Big Data and Artificial Intelligence Principles and Practices

Introduction

This 5-day course is a combination of the two back-to-back courses: Big Data Principles and Practices (3 days) and Artificial Intelligence Principles and Practices (2 days).  It provides a discounted, 5-day option to participants who choose to learn about both topics and their roles in Data Management.

The rise of Big Data has accelerated the pace of disruption in virtually every industry, creating vast ambiguity and unease. However, these changes are also creating enormous opportunities, as the tools to prosper during the age of Big Data disruption are becoming more accessible and available. To become a disruptor, embracing Big Data and deep-diving into the technology is only a part of the answer. It needs to be combined with understanding how Big Data can drive value within our functions, companies, and industries.

Furthermore, organizations are creating an avalanche of data, and with Artificial Intelligence (AI) technology we can put that data to work in order to increase benefits and reduce costs. With modern technology, we can use structured and unstructured data and apply Artificial Intelligence to bring new possibilities to improve decision making, improve company performance, and augment human capabilities. However, this new field of science comes with new terminologies, technologies, jobs, and organizational processes.

During this course, participants gain a thorough understanding of how Big Data and Artificial Intelligence are creating new insights that are enabling organizations to develop better products, to service clients in a more personal manner, and to make supply chain processes more efficient. Participants also gain insight into the transformation their organization can go through to become data-driven.

Objectives

By the end of the course, participants will be able to:

• Assess and explain the value that Big Data and AI can deliver to their industries, companies, and functions
• Demonstrate Big Data and AI technologies and their benefits
• Develop the maturity of Big Data within their organization
• Apply a variety of use cases to drive ideation
• Build an organization-wide Big Data program
• Discuss on a qualified level with business and data specialists on relevant topics

Training Methodology

This is an interactive course. There will be open question and answer sessions, regular group exercises and activities, videos, case studies, and presentations on best practice. Participants will have the opportunity to share with the facilitator and other participants on what works well and not so well for them, as well as work on issues from their own organizations. The online course is conducted online using MS-Teams/ClickMeeting.

Who Should Attend?

This course is designed for: organizational managers and leaders looking to understand Big Data and Artificial Intelligence, and take ownership of the data management agenda within their companies; functional leaders that are designing the Big Data and/or AI Roadmaps for their function; Big Data practitioners ready to take a business view on Data Management; and experienced practitioners looking to gain latest insights.

In short, this course is for managers wanting to identify what Big Data and Artificial Intelligence can do for them and to drive the Data and Digital Transformation, rather than understand the technical methodologies of what happens underneath its hood. 

Target Competencies

• Formulate business objectives for Big Data
• Project management of Big Data and/or AI
• Big Data and AI change management
• Big data technology sensing
• AI Best Practice Application

Course Outline

Value Creation with Big Data

• Introduction to Big Data technologies
• Trends in Big Data
• Big Data applications, use cases, and best practices across industries and functions
• Data sourcing strategies and challenges
• Ideation phase: creating first successes
• From ideation to Proof-of-Concept and minimum viable product

Managing Big Data Transformation

• Big Data Maturity model
• Developing a Big Data roadmap
• What does good look like determining your Big Data end game
• Orchestrating Big Data maturity across data, technology, and people

Big Data Leadership

• Lean/agile working in support of Big Data transformation
• Key success factors for adoption of Big Data at speed
• Required skills & competencies for successful digital transformation
• Understand the mindset of digital disruptors

Introduction to Artificial Intelligence (AI), Machine Learning (ML) and Data Science

• AI in a historical setting and combinatorial technologies
• Introduction to AI, concepts, narrow and general AI
• Different types of AI
• AI - sense, reason, act
• The thinking in AI: Machine learning

Advanced Analytics vs Artificial Intelligence

• Looking back, now, forward
• 4 types of data analytics
• Analytics value chain

Algorithms but without Technical Jargon

• Supervised learning
• Unsupervised learning
• Reinforcement learning

Data as Fuel for AI

• Structured and unstructured data
• The 5 V’s of data
• Data governance

The Data Engineering Platform

• Just enough to understand the data architecture
• Big data reference architecture
• 3 categories of data usage

AI Opportunity Matrix

• Successful use cases by Porter’s value chain
• Primary activities
• Supporting activities

Successful use Cases by Technology

• NLP
• Image recognition
• Machine learning

Ideation of AI Projects

• AI Funnel process
• Several idea generations approach
• Prioritize projects
• AI project canvas

Running of AI Projects

• Machine learning life cycle
• AI machine learning canvas
• When to make and when to buy AI solutions

How to Transform into an AI Ready Organization

• Use the AI strategy cycle
• Dimensions of the AI framework
• A practical approach to assess the AI maturity of the organization
• Best organizational structures
• Benefits of an AI Center of Excellence
• Skills and competencies

AI and Ethics

• Risks of AI
• Ethical guidelines
• Realizing trustworthy AI​