Course Details

  • Gain a practical introduction to artificial intelligence, exploring key concepts, ethics, and real-world applications

    AI Foundations

  • Learn the essentials of collecting, cleaning, and governing data to ensure reliability, security, and equity in AI projects

    Data Management 101

AI Foundations

Gain practical insights with our AI training for business, covering opportunities, risks and tools in our AI certificate program.

Who Should Attend

Managers, leaders and professionals seeking to understand AI’s business applications and risks. This is ideal for anyone pursuing an AI for business certificate to confidently apply artificial intelligence in organizational strategy.

In-Person

  • 2 Days

  • $2,795

Live Online

  • 2 Days

  • $2,795

  • Artificial Intelligence (AI) is transforming industries the way electricity once did, creating both challenges and unprecedented opportunities. This artificial intelligence certificate program provides foundational knowledge of AI tools, risks and governance, while showing you how AI already impacts your work.

    You’ll explore generative AI applications, business use cases and strategies for adoption. This AI training certificate program emphasizes practical applications, helping you understand the technology and how to apply it effectively.

    • Gain foundational understanding of AI and the current AI landscape

    • Increase your awareness of the future possibilities of generative AI

    • Explore the potential impact of AI on your organization—both pro and con

    • Analyze your organization’s business groups and ways roles may change

    • Increase your credibility with data-driven decisions and skill development plans

    • Learn how to think “AI” and embrace the opportunities that are presented

    • Learning what AI can do now and in the future and potential risks

    • Understanding the security implications of managing data and technology

    • Recognizing competencies needed to keep your organization competitive

    • Enabling yourself to use AI by applying the 4 Cs of success

    • Applying AI tools to extract useful information and diagnose work challenges

    • Blue sky and mind map: how to contribute to the organization by leveraging AI

  • Learning Objectives

    • Define Key AI Concepts and Terms

    • Understand the Business Imperative and Impact of AI

    • Identify Readiness to Adopt AI

    • Appreciate Need for Internal Alignment of AI Initiatives

    • Evaluate Design of AI Solutions that Deliver Value

    • Build Engagement in AI Conversations

    • Reflect on the Ethics and Governance of AI

    • Create Your AI Action Plan

    Overview of AI

    • Understand AI Terms and Definitions

    • Identify Familiar AI Solutions All Around Us

    • Gain an Overview of the Current AI Landscape

    • Learn About the Key Features of Algorithms

    Connecting Business Needs with AI Techniques

    • Recognize Business Situations Where We Should Look to AI for Assistance

    • Determine the AI Capabilities to Advocate for In the Solution That Your Company Decides to Build or Buy

    • Identify Assets You Need to Include in the AI Solution in Order to Actually Solve Your Problem

    Different Paths Toward AI Adoption

    • Learn the Typical AI Adoption Paths for Individual Contributors, Leaders, Teams, and Organizations

    • Understand the Common Risks and Rewards Along Each Path

    Assessing Your Resources

    • Understand the Environmental Conditions That Are Required for AI Success

    • Learn How Your Unique Abilities May Be Leveraged in AI Adoption

    • Consider What You Can Do to Fill Skill Gaps as a Professional and Across Your Team

    Designing Business Solutions with AI

    • Practice Identifying Business Problems That Can Be Improved with AI

    • Practice Selecting the Right Algorithm, the Right Data, and the Right Features to Create an AI Solution and Get Feedback in a Low-Stakes Environment

    • Practice Communicating the Value of Pursuing AI Initiatives

    Aligning AI Initiatives within the Organization

    • Understand the Strategic Value of Coordinating AI Initiatives

    • Craft a Vision for the Future and a Starting Point for Ethical Governance

    Putting It All Together: Your Action Plan

    • Choose What You CAN Do to Get Started with AI

    • Use the ENGAGE Framework for AI Adoption to Build an Action Plan

    • Share the Actions You Will Take First

Data Management 101

Who Should Attend

Business professionals, managers, analysts, and technology practitioners who work with data and want a stronger foundational understanding of how it should be managed. It is ideal for those who influence decisions involving data, systems, or strategy.

Get introduced to the foundational concepts, terminology, and practical applications of data management to help you understand how data drives better business outcomes.

In-Person

  • 2 Days

  • $2,795

Live Online

  • 2 Days

  • $2,795

  • In today’s digital economy, nearly every strategic initiative depends on data. However, many organizations struggle not because they lack data, but because they lack structured approaches to managing it. Data Management 101 provides a clear, practical introduction to the discipline of data management: what it is, why it matters, and how it supports modern business capabilities.

    Participants will explore core terminology, the functional areas of data management, the data lifecycle, and the technologies that enable data to create value. The course connects foundational concepts to real-world business applications, demonstrating how effective data management supports analytics, operational efficiency, compliance, and strategic decision-making.

    By the end of this course, you will understand how the functional areas of data management work together to enable better decisions, improved efficiency and regulatory compliance.

    • Build fluency in core data management terminology and concepts

    • Understand the 12 functional areas of data management and why they matter

    • Recognize how data moves through its lifecycle—from creation to deletion

    • Improve your ability to participate in strategic data conversations

    • Identify how modern business capabilities depend on sound data practices

    • Strengthen your awareness of governance, privacy and regulatory responsibilities

    • Gain a clearer view of the people, plans and frameworks required to improve data management

    • Core principles of data management and why they matter for business performance

    • Essential terminology and the major functional areas that support trusted, reliable data

    • The different types of data and how they affect storage, analysis and operations

    • The data lifecycle, from creation to deletion, and how managing each stage reduces risk and increases value

    • How data moves through the value chain and the technologies that enable it, including databases and cloud platforms

    • The connection between strong data management and effective analytics and decision-making

    • Governance, security and privacy responsibilities, including regulatory considerations

    • The people, roles and practical strategies required to strengthen data management over time

  • Learning Objectives

    • Explain the importance of data management for business performance

    • Define key data terms and concepts (data, information, database, data management, etc.)

    • Differentiate between structured, semi-structured and unstructured data

    • Describe the stages of the data lifecycle

    • Understand the data value chain and supporting technologies

    • Recognize the role of governance, privacy and security in responsible data management

    • Identify practical steps toward improving data management capabilities

    Foundations of Data Management – Key Terminology & Concepts

    • Overview of the importance of data management for businesses

    • Introduction to core terminology and definitions

    • Exploration of the 12 functional areas of data management

    • Memorable examples to clarify what each functional area means and why it is important

    • Building confidence to participate in data management discussions

    Data – What It Is and Its Life Cycle

    • Differences between structured, semi-structured and unstructured data

    • Real-world examples of each data type and their business relevance

    • Implications of different data types for storage, analysis and governance

    • Introduction to the data lifecycle: creation, storage, usage, sharing, archival and deletion

    • Why managing data across its lifecycle improves efficiency, compliance and security

    What’s Tech Got To Do With It? – The Data Value Chain

    • Traditional and modern data storage technologies (databases, data warehouses, data lakes, cloud storage)

    • Introduction to cloud computing and its role in data management

    • How organizations make IT decisions about storing and managing data

    • The relationship between infrastructure choices and long-term data capabilities

    • Connecting technology architecture to business value creation

    Data Management Applied – Enabling Better, Faster Decisions

    • The importance of data-driven decision-making

    • Case examples of successful data-driven operations

    • Introduction to analytics and extracting insight from data

    • Descriptive, diagnostic, predictive and prescriptive analytics

    • How analytics capabilities depend on strong foundational data management

    Responsible Data Management Involves Management

    • Common data security threats and vulnerabilities

    • Security measures: encryption, access controls and data masking

    • The importance of data privacy and regulatory compliance (e.g., GDPR, CCPA)

    • The role of data governance in ensuring quality, integrity and accountability

    • Managing data responsibly throughout its lifecycle

    Paths to Better Data Management – Plans and People

    • Strategies for implementing effective data governance frameworks

    • Key activities, risks and rewards of data management improvement efforts

    • Stakeholders and roles required to support sustainable data practices

    • Aligning people, processes and technology for long-term success

    • Turning foundational knowledge into practical next steps