, one of the earliest names for these fields was “decision support.” The goal was to im- prove the accuracy and efficacy of decisions in marketing, finance, human resources, and so forth. But the big data revolution offered companies another use for data and analytics. Beginning with online companies such as Google Inc. and Face- book Inc., companies began to develop “data products” for customers based on data and analyt- ics. Search services from Yahoo! Inc., Google, and others were arguably the first of these, but other sorts of products followed. Linkedin Corp., for ex- ample, developed “People You May Know,” and soon added others, including “Jobs You May Be In- terested In” and “Groups You May Like.”5 Online companies such as Facebook have done similarly. (Facebook has its own “People You May Know” product.) Many online data products are available as mobile apps, shaping what has been called an “app economy.”6 Not every app involves analytics, but those that do qualify as data products. Data products (most of which can be described as services) are not generally sold separately to customers but are used to attract customers for ad- vertising, draw attention to unknown products in large product pools, and enhance revenue through cross-selling and upselling. They have powered rapid REVISITING “THE DESIGN AND DEVELOPMENT OF INFORMATION PRODUCTS” In a 1996 article in this journal titled “The Design and Development of Information Products,” authors Marc H. Meyer and Michael H. Zack examined the design and im- plementation of information products. Despite the economic importance of the information products industry and the rapid pace of innovation at that time, previous research examining the design and development of infor- mation products, which Meyer and Zack defined broadly to include electronic or printed products based on data, information, and knowledge, did not exist. Meyer and Zack’s research was guided by several basic questions: What could companies in informa- tion products industries learn from research on physical products? How were information products designed and manufactured, and how can in- formation technology be used to support these processes? And more fundamentally, what was the archi- tecture of an information product, and what were the strategic, organi- zational, and technical implications of the architecture for companies operating in this arena? To answer these questions, Meyer and Zack built on research and knowledge about the design of physical prod- ucts. They developed a five-step process for developing information products — acquisition, refinement, storage/retrieval, distribution, and presentation — and showed how companies could create competitive advantage with an information prod- uct platform and generate value by refining information. Obviously, there have been tremendous changes in the infor- mation and technology areas over the past two decades — more powerful hardware and software, an explosion of bandwidth, a proliferation of mobile devices, and increasing access to the Inter- net. But it’s instructive to recall where this discussion began and how it has evolved. Today, we argue that it’s useful to think in terms of “analytics-based data products” as opposed to “infor- mation products.” Given the advances in digital technology and the increases in usage, we pro- pose augmenting the Meyer and Zack framework with two addi- tional steps — one focused at the beginning on creating the concep- tual product and the other focused late in the cycle on collecting mar- ket feedback. SLOANREVIEW.MIT.EDU FALL 2016 MIT SLOAN MANAGEMENT REVIEW 85 growth in the value and success of these online com- panies — not only in creating a much larger user base but also in differentiating offerings. Analytics used in data products take a variety of forms. The most common is descriptive analytics that provide insight on a customer’s level of activity or product usage.7 Google pioneered this format with its Google Analytics offering, a free set of data products that informs customers about visits to their websites. Descriptive analytics can also be compara- tive, for example, relating a household’s utility usage and expenditure to other household’s, or comparing a company’s travel activity with that of its peers. Predictive analytics are more difficult to gener- ate. One of the most common forms today is predictive maintenance for industrial machines. Using data gathered from sensors in machines, analytics compute the point when comparable machines have broken down and recommend par- ticular services before that time. Large companies such as GE, Siemens, and NCR have recently intro- duced predictive maintenance offerings. In the consumer sector, Seattle, Washington-based Zillow Group Inc.’s Zestimate, based on publicly accessible housing data, predicts the price a homeowner might receive for the sale of his or her house. Prescriptive analytics recommend specific ac- tions. In the agricultural products industry, companies such as Monsanto and DuPont offer data products that recommend, for example, when and what farm- ers should plant, when certain interventions, such as water or pesticide applications, are advisable, or when to harvest.8 These data products can help farmers achieve higher crop yields. Other forms of prescriptive analytics in data products involve matching algorithms, which match customers with products, dating candidates, or potential business network members. Current Processes and Issues For Information Products Relatively little is written or known about develop- ing new generations of data products. Indeed, we have noticed that the companies interested in pro- ducing new offerings often lack both structure and process. In their 1996 article, Meyer and Zack offered a clear structure for information product development. Although they were primarily addressing traditional information services com- panies, we believe that many of their ideas are quite relevant to the issue of designing data products from big data, including the following: • Product and process “platforms” can support a variety of specific information products. That idea has become much more popular in recent years, with many economists and strategists extol- ling the virtues of “multisided” platforms that allow interaction with multiple constituencies at once.9 • Different information products can share an overall product architecture or be part of the same product family. In Meyer and Zack’s view, it’s a mistake for organizations to think in terms of single products. • An idea “refinery” can create value from infor- mation. Although Meyer and Zack presented this concept 20 years ago, few organizations have begun to extract the kind of value from their in- formation that the authors envisioned.10 In this article, we will describe what leading companies are doing to create, refine, and generate value from data products. Given the emphasis that has been placed on the potential value that data assets offer as the digital economy progresses, a re- vised model for providing structure to the product creation process is in order. Meyer and Zack ad- dressed the topic in the form of an information product development process, where raw data sources or repositories provide inputs to the pro- cess of producing a product. They created a five-step methodology (acquisition, refinement, ABOUT THE RESEARCH This research involved two primary efforts. First was a set of 35 interviews with data scientists, many of whom were involved in data product creation, in 2012. The data scientists interviewed primarily worked for startup companies in online businesses, although some data scientists from larger companies (LinkedIn, Facebook, and eBay) were also included. In September 2014, we held a day-long meeting of large, established companies that were exploring data and analytics- based products and services. The participants in this session included executives from State Street Corp., GE, Monsanto, the World Bank, Thomson Reuters, Boston Financial Data Services, EMC, Intuit, MassMutual, Eli Lilly, Caterpillar, and others. We conducted interviews at many of the companies sep- arately from the meeting, and we also learned about them during the session. We conducted several additional interviews specifically for this article. In total, we interviewed managers at more than 40 companies that had some data prod- uct development activities underway. We discussed the types of products the companies were attempting to develop, the processes they employed for devel- opment, the technologies and platforms they used to develop and deliver the products, and the success of the products they had in the marketplace thus far. 86 MIT SLOAN MANAGEMENT REVIEW FALL 2016 SLOANREVIEW.MIT.EDU DATA & ANALYTICS storage/retrieval, distribution, and presentation) to turn data inputs into information products.11 While we think that model continues to be valid, the evolution of various technologies and corre- sponding data sources in today’s digital economy suggests a need for adjustments and augmentations to accommodate increased volumes, velocities, and varieties of data, as well as the corresponding stor- age and accessing activities required to manage the assets in the product creation process. An Updated Information Product Model Data product development activities today are rarely undertaken in a traditional product development se- quence that involves identifying the need, developing the product, and then taking it to market. The current pace of business — particularly online business — is too fast for that. Rather, product development activi- ties often take place in a continuous, iterative fashion, with the important activities conducted in parallel.12 The popular “lean startup” model specifies the devel- opment of a “minimum viable product,” with periodic refinements over time.13 To the degree that data prod- uct developers are comfortable with any approach to product development, it is that one. An updated model needs to reflect new “time to market” expectations in the product development process. In light of these new realities, we have added additional steps to Meyer and Zack’s original five-step model. At the front end, we add a step that involves conceptualizing the product; at the back end, we add the establishment of a market feedback mechanism. Additionally, we point to the ongoing need for managers to collect input from a variety of stakeholders. STEP 1: Conceptualizing the Product Before jumping in, organizations must identify an infor- mation product that meets a need from the marketplace. This introductory step needs to take place before data acquisition. It requires conceptu- alizing the information product, along with identifying the required data resources. The pro- cess involves product definition, data investigation (which should include sourcing data creatively14), and establishing the framework necessary to pro- duce a prototype. Once this set of requirements is met, the remaining steps of the development process can be carried out more efficiently. For ex- ample, once managers know the data elements that will go into the product, storage and retrieval can be streamlined. An interesting example is CarMD.com Corp., an Irvine, California-based company that provides services that leverage automotive diagnostic infor- mation. The original idea was to provide diagnostic capabilities that led consumers to auto repair esti- mates and potential service providers.15 One of the company’s products compares the data extracted from onboard computers in cars against online auto repair databases and offers consumers infor- mation on auto maintenance. STEP 2: Data Acquisition Once the conceptual model has been worked out, data acquisition can be pursued in a more efficient manner. Organizations tend to acquire or accumulate data that corre- sponds to their functional activities. However, given the vast amounts of data being generated by information devices and the data available from public sources, the acquisition process needs to connect the requirements of the conceptual model to data that will create the product. In addition to acquiring structured data (for example, customer purchase records), companies should also consider using unstructured sources (for instance, social media comments) that might be able to add value. Companies should be prepared to look within and outside their own systems for such data.16 Data product development activities today are rarely undertaken in a traditional product development sequence that involves identifying the need, developing the product, and then taking it to market. SLOANREVIEW.MIT.EDU FALL 2016 MIT SLOAN MANAGEMENT REVIEW 87 STEP 3: Refinement Although Meyer and Zack’s data refinement process remains quite relevant, it has to be augmented to facilitate new data sources and to take advantage of advanced analytic methods. The original model talked about the importance of being able to “glean further meaning from combina- tions of individual [data] elements.”17 Today, much data refining is achieved with automated tools. Real- time machine learning and algorithmic processing of data elements can categorize, correlate, person- alize, profile, and search data quickly to create meaningful models that have significant value for consumers.18 For example, Passur Aerospace Inc., based in Stamford, Connecticut, uses both its own data and public data to develop scheduling information for airlines and travelers. Drawing on publicly available data on weather, flight schedules, and other factors, along with its own internal data based on radar statistic feeds, it generates flight arrival estimates. Ap- plying advanced analytics, Passur’s arrival estimates outperform ones based on traditional techniques.19 STEP 4: Storage and Retrieval Storage and re- trieval are as important as ever. However, retrieval in today’s environment must incorporate advance- ments in query and search processing capabilities (for instance, making use of algorithms) that can access more granular levels of data. Traditional storage techniques need to be augmented by new technologies such as map reduction (a software framework for distributed processing of large data sets on computer clusters of commodity hardware) and parallel processing capabilities to manage larger and faster-moving data sources. Many orga- nizations store data in relatively unstructured formats when they initially capture it, refining it over time. Data storage, retrieval, and processing are increasingly taking place in the cloud rather than on a company’s premises. This not only pro- vides companies with flexibility in their technology infrastructures but also can make it easier for them to combine internal and external data. STEP 5: Distribution The distribution options for information products have shifted dramatically from the earlier menu of possibilities, some of which (such as fax and CD-ROM) have been super- seded by the Web. Timing and frequency remain critical aspects of distribution; data products must be continuously available and updated in near-real time. In the digital economy, online media (such as websites and portals) fully address the required level of continuous accessibility to information products. However, Web access via traditional computers is quickly being overtaken by mobile ac- cess via smartphones, tablets, and apps. As a result, providers of information products that are distrib- uted via mobile devices need to revamp their content formats and design. At the same time, distributing data products through the cloud adds a new dimension to the question of how frequently information needs to be updated for users. Consider, for example, a business- to-business case involving a shipping service provider that offers information products including en-route metrics, such as estimates of time to deliv- ery. Assuming the data is available, the frequency and timeliness of such information — generated through GPS traffic information, location data, and analytics — can be close to real time. STEP 6: Presentation In the original Meyer-Zack model, information products gained value from the context of their use. The user interface mattered — and the easier products were to use, the more valuable they were. Although the digital economy places heavier emphasis on analytics than on simple data provision, there are some important constants. While standard reporting (that is, simple informa- tion products) continues to meet the needs of many consumers, more advanced analytics-based products Real-time machine learning and algorithmic processing of data elements can categorize, correlate, personalize, profile, and search data quickly to create meaningful models that have significant value for consumers. 88 MIT SLOAN MANAGEMENT REVIEW FALL 2016 SLOANREVIEW.MIT.EDU DATA & ANALYTICS such as forecasts, predictions, and probabilities (such as real-time calculations generated through machine learning) can lead to differentiation and competitive advantage. STEP 7: Market Feedback The competitive nature of the information product space, availability of new data sources, and demand for timely decision sup- port require an ongoing emphasis on innovation and on monitoring product usage. Adding this step at this stage of the analytics-based data product de- velopment process is consistent with the iterative nature of product development in a “lean startup” context. Once again, the evolution of new technolo- gies has provided a mechanism for facilitating a feedback and information extraction process from the marketplace. New forms of market research are capable of leveraging social media platforms (for ex- ample, business Facebook pages) to listen to the marketplace. Interactive blogs and flash surveys can be utilized to assess customer perceptions of existing information products.20 New features of online in- formation products can be tested in a matter of hours with A/B or multivariate online testing ap- proaches. Both user correspondence and digital metrics on product use (for instance, views, clicks, downloads, and bounces) can be analyzed to en- hance products continuously. A Structured Approach to Stakeholder Involvement In order to achieve effective results from the imple- mentation of the product development model, stakeholder involvement is essential.21 Having par- ticular types of input at different stages of the product development process is important. There- fore, companies need to develop some degree of structure for stakeholder input. During the stage when the product is being con- ceptualized, it’s important to have involvement from three specific groups: subject matter experts at the business level (who can help determine the feasibility of the product design); managers of ex- isting and complementary information products (who can help companies avoid cannibalization and duplication); and marketing people (who can help assess the nature and scale of consumer de- mand). These individuals can assist in providing the framework for designing or upgrading existing products to add value to meet market needs. For the data acquisition and the storage and re- finement stages, stakeholder involvement should expand to include legal representatives, who can speak to data ownership, privacy, and use issues; IT personnel, who can provide input on hardware and software requirements for data products and also help in developing and improving the functionality of the product; and data managers and analytics and data scientists to assist in product platform exe- cution. It is critical to involve analytics and data science professionals to help in structuring and ana- lyzing data. For the distribution and presentation stages, the stakeholders should again include marketing peo- ple (who can help sort out consumer/user needs for the initial product launch and subsequent product releases) and IT personnel (who can deal with hardware and software issues in product function- ality during the product rollout). During the market feedback stage, it’s impor- tant to involve people from both IT and marketing. IT personnel will be able to leverage available com- munication devices to interact with users on a variety of platforms (Web, mobile, social media, email, etc.). Marketing can devise strategies for in- teraction and feedback and will be able to gather and synthesize the feedback. If data product development is to be successful, human resources personnel also have a pivotal role to play. Although data scientists can be difficult to hire and retain, they are essential for developing analytics-based data products. The competitive nature of the information product space, availability of new data sources, and demand for timely decision support require an ongoing emphasis on innovation and on monitoring product usage. SLOANREVIEW.MIT.EDU FALL 2016 MIT SLOAN MANAGEMENT REVIEW 89 Structure Versus Market Responsiveness There has always been a trade-off in product devel- opment between having structures that ensure that the product addresses market needs and is of high quality, and being able to introduce products quickly and remain responsive to customer needs. Though the pendulum in data products has clearly swung in the direction of responsiveness, there is still a need for structure and method in developing new offerings. As the proliferation of information has led to com- moditization, it is often the accompanying analytics that make data products truly useful. Analytics can be difficult and time-consuming to develop, so it is vital to have a sense of which analytics are needed and valu- able before developing and introducing them. We believe that the steps outlined in the Meyer and Zack article — combined with the additional steps we out- line — can be helpful to organizations in assembling data and analytics products that provide value to con- sumers and businesses. Just as in the early e-commerce era almost every company decided it needed a website, we envision an era in which almost every company feels the need for a data product. It will thus become increasingly im- portant for organizations to have some discipline about which data products are developed and which functions they incorporate. We doubt that Meyer and Zack could have fore- seen the explosion of information and technology that has taken place since 1996. However, their arti- cle provided early insights and warnings about the importance of rigorous thinking when developing products based on intangible information assets. Thomas H. Davenport is the President’s Distin- guished Professor of Information Technology and Management at Babson College in Wellesley, Mas- sachusetts, and a fellow at the MIT Sloan Initiative on the Digital Economy. Stephan Kudyba is an asso- ciate professor of data analytics and management information systems at the New Jersey Institute of Technology’s Martin Tuchman School of Manage- ment in Newark, New Jersey. Comment on this article at http://sloanreview.mit.edu/x/58121, or contact the authors at firstname.lastname@example.org. REFERENCES 1. M.H. Meyer and M.H. Zack, “The Design and Develop- ment of Information Products,” Sloan Management Review 37, no. 3 (spring 1996): 43-59. 2. P. Rotella, “Is Data the New Oil?,” April 2, 2012, www.forbes.com. 3. Organisation for Economic Co-operation and Develop- ment, “Data-Driven Innovation: Big Data For Growth and Well-Being” (Paris: OECD Publishing, 2015). 4. J. Howard, M. Zwemer, and M. Loukides, “Designing Great Data Products” (Sebastopol, California: O’Reilly, 2012). 5. T.H. Davenport, “Analytics 3.0,” Harvard Business Review 91, no. 12 (December 2013): 64-72. 6. Organisation for Economic Co-operation and Develop- ment, “The App Economy,” OECD Digital Economy Papers, no. 230 (Paris: OECD Publishing, 2013). 7. The three types of analytics are described in detail in T.H. Davenport and J.G. Harris, “Competing On Analytics: The New Science of Winning” (Boston: Harvard Business Review Press, 2007). 8. J. Bunge, “Big Data Comes to the Farm, Sowing Mistrust,” Wall Street Journal, February 25, 2014. 9. A. Hagiu, “Strategic Decisions For Multisided Plat- forms,” MIT Sloan Management Review 55, no. 2 (winter 2014): 71-80. 10. Meyer and Zack, “Design and Development of Information Products.” 11. Ibid. 12. One CEO creating a data product in the health care industry told us, “We tried agile [referring to agile product development methods], but it was too slow.” 13. E. Ries, “The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses” (New York: Crown Business, 2011). 14. D. Barton and D. Court, “Making Advanced Analytics Work For You,” Harvard Business Review 90, no. 10 (October 2012): 78-83. 15. Bain & Company, “Using Data As a Hidden Asset,” August 16, 2010, www.bain.com. 16. E. Dwoskin, “Startup Factual Knows Your Commute, and Much More . . . ,” Wall Street Journal, Dec. 10, 2015. 17. Meyer and Zack, “Design and Development of Infor- mation Products,” 46. 18. D. Kiron, P.K. Prentice, and R.B. Ferguson, “The Analyt- ics Mandate,” May 12, 2014, http://sloanreview.mit.edu. 19. A. McAfee and E. Brynjolfsson, “Big Data: The Man- agement Revolution,” Harvard Business Review 90, no. 10 (October 2012): 60-68. 20. B. Zeidler,“6 Ways to Extract Customer Insights From Social Conversations,” February 2015, www.quirks.com; and T. Rajpathak and A. Narsingpurkar, “Managing Knowl- edge From Big Data Analytics in Product Development,” white paper, Tata Consultancy Services, Mumbai, India, 2013, www.tcs.com. 21. B.H. Wixom, “Cashing In On Your Data,” research briefing, MIT Sloan Center for Information Systems Research, Aug. 21, 2014, http://cisr.mit.edu. Reprint 58121. Copyright © Massachusetts Institute of Technology, 2016. All rights reserved. PDFs Reprints Permission to Copy Back Issues Articles published in MIT Sloan Management Review are copyrighted by the Massachusetts Institute of Technology unless otherwise specified at the end of an article. MIT Sloan Management Review articles, permissions, and back issues can be purchased on our Web site: sloanreview.mit.edu or you may order through our Business Service Center (9 a.m.-5 p.m. ET) at the phone numbers listed below. Paper reprints are available in quantities of 250 or more. To reproduce or transmit one or more MIT Sloan Management Review articles by electronic or mechanical means (including photocopying or archiving in any information storage or retrieval system) requires written permission. To request permission, use our Web site: sloanreview.mit.edu or E-mail: email@example.com Call (US and International):617-253-7170 Fax: 617-258-9739 Posting of full-text SMR articles on publicly accessible Internet sites is prohibited. To obtain permission to post articles on secure and/or password- protected intranet sites, e-mail your request to firstname.lastname@example.org. MIT MIT SL SLO OAN MANA AN MANAGEMEN GEMENT REVIEW T REVIEW DIGIT DIGITAL TRANSFORMA AL TRANSFORMATION TION Copyright © Massachusetts Institute of Technology, 2016. All rights reserved. Reprint #58121 http://mitsmr.com/2cWDPtt he authors at email@example.com. REFERENCES 1. M.H. Meyer and M.H. Zack, “The Design and Develop- ment of Information Products,” Sloan Management Review 37, no. 3 (spring 1996): 43-59. 2. P. Rotella, “Is Data the New Oil?,” April 2, 2012, www.forbes.com. 3. Organisation for Economic Co-operation and Develop- ment, “Data-Driven Innovation: Big Data For Growth and Well-Being” (Paris: OECD Publishing, 2015). 4. J. Howard, M. Zwemer, and M. Loukides, “Designing Great Data Products” (Sebastopol, California: O’Reilly, 2012). 5. T.H. Davenport, “Analytics 3.0,” Harvard Business Review 91, no. 12 (December 2013): 64-72. 6. Organisation for Economic Co-operation and Develop- ment, “The App Economy,” OECD Digital Economy Papers, no. 230 (Paris: OECD Publishing, 2013). 7. The three types of analytics are described in detail in T.H. Davenport and J.G. Harris, “Competing On Analytics: The New Science of Winning” (Boston: Harvard Business Review Press, 2007). 8. J. Bunge, “Big Data Comes to the Farm, Sowing Mistrust,” Wa