How to Revamp your BI and Analytics for AI-based Solutions

As the pandemic has proven, digital transformation is possible—and at speed. Many more aspects of business operations have moved online or have enabled remote or no-touch access. This evolution has generated another growth spurt of “big data”, from websites, social media, and the Internet of Things (IoT). With new customer behaviour likely to stick after the pandemic and working from home remaining an important factor, novel approaches to decision-making support are an increasingly important consideration for many organisations.

In this context, the recent growth in interest in and focus on the use of artificial intelligence (AI) and machine learning (ML) across all aspects of business in every industry and government raises important questions. How can AI/ML be applied at management levels in support of decision making? What new possibilities or problems does it present? How far and how fast can businesses move to benefit? What are the downsides?

The seminar

AI, combined with big data, IoT and automation, offer both the threat and the promise of revolutionising all aspects of IT, business and, indeed, society. In this half-day session, Dr Barry Devlin explores what will enable you to take full advantage of emerging AI technology in your decision-making environment. Starting from the familiar worlds of BI and analytics, we position traditional and emerging BI and analytics tools and techniques in the practical application of AI in the business world. Extrapolating from the rapid growth of AI and IoT in the consumer world, we see where and how it will drive business decision making and likely impact IT. Based on new models of decision making at the organisational and personal levels, we examine where to apply augmentation and automation in the roll-out of AI. Finally, we address the ethical, economic and social implications of widespread adoption of artificial intelligence.

Learning objectives

• A comprehensive architectural framework for decision-making support that spans from BI to AI
• A brief primer on the evolution, key concepts, and terminology of AI
• Understanding the relationship between “big data” / IoT / social media and AI /ML and how it drives business value
• Approaches to applying AI to decision making
• Augmentation vs. automation of decision making
• How AI, social media, and IoT impact the IT department
• New technology solutions for business applications using AI and IoT, including embedded BI and edge analytics / social media
• How to evolve today’s BI to future AI-based solutions
• Ethical, economic, and social considerations for using AI to support decision making.

Intended for you

This seminar is of interest to all IT professionals and tech-savvy businesspeople directly or indirectly involved the design, delivery, and innovative use of decision making support systems, including:

• Enterprise, systems, solutions and data architects in data warehouse, data lakes, BI and “big data”
• Systems, strategy and business intelligence managers
• Data warehouse, lake and decision support systems designers and developers
• Tech-savvy business analysts and data scientists.

Course Description

We will send the course materials and meeting instructions well in advance as well as the invitation with hyperlink to join us online. The seminar will start at 09:00 and lasts until 13:00. The online meeting will be available at least one half hour earlier so please log in timely in order to check your sound and video settings beforehand.

1. Architectural Framework and Models for Decision-Making Support
   • Conceptual and logical architecture for information use in decision making
   • How businesspeople really make decisions and take actions
   • Considerations beyond rational choice theory and cognitive biases
   • Organisational models for decision making / action taking
   • Architectural considerations—from traditional BI to operational analytics

2. Applying AI to Decision Making: Top-Level Considerations
   • A brief primer on AI terminology, techniques such as artificial neural networks, and emerging approaches
   • From training to operational use—data and technology options
   • Automation vs. augmentation—the key choice in applying AI
   • AI considerations for operational, tactical and strategic decision-making
   • Positioning AI in relation to Data Warehouses, Lakes, and other constructs

3. Applying AI to Decision Making: The Devil in the Detail
   • AI in information preparation and governance
   • AI in BI and analytics tools
   • Model management
   • Centralisation vs distributed processing approaches
   • Migrating from BI to AI—key steps and options

4. Building the Future of Decision Making with AI—Key Considerations
   • Ethical considerations for analytics and AI in business
   • Specific ethical concerns for AI-driven decision making
   • The dangers of surveillance capitalism
   • Wider ethical concerns for society
   • Potential and possible impacts of AI on the economy and employment.