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

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.

Decisions, Decisions: Why Big Data Matters

The business imperative to better exploit data is strong and getting stronger, but simply collecting data is not very useful. The key is to figure out what data to collect, how to analyze it, and how to use it to make business decisions.

In this session, we look at two very different settings where companies used data to generate competitive advantage. These two examples highlight both the potential to use data to improve performance and the fact that data science initiatives are extremely varied.

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.

Design Thinking

Design thinking is a human-centered, prototype-driven process for innovation that can be applied to product, service, business, and organizational design. At its core, design thinking offers us mindsets, methods, behaviors, and abilities to help us solve problems and create meaningful solutions, big or small. 

In this session, we look into how this powerful way of working might help to transform the use of AI and big data by helping us to conceive of ways to prevent failures or flops

The Future of the Workplace

The only thing predictable about the future of work is that there will be lots of change. One day you read that there is a looming labor shortage as the population ages, the next you read that mass unemployment is right around the corner due to the advent of robots and other AI. 

In this session, we’ll look at trends in the labor market and the future of work and consider how demographic changes present business and labor market opportunities, as well as challenges. Will the robots really come and, if so, what are the implications for today’s employers? We’ll discuss how the answers to these and related questions vary by labor market and by what expertise in AI your company needs. We’ll also consider how the nature of future working relationships will be different due to the disruptions caused by COVID, even when the pandemic is long gone.

Autonomy Application

Building robust decision making systems is challenging, especially for safety critical systems such as unmanned aircraft and driverless cars. Decisions must be made based on imperfect information about the environment and with uncertainty about how the environment will evolve. In addition, these systems must carefully balance safety with other considerations, such as operational efficiency. Typically, the space of edge cases is vast, placing a large burden on human designers to anticipate problem scenarios and develop ways to resolve them. 

In this session, we’ll discuss ways in which AI can be applied to the design of these safety critical systems, which have the potential to significantly improve robustness of these systems. We’ll also outline some methodologies for addressing two major challenges in this approach.