Most data teams do not have an AI problem yet. They have an operating model problem.

Mike Doll, VP of Data at Guitar Center, joins The Tech Trek to talk about why analytics teams often become reactive ticket factories, and what it takes to turn data into a true business partnership.

As companies push harder into AI, automation, and faster decision making, the foundation matters more than ever. If the data team is buried in scattered requests, unclear priorities, and dashboard maintenance, AI will not magically fix the problem. It may only expose it faster.

Mike shares how modern data teams can rethink intake, structure analytics partnerships, separate quick BI needs from deeper analytical work, and create a more consultative model that helps the business answer harder questions.

Key Takeaways

• AI will not fix a broken data operating model. Teams still need clear intake, trusted data, business context, and a better way to prioritize work.

• Data teams become ticket factories when every request is treated the same and stakeholders do not understand what happens after they ask for help.

• BI and analytics serve different needs. Quick reporting should be fast and reliable, while deeper analytics requires judgment, framing, and business partnership.

• Self service only works when the data foundation is strong. Without that foundation, it can create more confusion instead of more speed.

• The future of analytics is not just faster answers. It is better questions, stronger context, and data teams that understand how the business actually operates.

Timestamped Highlights

00:41 Mike explains his role leading Guitar Center’s central data organization, including data engineering, analytics, BI, data science, and data strategy.

02:09 How data teams become ticket factories, and why unstructured requests can turn analytics into a black box for the business.

05:29 Why analytics delivery is different from software delivery, and why data teams need closer alignment with business leaders.

07:28 Where self service helps, where it breaks down, and why simple questions need a different model than complex business problems.

09:47 Mike explains the consulting model for analytics teams, with dedicated business partners, stronger dialogue, and shared value creation.

15:35 How AI is changing quick BI workflows, and why harder analytics questions still require human judgment and problem framing.

18:00 How Mike started shifting Guitar Center away from reactive ticket taking by improving intake, visibility, communication, and trust.

Line Worth Remembering

“The value that analytics teams can bring is answering those hard questions.”

Practical Moves

For data leaders trying to move beyond reactive analytics, Mike’s advice is to start with the biggest points of friction.

That might mean creating a clearer intake process, giving stakeholders visibility into work, assigning dedicated analytics partners to key business areas, or rebuilding trust through fast but meaningful wins.

The point is not to add process for the sake of process. The point is to create a data function that can move quickly without losing context, accountability, or connection to business value.

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