Data Analytics & AI Leadership Forum: Digital Acceleration for the AI-Ready Enterprise - Argyle Executive Forum Events


The complexities of creating and deploying advanced analytics grows daily and the pressure to deliver business value has never been greater!

As the business landscape ebbs and flows, data and analytics leaders such as Chief Data Officers (CDOs) and Chief Analytics Officers (CAOs) must fine-tune strategies to build a data analytics-centric culture and improve business outcomes.

Attend the September 16th Data Analytics and AI Leadership Forum to hear how top companies have improved data and analytics strategies to enable agile, digital acceleration at their AI-ready, data-rich organizations.

You will learn:

  • Methods to accelerate change, and build data literacy and trust within your organization
  • How to apply critical human decision-making insights for analytics success
  • The latest data and analytics strategies, trends, and tools (including ML/AI)
  • How to treat data as an asset for improved business outcomes and information flow
  • Methods for extracting ‘understandable to humans’ insights from Machine Learning models (ML and deep learning applications)
  • Data integration tactics to help you use analytics to deal with the data monster in everyone’s analytics closet (unorganized, incomplete, or inaccurate data)
  • Beyond KPIs – Creating and measuring the metrics that matter









11 AM - 11:30 AM ET

Keynote: Epic AI Fails - Avoid Data Dysfunction and Other Project Killing Mistakes

Machine learning and AI allow companies to better predict outcomes, automate processes, make decisions, and generate improved content. ML and AI are also a heavy lift for some companies as they require constant maintenance and support. These processes also require that your organization collect and store data in a manner that can be used by your data scientists and data engineers.

Attend this keynote session to learn the most common AI/ML pain points and how to overcome them. You will also learn:

  • Which predictable and avoidable mistakes most often lead to the failure of ML/AI projects
  • Why bias in data, data selection, and the algorithms used are crucial, and how to sure up your company’s data processes
  • How to avoid feedback failures, model stability issues, and wonky metrics
  • What’s next in AI/ML technology?

11:35 AM - 12:05 PM ET


Abstract coming soon!

12:10 PM - 12:55 PM ET

Panel Discussion: Ethical AI - Building Privacy & Trust in Our Data Driven World

What does the ethically responsible delivery of AI look like? Many organizations struggle with the building of reliable and trustworthy AI, while also embracing long-lasting innovation.

In this timely and practical panel discussion, you will learn what AI data bias is and how to uncover it within your organization, as well as how to embed privacy and ethics in all AI designs/programs.

You will also learn:

  • Ways to ensure that data is clean and unbiased before it is used in analytics or machine learning
  • How to create a culture where ethical data is embraced across the organization
  • Best practices for compliance with data privacy laws when handling and protecting sensitive information (liability and security)

1 PM - 1:30 PM ET


Abstract coming soon!

1:30 PM - 2 PM ET


2 PM - 2:30 PM ET


Abstract coming soon!

2:35 PM - 3:05 PM ET

KEYNOTE: Cloud Based AI and ML - Pump up Productivity and Decrease Spend

AI and cloud computing when properly managed are powerhouse process and profitability enhancers! However, the combination of these technologies raises a myriad of cost, reliability, security and productivity issues.

Attend this keynote session to learn how to address the most common cloud-based AI/ML issues while lowering cloud spend.

You will also learn:

  • How to make your AI-driven cloud computing efforts more strategic and insight-driven
  • What often hinders the advantages of cloud-based machine learning algorithms
  • Why prediction speeds are a primary concern, and what to do about it
  • Best practices for intelligent data security

3:10 PM - 3:55 PM ET

PANEL DISCUSSION: Measuring the ML Metrics that Matter

A recent industry study showed that 85% of Machine Learning (ML) projects fail to deliver value! That means big financial losses and oodles of wasted expert hours!

There are numerous (avoidable) mistakes that lead to the failure of ML projects. However, a lack of quality metrics is one of the most common.

Attend this panel to learn why great Machine Learning starts with high quality data, and which metrics will really make or break your ML efforts.

You will also learn:

  • How top companies are measuring the success of Machine Learning products and processes
  • Ways to strike a balance between measuring business performance and model performance
  • Methods for defining and quantifying success
  • Smart (and not so smart) model evaluation metrics


We are proud to share with you the following Argyle Industry Influencers. Their contributions to Argyle help keep the programs we offer our membership current and relevant, so we can continue delivering you premiere experiences, content development, and membership engagement.

Dr. Nels is a clinical systems IT director at a fortune 5 company (CVS Health), the author of Graduation with Civic Honors, an avid writer, tech chaser, sports card collector, and a major TensorFlow enthusiast. You can find on Twitter @nelslindahl or LinkedIn and you can subscribe to my Substack newsletter I publish it every Friday