How Collaborative, Agentic AI Turns Data Work into Live Decision-Making | Ep 46

Explore Data Neighbor free workshops on AI evaluation and agentic analytics (plus our live, hands on course): https://dataneighbor.com/

Learn more about Count:
https://count.co/

In this episode of the Data Neighbor Podcast, we sit down with Ollie Hughes, co founder and CEO of Count, to unpack why traditional BI often fails at the real job teams hire data for: making better decisions, faster.

If you care about the future of analytics workflows, this is a must watch.

Connect with the team (tell us YouTube sent you!):
Shane Butler: https://linkedin.openinapp.co/b02fe
Sravya Madipalli: https://linkedin.openinapp.co/9be8c
Hai Guan: https://linkedin.openinapp.co/4qi1r

Connect with Ollie:
  / hughesoliver  

In this episode, you’ll learn:
Why dashboards are not designed for collaborative decision making
What the “service trap” is and how it keeps data teams in reactive work
How operational clarity helps companies simplify what matters
Why a canvas based interface changes how teams reason with data
What collaborative analysis looks like in practice between analysts and stakeholders
How agile decision making can reduce time to insight and time to decision
How Count thinks about AI agents for analytics without black boxes
What skills will matter most for data teams as AI takes on more of the mechanical work

Chapters:
00:00 Introduction to Count and Data Challenges
04:06 Ollie's Journey in Data and Analytics
08:05 The Limitations of Traditional BI Tools
11:42 The Service Trap in Data Teams
23:12 Operational Clarity and Simplification in Data
26:07 Count's Unique Approach to Data Visualization
29:31 Collaborative Data Analysis
33:29 Agile Decision-Making in Data Teams
36:35 The Role of Data Teams in Business
40:48 Transforming Data Communication
44:29 Customer Feedback and Impact
47:27 Leveraging AI for Enhanced Data Analysis
55:47 Future Skills for Data Teams

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