Podcast Summary: The Digital Executive

Episode: Dr. Wendy Lynch on Making Data Drive Decisions | Ep 1180
Date: January 9, 2026
Host: Brian (Coruzant Technologies)
Guest: Dr. Wendy Lynch, Analytic Translator, Researcher, Author

Overview

This episode explores how organizations can bridge the gap between analytics and business by focusing on the critical role of the "analytic translator." Dr. Wendy Lynch shares insights from decades of experience helping Fortune 100 companies make data-driven decisions that stick. The discussion centers on the importance of communication, team synergy, board engagement with technology, and the upcoming changes in AI adoption in business.

Key Discussion Points & Insights

1. What is an Analytic Translator?

[01:46]

• Dr. Lynch defines an analytic translator not just as someone who explains complex analytics, but as a team builder fostering mutual appreciation between business and technical teams.
• Their main value is in helping teams "get the questions right the first time," enabling analysts to deliver the information decision-makers truly need.
• Miscommunication is identified as a chief barrier:

  "Chief analytic officers said their teams are 50 to 80% less effective than they should be because of miscommunication." — Dr. Wendy Lynch [02:42]

Notable Analogy

[03:40]

• Dr. Lynch compares business and technical teams to high school cliques (robotics club vs. football team):

  "Now we're asking those two groups to work really well together as adults." — Dr. Wendy Lynch

2. Framing the Right Questions for Analytics

[04:24]

• Leaders shouldn’t try to become pseudo-analysts; instead, they should build strong communication with analytic translators.
• The key isn’t how leaders ask data questions, but whether they are willing to provide five minutes of context and purpose for what they need:

  "If I don’t have the context, the purpose, the expectations, the knowledge of what decision that leader is trying to make, then I’m guessing. And those guesses are rarely correct." — Dr. Wendy Lynch [05:28]

3. Why AI and Analytics Often Fail to Drive Business Decisions

[07:00]

• Miscommunication, speed, and pressure from above (e.g., boards) are major hurdles.

• Boards often push for analytics/AI based on industry hearsay despite lacking expertise, which can undermine projects.

• Dr. Lynch highlights the misconception that AI adoption is purely technical, when in reality it's a "social, cultural change":

  "Humans have to be part of the design, training, testing, implementation, or AI doesn’t work." — Dr. Wendy Lynch [08:48]

• She advises boards to update their membership:

  "Too many boards are operating with experts from 30 or 40 years ago. So my advice here is: board chairs, if you’re listening, update your boards with people who know about current technologies and the current trends in AI." — Dr. Wendy Lynch [08:02]

• Stat: Only 2% of current board members have advanced AI knowledge. [09:56]

4. The Future: Shifts in the Human-AI Relationship

[10:32]

• Predicting a decade out is tough due to the rapid evolution of AI—change in the last 18 months has outpaced previous expectations.

• Short-term (Next Year):
  Significant shifts in job types and narrow AI implementations (e.g., customer service, sales, coding).

• Medium-term (Next 5 Years):
  There will be a "huge chasm" between companies who lay strong foundations (data infrastructure, governance, change management) and those who don't.

• Stat:

  "90% of [high-level data professionals] said that leaders are not paying enough attention to problems with their data or data structure. ... one in five have the level of data structure that they need, and fewer than one in ten have data integration and interoperability between the different areas to really leverage AI." — Dr. Wendy Lynch [12:33]

• Advice:
  If leaders don’t understand their data and aren’t advised by analytic translators, "you’re going to continue to wander around in the dark while others are leapfrogging forward." [13:36]

Notable Quotes

• “We have enough analysts and we have enough horsepower these days. But what happens is ... miscommunication.” — Dr. Wendy Lynch [01:59]
• “It’s more about their willingness to spend five minutes answering what the analytic translator needs from them.” — Dr. Wendy Lynch [05:07]
• “AI is a social, cultural change in addition to a new type of technology. So if they think they can hand it off to IT and just snap their fingers ... it’s never going to work.” — Dr. Wendy Lynch [08:25]
• “Only 2% of current board members have advanced knowledge of AI.” — Dr. Wendy Lynch [09:56]
• “Within five years, there’s going to be a huge chasm between the companies who have put in the real fundamental work and those who didn’t.” — Dr. Wendy Lynch [11:28]
• “If you are not understanding how your data will be used, can be used, should or shouldn’t be used ... then you’re going to continue to wander around in the dark while others are leapfrogging forward.” — Dr. Wendy Lynch [13:33]

Important Timestamps

• 01:46: Definition and importance of analytic translators
• 03:40: Analogy: Business vs. technical teams ("cafeteria groups")
• 04:24: Framing questions and leader-analyst relationships
• 07:00: Patterns in failed analytics/AI initiatives
• 09:56: Board composition and technical knowledge gap
• 10:32: The evolving relationship between humans, analytics, and AI; short-term and long-term predictions
• 12:33: Data structure gaps in organizations

Tone and Style

• Conversational, practical, and approachable; Dr. Lynch uses analogies and real-world examples to ensure clarity for both technical and non-technical listeners.
• Emphasizes collaboration, humility, and the importance of human factors over pure technical prowess.

Key Takeaways

• Analytic translators are essential for bridging gaps between business and technical teams.
• Leaders don’t need to be tech experts but must provide context for data questions.
• Board-level tech expertise is critically lacking.
• Treating AI as only a technical upgrade will fail; cultural and human factors are decisive.
• Future success with AI depends on foundational work in data, governance, and team training.
• Rapid advancement in AI will separate proactive companies from those left behind.