Big Data. can you seize the opportunity? featuring Donald A. Marchand and Joe Peppard. January 30, Sponsored by

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1 Big Data can you seize the opportunity? featuring Donald A. Marchand and Joe Peppard January 30, 2013 Sponsored by 2013 Harvard Business School Publishing. Created for Harvard Business Review by BullsEye Resources,

2 WEBINARS Big Data Can You Seize the Opportunity? January 30, 2013 OVERVIEW Projects related to big data typically fail to achieve their desired business objectives. This is often because organizations focus on the technology and data instead of how people will use this information. Usually organizations focus somewhat narrowly and seek to use exploit their existing data, using a clear design-to-build process, with known outcomes. But increasingly companies are taking a broader view, changing their mindset, and exploring data. This requires a more open-ended process (design-for-use) that focuses on discovery of new knowledge, which requires a careful framing of the questions to be answered and the data to be used. Research reveals a set of guidelines that can help companies get the greatest value from their big data. CONTEXT Professor Marchand and Peppard summarized the main ideas from their research and HBR article on big data, described trends related to big data they are seeing in organizations, explained why analytics projects are different from typical IT projects, and offered guidelines for successful analytics projects. contributors Donald A. Marchand Professor, Strategy Execution and Information Management, IMD, Switzerland Joe Peppard Professor, Information Systems, Cranfield University School of Management, United Kingdom Angelia Herrin (Moderator) Editor, Special Projects and Research, Harvard Business Review Key learnings Executive disappointment with the value from big data is driving several major trends. Historically, executives have paid little attention to the quantity or quality of data in their organization. But over the past 18 months, the label of Big Data has brought more C-level attention to how organizations are attempting to use their data to create value. This greater attention on an organization s data has resulted in executive disappointment about the value from their data and has sparked three major trends: 1. From big systems to big data. Over the past two decades, organizations have implemented systems, such as ERP and CRM, which produce massive volumes of data (along with extensive varieties of data and data at high velocity). But most organizations have not derived much value from their data, and the size of data has added to the problem. 2. From reengineering processes to decision making. Historically organizations have thought about their processes and made revisions to improve their processes. A new trend is for organizations to think about how they make decisions and how they use analytics to improve their decisions. 2

3 3. From managing and controlling data to leveraging information through people. Most companies have traditionally focused on controlling their data. What is changing is that organizations are increasingly looking to leverage their data, something that leading companies have long done. It is people who give meaning to data. Joe Peppard How organizations think about data affects the value they will derive from it. Professor Peppard described two very different views of data. Information is a corporate resource. The traditional perspective is that information is a corporate resource. It is viewed as an object that resides in databases, reports, and dashboards, where it is capable of being manipulated. Information is an outcome. It is an outcome of people constructing meaning out of data. In this view, information resides not in artifacts but in people s minds. Individuals actively create meaning from data. How an organization and its executives view data will affect the types of projects they pursue, the processes they use, and the skills and capabilities they seek to develop. The most effective IT projects are not about technology; they are about information and people. Research by Professors Marchand and Peppard looked at over 100 IT projects over a 15-year period. They found a high failure rate, as many projects did not achieve their expected business value. They also found that many executives and organizations believed that technology was a solution to a problem. A clear conclusion was that IT is not a solution. The business value of an IT project and particularly of business intelligence ( BI ) and analytics does not come by deploying tools and technology. Value comes from how technology is used by people. The presenters said the key message from their research was: Business intelligence does NOT reside in the data warehouse or with big data. Rather, BI emerges in the minds of employees when they identify and access data, combine data with their own or others knowledge of a business situation, and produce novel insights and/or resolutions to the issue at hand. Further, they said that for BI to have business value requires an understanding of decision making, knowledge discovery, and creation (insight) and (human and organizational) learning. When organizations don t make good decisions, the common belief is that if executives are provided with more and better information, they will make better decisions. But this may not be true. Maybe the problem is with the executive, not with the data. The issue may be with the executive s experience, analytical abilities, or biases, or with how the person uses the data. So, both successful and unsuccessful projects are more about people than about technology or data. Value does not come from IT. It is from the use of IT, technology, and information by people in the business. Donald A. Marchand 3

4 BI/analytics projects need to be managed differently from traditional IT projects. The purpose of IT and analytics projects can be thought of in terms of either exploitation or exploration. Exploitation. This type of project seeks to exploit an organization s data for competitive advantage. A typical exploitation project is process focused, aims to take out costs and inefficiencies, and looks to automate tasks, activities, and information flows. Exploration. These are projects focused on generating new knowledge or insights from information. They support decision making and can be used to build new businesses and entirely new business models. Traditional IT projects can be described as using a design-to-build process (D2B), while analytics projects are best served through a design-for-use process (D4U) (Figure 1). D2B. In a traditional IT project paradigm, the goal and outcome are known, as are the necessary organizational competencies. The project follows a clear implementation plan to deploy the technology. An analogy is that an organization knows where it is going, has a map to plot its route, and uses a compass to get to the desired outcome. D4U. This type of project works very differently. There is no clear destination, and therefore there isn t a map. Different skills and competencies are required. The project is about exploration, which starts with sensing of problems and opportunities, using analytics, and then developing insights. Figure 1. Contrasting Paradigms for Running IT Projects Typical Projects Typical Overarching Goals Project Structure Competencies Required What Does Success Look Like? D2B Install an ERP Automate a claims-handling process Improve efficiency Lower costs Increase productivity Define desired outcome Redesign work practices Specify technology needs Develop detailed plans to deploy IT, manage change, train users IT professionals with engineering, computer science, and math backgrounds People who know the business Projects comes in on time, on plan, and within budget Project achieves desired process change D4U Develop a new, shared understanding of customers needs and behaviors Predict future growth markets Change how employees think about and use information Challenge assumptions and biases that employees bring to decision making Use new insights to serve customers better Develop theories Build hypothesis Identify relevant data Conduct experiments Refine hypotheses in response to findings Repeat the process In some cases, IT professionals with engineering, computer science, and math backgrounds People who know the business Data scientists Cognitive and behavioral scientists Employees base decisions on data, evidence Employees use data to generate new insights and contexts 4

5 Discovering new knowledge is a function of the questions asked and the data used. Just as different types of initiatives use different types of processes, so too does discovery of new knowledge. The types of questions asked can be clearly defined or undefined, and the data used to answer these questions can be known or unknown. When the question is defined and the data is known, it is an exploitive undertaking with a clear objective and a known process. An example is a telco that wants to understand its customer churn. This is very different from Google, which is collecting any data it can, even though it may not know what the data is and what questions it might seek to answer from this unknown data. This would be using data for exploration. Keys to discovering new knowledge include the framing of the question and getting the right data to answer the question (Figure 2). Figure 2 Discovering New Knowledge. Research has identified a set of guidelines in D4U projects. These guidelines are: Place people at the heart of the initiative. People are the key success factor; not the technology or the data. Emphasize information use as the way to unlock value from IT. Probe deeply into how data could be used by asking second-order questions. Discover what data you have, don t have, and need. Give project teams the freedom to reframe the business problem. Equip project teams with behavior and cognitive scientists. Effective project teams don t just have data scientists, which can limit how an organization thinks about and uses its data. Project teams need experts to interpret data that has insights into thinking and behavior. Focus on learning. Teams that explore work in an environment that facilitates a culture of information sharing. They strive to demonstrate cause and effect and constantly work to identify appropriate tools and techniques. Worry more about solving business problems than deploying technology. There will be continuous waves of technology. But more important than picking which technologies to implement is deciding which business problems on which to focus. 5

6 BIOGRAPHIES Donald A. Marchand Professor, Strategy Execution and Information Management, IMD Switzerland Dr. Donald A. Marchand is professor of strategy execution and information management at IMD in Lausanne, Switzerland. His research and teaching interests include how organizations and leaders use information and knowledge to collaborate and compete, how companies develop business models to leverage standardization and flexibility in processes and information systems locally, regionally and globally, and how leaders create organizations where people can fully share and act on what they know and discover what they do not know to learn and change closer to real time. From 1997 to 2000 he was the director of the research project Navigating Business Success that discovered the Information Orientation (IO) metric and how this new metric can be used to boost bottom-line performance through effective information, people, and IT capabilities. His current research focuses on leading great human achievements. Professor Marchand has authored several books, over 30 book chapters, and 100 articles for journals and business magazines. He co-founded a Swiss-based company, enterpriseiq, (www.enterpriseiq. com). He received his BA from the University of California, Berkeley, and his MA and PhD from the University of California, Los Angeles. Joe Peppard Professor, Information Systems, Cranfield University School of Management Joe Peppard is director of Cranfield University School of Management s IT Leadership Program. He is also adjunct professor at the University of South Australia. He has held academic appointments at Loughborough University, Trinity College Dublin, Groningen University, and the University of Sydney. The focus of Joe s research and teaching is information systems and technology. His research focuses on driving the organization forward in the use and exploitation of IT including the leadership role of the CIO and thinking strategically about information, systems, and technology to increase business opportunities and generate value from IT investments. His consulting is focused on advising organizations on IT and strategy related matters and how to unlock business value from their IT investments. He also works with a number of technology companies. Joe has published widely in a number of journals (some papers are available for download at In 2009 he was awarded the Stafford Beer Medal by the OR Society for his research. His most recent books include Strategic Planning for Information Systems and Customer Relationship Management: Perspectives from the Marketplace. He is an editorial board member of European Management Journal and the Journal of Information Technology. Angelia Herrin (Moderator) Editor for Research and Special Projects, Harvard Business Review Angelia Herrin is Editor for Research and Special Projects at Harvard Business Review. At Harvard Business Review, Herrin oversaw the re-launch of the management newsletter line and established the conference and virtual seminar division for Harvard Business Review. More recently, she created a new series to deliver customized programs and products to organizations and associations. Prior to coming to Harvard Business Review, Herrin was the vice president for content at womenconnect.com, a website focused on women business owners and executives. Herrin s journalism experience spans twenty years, primarily with Knight- Ridder newspapers and USA Today. At Knight- Ridder, she covered Congress, as well as the 1988 presidential elections. At USA Today, she worked as Washington editor, heading the 1996 election coverage. She won the John S. Knight Fellowship in Professional Journalism at Stanford University in The information contained in this summary reflects BullsEye Resources, Inc. s subjective condensed summarization of the applicable conference session. There may be material errors, omissions, or inaccuracies in the reporting of the substance of the session. In no way does BullsEye Resources or Harvard Business Review assume any responsibility for any information provided or any decisions made based upon the information provided in this document. 6

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