participants learning about big data analytics in a Stanford Executive Education program

Big Data, Strategic Decisions: Analysis to Action

Curriculum

Gain a fundamental understanding of how to use data analytics to drive more creative and strategic decisions across your organization.

Today, many companies are overwhelmed by data, in a good way. But how do you harvest that data? How do you put it to work for your company so that you make better decisions? We are going to help executives figure out how to translate data into competitive advantage.
Paul Oyer, Faculty Director
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

Program Highlights

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.