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Who should attend
- Senior-level executives who understand the importance of data in their organizations and want to harness it for greater competitive advantage. Ideal for executives with little or no expertise in data analysis or statistics
- Decision makers — from any size company, any industry, and any country — who seek to become more data and analytics savvy
About the course
Harness the power of data analytics to improve decisions, gain a competitive edge, and enhance your company’s performance, products, and processes.
Ask. Analyze. Act. Big Data, Strategic Decisions: Analysis to Action gives you the frameworks, tools, and confidence to ask the right questions, interpret the analysis, and use both to transform your data into strategic decisions. No technical or statistical expertise is required, just a desire to use data more effectively to make an impact on your organization — from marketing and operations to HR, supply chain, and business models.
Every morning you will learn conceptual frameworks and tools from world-renowned Stanford faculty to help you make smarter data-driven decisions. Every afternoon you will put learning into action, working on a real data challenge with a small team and a seasoned data analyst who will translate the technical into the actionable. Immerse yourself in design thinking and Agile methodologies to creatively manage your data initiatives. Silicon Valley leaders at the forefront of data analytics will share their experiences on how to best leverage data in a business context. And, Stanford faculty will provide insight into machine learning and the future of artificial intelligence, as well as explore the risks, perils, and ethics of using big data.
There’s no better place to learn about innovative and practical approaches to data analytics than on the Stanford Graduate School of Business campus, in the heart of Silicon Valley. Thoughtfully designed for data curious leaders, this experiential program brings together Stanford faculty from Stanford GSB and the School of Engineering combined with guest speakers, a lab visit, and a competitive simulation project for a truly comprehensive and creative learning experience.
- Learn and practice creative data-driven strategies to enhance decision making across every facet of your organization.
- Uncover hidden or unexpected connections, correlations, patterns, and trends to drive better decisions.
- Apply design thinking and Agile methodologies to develop big data solutions that are usable and deliver value.
- Explore the future of big data, machine learning, and artificial intelligence.
- Use conceptual frameworks and tools to recognize the power and potential of data to implement strategic initiatives and drive competitive advantage.
- Network with peers from diverse industries and functional areas to get fresh ideas about how data can be used effectively.
Data is everywhere and the implications are endless — it can help you determine who to hire, what prices to set, what supply source to focus on, and where to put your marketing dollars. Big Data, Strategic Decisions: Analysis to Action gives you the frameworks and tools, innovations and insights to make better decisions and compete in the age of big data.
The curriculum focuses on five key areas to give you a more holistic, innovative, and actionable learning experience. Stanford faculty, economists, data scientists, futurists, and Silicon Valley leaders collaborate to provide:
- Data-driven decision-making essentials from conceptual frameworks and tools to design thinking, Agile, and data visualization
- Experiential, team-based data simulation projects, working with a Stanford data scientist to put learning into action
- Practical applications of data analytics like marketing, business models, or HR to help you see connections to your own organization
- Insights and implications into the latest developments and future of big data from machine learning to artificial intelligence
- Understanding of the risks, limitations, and ethics of using big data
Below are just a few of the sessions you’ll attend as part of the program.
Design Thinking and Agile for Big Data Initiatives
Big data projects must generate actionable insights that will be usable by key decision makers to create significant value for an organization. Agile and design thinking are two complementary methodologies that can enable leaders to extract that value from their big data projects. This involves using design thinking to understand what decision makers need, and Agile to develop minimal viable big data solutions to test the usability of their output and the value they generate. Through iterative testing and refinements, leaders can design solutions that are usable and deliver value.
In these sessions, you will experience how design thinking and Agile can be combined to manage your big data initiatives. Through a series of experiential activities you will immerse yourself in the key steps of the two processes: empathy and needs finding, ideation, prototyping, developing and testing minimal viable solutions. You will leave with a toolbox that you can use to drive your own internal big data initiatives.
Machine Learning in Action
Statistical algorithms have been used in business for decades, in areas ranging from direct marketing through catalogs to credit scoring. However, the rapid increase in digitization of business activities has created the opportunity to make use of these algorithms for a much wider range of activities. Machine learning is a term that describes a new generation of statistical algorithms that can be used for tasks like image recognition or predicting customer churn. In this session, you’ll learn about the capabilities and limitations of machine learning, with a focus on the role of executive leadership of organizations that begin to deploy machine learning technology.
Using Data to Make Better Marketing Decisions
At the heart of a successful firm is an effective strategy to interact with its customers. Information technology offers new and exciting opportunities to learn about consumer wants and needs, communicate to customers, and reduce wasteful spending.
In these sessions, suitable for non-specialists, you'll study some of the tools and frameworks for data-driven decision making. You will learn how consumer purchase and search data can be used to support marketing decision making, including but not limited to product concept testing, communication, and pricing. You'll also discuss frameworks that apply outside marketing.
Stefanos Zenios is the Investment Group of Santa Barbara Professor of Entrepreneurship and Professor of Operations, Information, and Technology. He is also the faculty codirector of Stanford GSB’s Center for Entrepreneurial Studies. An innovative teacher and researcher, Zenios is the main archite...
Research Statement Susan Athey’s research is in the areas of industrial organization, microeconomic theory, and applied econometrics. Her current research focuses on the design of auction-based marketplaces and the economics of the internet, primarily on online advertising and the economics of th...
Research Statement Professor Mendelson leads the School’s efforts in studying electronic business and its interaction with organizations, markets and value chains, and incorporating their implications into the School’s curriculum and research. His research interests include electronic business, e...
Research Statement Paul Oyer studies the economics of organizations and human resource practices. His work has looked at the use of broad-based stock option plans, how firms use non-cash benefits, how firms respond to limits on their ability to displace workers, and how labor market conditions af...
Bio Bart Bronnenberg is a Professor of Marketing at Stanford Graduate School of Business. He is also a research fellow of the Centre for Economic Policy Research (CEPR) in London. He holds Ph.D. and M.Sc. degrees in management from INSEAD, Fontainebleau, France, and an M.Sc. degree in industrial ...
Research Statement Peter Reiss is an industrial organization economist and applied econometrician. His research studies how technology, consumer tastes, and industry structure affect firms’ competitive positions. In recent work, he has examined multi-part pricing policies, strategic entry and ent...
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