Harris Analytics Institute

Cohort 2

Cohort 1

May 2026

Q2 2026 Cohort

May 2026 cohort

Use AI to catch what QC requirements, reports, and updates are hiding.

Enterprise HRIS & Quality Leader

Erie Insurance

National University

Sue Lhymn, Ph.D.

HRIS Manager with 25+ years in systems, data, and analytics

Data Quality & AI Risk Specialist

Pfizer

Emergent

University of Phoenix

Davenportu

Laura Harris, Ph.D.

CEO of an AI-driven, IP-generating, quality-focused company

<h2>Most project documents look complete. Requirements feel thorough. Status reports read confidently. Updates sound reassuring, but the gaps that cause delivery failures rarely announce themselves. They hide inside vague acceptance criteria, missing edge cases, and assumptions no one questioned at sign-off. </h2><h2>This workshop focuses on that critical window between receiving a document and approving it. Instead of relying on instinct or a quick read-through, you will learn how to use AI as a structured verification partner; one that surfaces what's missing, flags what's ambiguous, and confirms what actually needs to be true before the work begins. </h2><h2>If you have ever approved something that later turned into a costly rework, a missed dependency, or a scope dispute, this session will give you a repeatable system to prevent it. You will leave with an AI-powered verification framework you can apply to requirements, status reports, and project updates starting the same day.</h2>

<p>PMs, BAs, and Product Owners who are the last line of defense before a document moves to execution and want to be confident in that role</p>

<p>Stakeholders who approve project documents regularly but lack a structured method for knowing what to look for</p>

<p>Delivery leads who have experienced late-stage surprises caused by gaps that were present at sign-off but never caught.</p>

Design a Bioinformatics Pipeline You Can Actually Use

Biomedical Informatics & Data Expert

Helps researchers turn messy data into clear, executable analysis and results.

<p>Many researchers and analysts reach the same frustrating point: you have data, but you are not sure what to do next. You may have learned individual tools or methods, but turning them into a clear, repeatable analysis pipeline feels overwhelming.</p><p>This workshop focuses on that missing step. Instead of focusing on individual tools, you will learn how to translate your dataset into a concrete analysis question, map the exact steps needed to answer it, and choose methods and tools you can realistically run. By the end, you will have a structured plan for analyzing your own data, not just a collection of disconnected techniques.</p><p>If you have ever felt stuck between “I have data” and “I have results,” this session will help you move forward with clarity and confidence. You will leave with a pipeline you can run, adapt, and reuse, along with a framework for making better analysis decisions in future projects.</p>

<p>Researchers or grad students with biological data who feel stuck turning it into a clear, executable analysis plan.<br></p>

<p>Early-career bioinformaticians or analysts who know some tools but struggle to connect them into a coherent workflow.</p>

<p>Scientists transitioning into computational work who want a practical framework to decide what analysis to run next.</p>

Now What? How to Decide What Analysis to Run on Your Data

Co-founder/CEO, Harris Analytics Institute; Former Director of Training, MSU

Translate messy data into a focused question that directly guides your next step.

Turn your dataset into a clear analysis question

Design a pipeline detailing the steps to analyze your data

Build an analysis flowchart

Choose 1–2 reasonable tool options based on your data and what you want to learn.

Decide how to run an analysis on your data

Don’t Be Fooled: Drive Business Outcomes with ISO Standards

Sue Lhymn is an HRIS Manager with 25+ years in systems, data, and analytics.

Laura Harris is a systems and data scientist focused on ISO quality practice.

Be more efficient in working with stakeholders through customer focus, process and risk-based thinking.

Apply ISO quality principles in practical, everyday work.

Reduce ambiguity by defining requirements and expectations clearly.

Turn quality objectives into clear, actionable processes.

Continuously improve by managing requirements, processes, and controls consistently.

Learn directly from Harris Analytics Institute

Harris Analytics Institute

Workshops

How to Avoid the Quality Control Approval Trap When Using AI

Build a Bioinformatics Pipeline You Can Run on Your Own Data

Lightning Lessons

Now What? How to Decide What Analysis to Run on Your Data

Don’t Be Fooled: Drive Business Outcomes with ISO Standards

Drive Business Outcomes with ISO Quality Standards