U.S. Army Data Driven Leadership Certificate Program

We must become data-centric if we want to be part of the national security solution for the future. Learning together in DDL...was a game changer. U.S. Army. Maj. Gen. James Gallivan (Ret.)

We took a quantum leap in that week. Going through the [DDL] program as a team helped everyone understand how to collect and structure data so that we could do a better job of analysis and storytelling. Christopher Barrett, Executive Director of the U.S. Army Evaluation Center

• Enterprise Data Management Foundations

  This module will set the stage for the program, reviewing the core components that make-up an enterprise data management program. This module will also review the vast sources and types of data available today and the stages of the data lifecycle management process and discuss benefits of EDM to organizations today.

• Enterprise Data Strategy and Governance

  This module will discuss the business case (value realization of data) for enterprise data management, and the foundational importance to digital transformation initiatives, through the development of a mission-aligned, data-driven strategic roadmap. The module will also outline the role of the data leader and their charter to drive strategic decisions for data pipeline development, aligning these decisions with the broad company’s data strategy. We will also define data governance, look at the different elements of establishing good data governance, and consider ways to implement an effective data governance agenda.

• Building/Maturing the Data Organization

  This module is an overview of the practical operations of the data/AI function and will cover how to properly plan, structure, budget, run and obtain organizational “buy-in” for the data and AI office function. The goal of this module is to balance the art and the science of strategic data management and its intersection with business strategy, product development, information technology, and operations. It will also address how to mature data capabilities in the organization over time to meet the needs of the organization.

• Data Engineering

  As organizations seek to gain a competitive advantage through data, analytics, and AI, data leaders face increased pressure to provide data infrastructure that enables and accelerates data-driven innovation. Data leaders are chartered to drive strategic decisions for data infrastructure and technology, aligning these decisions with the broad company’s data strategy. To stay competitive, companies must understand and adopt modern trends: leveraging cloud, deploying automation, adopting emerging architectures, implementing best practices, and tooling. This module will provide students with an understanding of the critical technology choices that organization needs to make and prepare them to make these decisions.
  

• Data-Driven Digital Transformation

  This module will inform and discuss ways in which managers can develop a data-driven transformation culture that feeds the larger digital construct and become change leaders to enhance the competitive advantage of their organization.

• Demystifying AI

  This module provides organizational stakeholders with a practical guide to using AI in the near future. We begin with an overview of AI technologies (including generative AI via LLMs) and what they can and cannot do. We give some tips for finding, organizing and retaining AI talent and some tips for creating an enterprise AI strategy. This module will also discuss how stakeholders can assess/mitigate the development of bias within AI, and why AI systems must be designed, developed, and deployed responsibly to ensure accountability and transparency, and lead toward a more just and equitable world.

• Modern Analytics for Business Intelligence

  This module focuses on the analytic component of Business Intelligence
  in the context of its practical application. Participants will have a chance
  to gain and solidify knowledge of the most important contemporary
  methods of analytics, to develop understanding of practical applicability,
  how to formulate analytic tasks in support of business objectives, and how
  to define successful projects.

• Managing Data and Analytics Projects

  With the growing demand for data science and AI skills, there are many options for students to learn fundamentals of data and analytics modeling. There are fewer opportunities to learn how to manage analytics projects, which often involve leading teams with diverse skills and interacting with stakeholders in a variety of roles. Using a decision-driven framework, this module offers students practical guidance and experience around the process of initiating, delivering, and evaluating analytics projects.

• Operationalizing AI

  In this module, students will explore the comprehensive AI lifecycle, from initial business scope to model stewardship. The focus will be on the necessary roles, responsibilities, and skills needed, as well as on the governance and technology required to operationalize AI efficiently and effectively. Attention will be given to the principles, policies, and practices of Responsible AI, as well as the emerging field of Generative AI and the unique challenges it poses for operationalization.

  Students who complete this module will have the ability to design and manage AI systems from an end-to-end perspective to effectively deliver the ROI while also ensuring the responsible use of AI.

• Data Visualization and Communication

  This module will focus on best practices for data visualization. The module will also discuss effective ways to communicate data management-related organizational goals via well-crafted stories to your respective stakeholders. 

  • Evolution of data reporting and visualization
  • Goals and examples of visualization (the good, the bad, the ugly)
  • Storytelling best practices
• Data Security and Privacy
  Collecting and sharing information raises opportunities and creates challenges for organizations. Understanding how organizations think about their data (and value, or not, their privacy) and what the individual, organizational, and societal economic consequences of data collection/protection decisions are can help managers navigate these complex tradeoffs.