Executive Summary By Blake Johnson

Transcription

1 Executive Summary By Blake Johnson Creating Business Value with Enterprise Data and Analytics A one-day event presented by: Stanford s Management Science and Engineering department, The Global Supply Chain Management Forum, and Teradata Corporation May 10, 2012 On May 10, 2012 the Department of Management Science & Engineering at Stanford University held an industry forum Creating Business Value with Enterprise Data and Analytics. The event was co-sponsored by the Stanford Global Supply Chain Management Forum and Teradata Corporation and was led by Blake Johnson, a consulting professor in the Department of Management Science & Engineering. It was the third annual event at Stanford on creating competitive advantage with enterprise data and analytics, and was attended by roughly 100 executives from business, analytics and IT roles at leading companies from a range of industries. The event was organized around the three foundational elements of creating business value with enterprise data and analytics: 1. Hardware and software for making enterprise data and analytics directly accessible to business users flexibly, scalably and cost-effectively 2. Building analytic capabilities, including technical skills and cross-functional collaboration required, and how to hire, train and partner to achieve them 3. Planning and managing initiatives: Where to start and who to involve to deliver value quickly and grow and scale efficiently Key takeaways from the discussion of each of these topics are summarized below Framing the Day's Discussion Blake Johnson began the day with an overview of the event s key themes. The key message was that enterprise data and analytics technologies have reached a maturity that enables their technical aspects to become largely invisible to business users. This gives business users what they want, which is direct, user-friendly access to enterprise data, analytics tools, and platforms for easy collaboration and sharing of results. Under the hood, automation embedded in enterprise technologies ensures data is stored on hardware optimized to its size and level of use. Granular operational data and other big data is automatically pre-processed and preaggregated to high-value, user friendly forms. Inexpensive, off the shelf analytic modules or

2 analytic aps provide plug and play solutions to many common analytic tasks. Internal social networks provide secure and easy to use platforms for collaboration and delivery of data and analytics. The net result is a sea change in the ability of non-technical business users to access enterprise data and deliver business value with analytics on a largely self-service and therefore highly scalable and cost efficient technology foundation. Leading companies with established foundations are now focusing on large scale organizational implementation, and enterprise data and analytics are becoming simply how tens of thousands of employees do their job. Equally important, companies earlier in the journey have established and accelerated pathways to follow that deliver value and drive organizational learning on the way. Hardware and software for making enterprise data and analytics directly accessible to business users flexibly, scalably and cost-effectively Chris Rogaski, Senior Director of Analytic Applications at ebay, began the day by describing the hardware and software infrastructure ebay has established to provide business and analytics users across the company with direct access to ebay s tens of petabytes of data, and the analytic and collaboration capabilities required to create business value with it. The foundation of ebay s capabilities is an integrated hardware ecosystem able to match data by type, volume and usage characteristics to the most appropriate form of storage, from ebay s high performance enterprise data warehouse for heavily-used, performance-critical data to Hadoop clusters for deep storage of massive and infrequently-used unstructured data. Business users across the company are able to access and work in this data ecosystem directly via automated, self-service personalized virtual data marts, providing low cost, high performance access to a shared single version of the truth. Users also have access to a range of analytic and visualization tools to create and deliver value with the data, and can collaborate and share results with ebay s Data hub internal social network. Todd Walter, Chief Technologist of Teradata delved deeper into the business role and value of a tightly integrated and diverse hardware ecosystem specifically its ability to ensure the diversity of data within a company is appropriately located on hardware with the cost and performance characteristics best matched to data size, type and usage, from high-end solid state and in-memory storage for critical, high utilization data to inexpensive archival storage. For example, at most companies 30-50% of data requests draw on just 1% of the company s data typically operational data critical to a range of core activities. In contrast, only 10-20% of data requests target the least heavily used 80% of a company s data. As a result, large improvements in both cost and performance can be realized by locating hot data on high performance hardware and cold data on lowest cost storage.

3 To operationalize management of data in this way, automated capabilities to monitor data usage and move data across hardware types over time are required. In addition, to ensure seamless, location independent data access by users, data ecosystem mapping and access management technologies are required. Both capabilities are now available from technologies such as Teradata s Unity offering. Four panelists shared their key insights. Jasmina Pavlin, Enterprise Data Systems Department Manager for Intel, described several of Intel s many uses of enterprise data, from global aggregation of spend and other operational data to analysis of the massive data sets generated by Intel s sophisticated manufacturing equipment. Stephanie McReynolds, Senior Director of Product Marketing for Teradata Aster, described technologies that enable queries written in the industry-standard SQL to be automatically translated into the Map-Reduce framework and executed on large data sets stored in Hadoop. Priyank Patel, Senior Product Manager of Analytic Applications for Teradata Aster, described the emerging supply of analytic modules created to enable out of the box execution of common analytic tasks. Like SQL-to-Map reduce tools, these technologies enable substantial reductions in the cost and lead time required to implement advanced analytics, and with it the often-quoted need for large numbers of data scientists. Last, Steven Hillion, Chief Product Officer of Alpine Data Labs, highlighted the emerging role of internal social networks as low cost, easy to use delivery vehicles for reporting and analytic capabilities within a company. Building analytic capabilities, including technical skills and cross-functional collaboration requirements, and how to train, hire and partner to achieve them Once enterprise data is organized and accessible, analytic capabilities become central to leveraging it to deliver business value. John Ahrendt, Senior Vice President of Enterprise Data & Analytics (EDA) at Wells Fargo, described the role his organization plays in enabling enterprisewide data and analytics to deliver business value and competitive advantage. As a sharedservices organization, EDA at Wells Fargo provides foundational capabilities used across the company, and works collaboratively with the analytics teams located in each key functional area of the bank to ensure efficiency, scalability and appropriate reuse of their analytic capabilities. Matrix organizational structures of this kind for analytics teams are proving effective across a wide range of companies. As is typical under matrix structures, custom and ad hoc analytics at Wells Fargo are managed by individual lines of business, while production capabilities and shared analytics are managed by the central EDA organization. Thomas Olavson, Director of Operations Decision Support at Google, focused on the skills analytics teams require for success. He began by contrasting people with I shaped skills sets, representing strong technical depth, with people with T shaped skill sets, who combine technical depth with breadth from soft skills and business domain knowledge. T shaped skills are essential to identifying and effectively addressing the most important business problems, and to ensuring technical skills are used to deliver useful results grounded in business reality.

4 To deliver high quality analytics, technical skills are required from a range of domains, including computer science, operations research, statistics, and strategic planning, each of which brings unique skills as well as its own style and biases. Careful management is required to ensure strategic planning skills are used to identify the right questions, operations research skills are used to effectively model business problems, statistical skills are appropriately utilized in the analysis, and computer science coding skills are used to implement high performance solutions. Panelists James Deaker, Vice President of Data Solutions and Insights at Yahoo!, emphasized the critical importance of building strong communication and working relationships across business, analytics and IT teams. Sanjeev Kumar, Analytics Product Manager at Dell, described Dell s work to combine analytics capabilities with its hardware and service offerings to customers. Planning and managing initiatives: Where to start and who to involve to deliver value quickly and grow and scale efficiently The last part of the day focused on organizational implementation and delivery of business value. Wes Hunt, Vice President of Customer Analytics at Nationwide Insurance, began by describing the broad-based organizational adoption, training and change management that has enabled tens of thousands of Nationwide employees to leverage enterprise data and analytics to deliver greater value through their daily interactions with customers. Customer care and treatment is the primary differentiator in consumer financial services, and customers expect Nationwide s relationship managers to know about me, care about me, act on my behalf and be easy to do business with. Wes compared investment in data and analytic technologies to outfitting a sports team with necessary uniforms and equipment a foundational step that must be augmented with training, processes, skills and coaching in order for the team to perform to its potential. While many of the most visible business applications of enterprise data and analytics to date have focused on customer interactions, equally large amounts of value are now being created with sensor and machine data. Three GE executives described GE s billion-dollar, board-level initiative to accelerate these activities at GE. Jonathan Ballon, Chief Operating Officer of GE s central Software Center of Excellence, Aiman Abdel-Malek, General Manager of Health Care Global Services Technology, and Erik Udstuen, General Manager of Intelligent Platforms, began by highlighting the magnitude of the value creation opportunity across GE s verticals such as health care, aerospace and energy. GE s established its new central software and analytics organization to provide shared resources and support development of best practices across the analytics teams embedded in these and GE s other primary business units. Working together, the central and vertical teams combine the deep, industry-specific knowledge of the business units with the best in class software and analytics capabilities of the central software and analytics organization. The result is new information and analytics driven products and services which allow customers to realize greater value from GE s products, and transform how GE interacts with and serves its customers.

5 Oliver Ratzesberger, Vice President of Information Analytics & Innovation at Sears Holding Company, ended the day by describing the accelerated path Sears has taken to bring the power of enterprise data and analytics to its core business activities. Drawing on his leadership experience at ebay, Oliver began by transitioning Sears from a large number of data marts to a centralized data infrastructure. Three factors enabled him to secure broad organizational support: 1. Clear documentation of the total cost of ownership of the existing data marts, ranging from the business impact of siloed data to the cost of data redundancy and inconsistency, on-going data transfers, and multiple service and support teams 2. A commitment to democratic, self-service access to enterprise data once centralized and integrated 3. Strong senior management support for both factors #1 and #2. Sears next launched Analytics as a Service capabilities on top of its data warehouse. Selfservice capabilities provides users with a virtual environment within the data warehouse to work in, along with analytic and visualization tools. Production-scale capabilities are developed by a central team of analytic experts in partnership with business, with the sophisticated customer segmentation capability now used broadly across the company a key example. Key take-aways and next steps The forum concluded with a summary of key insights: New technologies are making enterprise data and analytics cheaper and easier: Technologies now enable large and diverse data ecosystems to do much of their own management and performance optimization, and the last mile to business users can now be largely automated with self-service virtual environments and userfriendly analytics and collaboration tools. The secrets of large-scale organizational adoption and value creation are being unlocked: Enterprise data and analytics now provide the foundation for how tens of thousands of people do their jobs at information and analytics-driven companies across a range of industries. Successful companies craft pathways to this end state by delivering value and enabling organizational learning as they go, and building a scalable and lowest total cost of ownership technology foundation. Enterprise data and analytics can t succeed if business and IT are siloed: The boundary between business and IT must blur and become a continuum, and failure to do so may be the only insurmountable barrier to success. Analytics teams

6 naturally operate at the midpoint of the business-it continuum, and play a bridging role. With the promise of enterprise data and analytics now becoming a reality, activity and enthusiasm across the group was high. Anticipation of next year s event at Stanford was strong, and a range of follow-up activities were planned to enable on-going collaboration and sharing of best practices in the interim.