CIO Insights on Data Monetization, Data Lakes, and AI

In this podcast, Blue Margin co-founder and CEO Brick Thompson hosts CIO John Manzanares. John is a seasoned IT leader and board member for PE-backed field services, construction, transportation, and logistics companies, as well as the Society for Information Management. He served as Executive VP and CIO of ITS Logistics, a PE-owned $900 million revenue transportation and logistics firm. Prior to that, John was VP and CIO of CoolSys, a PE-backed $500 million HVAC and refrigeration services company, and led as CIO of Daylight Transport, a $200 million transportation provider. With a master’s in computer science from USC, John brings over 30 years of corporate IT, private equity, and industrials experience.

First and foremost, as a CIO, you need to be a business leader, and technology is the tool you bring to the table to help solve business problems. If you’re not creating business value with a project, then why are you doing it? If you can’t understand what the business priorities are and speak in business terms, understanding the value of what the customers and shareholders need, you’re missing an opportunity to improve and drive technology to solve business problems.

CIO John Manzanares

The episode covers how a PE-backed mid-market commercial services company monetized its data, why data lakes are central to a buy-and-build strategy, how Microsoft Copilot is changing data reporting, how to navigate AI responsibly, and how to minimize business intelligence project delays. Watch on YouTube or listen on Spotify below.

How a PE-Backed Mid-Market Commercial Services Company Monetized Its Data

Within a buy-and-build strategy, PE partners need to plan ahead for how acquired companies’ data will be integrated. John advises thinking from a business perspective first: what are we doing, where are we heading, and what solution does that trajectory require? Building before that clarity is defined leads to rework.

While CIO of CoolSys, John created an IT roadmap that helped facilitate the company’s successful exit. Within 27 months, CoolSys acquired 14 businesses, posing significant integration challenges. To address them, CoolSys partnered with Blue Margin to build a data warehouse, management reports, and executive dashboards. These powered operational improvements and uncovered process inefficiencies that led to savings and EBITDA improvements of hundreds of thousands of dollars each year. Download the CoolSys case study to see the details and dashboards.

How Data Lakes Are Key to the Buy-and-Build Strategy

The shift towards using the data lake with some type of model on it, where there’s a data layer to help provide consistency and understanding, really plays into that speed to value that private equity is driving towards.

CIO John Manzanares

Data lakes are gaining ground as the centralized data repository for PE-sponsored companies because they offer a more agile, faster, and less expensive approach than traditional data warehouses. Unlike warehouses, which require defining a data schema before data can be stored and which hold highly structured historical data for specific purposes, data lakes allow schema definition after data storage and accommodate raw, unstructured data without predefined purposes. The tradeoff is query performance, but the speed to insight advantage is significant. By incorporating a semantic layer for consistency and clarity, data lakes enable rapid insights during the critical first 100 days of a holding period.

If I’m a PE owner and we bought this platform company and we’re adding acquisitions to it with an exit strategy of 3 to 5 years, do we have time to wait around for the huge effort that needs to go into that traditional data warehouse? What value can we get right now from looking at data in the data lake, and getting that data into the hands of business leaders to make quick decisions?

CIO John Manzanares

For more on the benefits of creating a data lake before a traditional data warehouse, including faster speed to insight, a testing ground for KPIs, and AI readiness, see our podcast: Why You Should Build a Data Lake.

How Microsoft Copilot Is Changing Data Reporting

I’m guessing that near-future tools will be able to look at data repositories, like a data lake, and enable natural language querying much better than we’ve had. I think Microsoft’s big investment in OpenAI and their commitment to putting Copilot in all of their tools may lead the charge here. And I bet we’ll see some LLM tools replace or become the scaffolding behind Q&A tools.

Brick Thompson, CEO, Blue Margin

Since this episode was recorded, Microsoft’s investment in AI has materialized exactly as described. Microsoft Fabric and Power BI Copilot are now generally available, giving executives the ability to ask natural language questions of their data and receive dashboard-level responses in seconds. John envisioned that tools like Copilot would pull data insights directly from Power BI into email digests and executive summaries, while analysts and regular users could still access granular filtered views. That capability exists today.

The foundation it requires is clean, structured, unified data. John’s framing remains accurate: the prerequisite for AI and machine learning in any organization is the data itself. Building a data lake now positions PE-backed companies to take advantage of these tools immediately rather than scrambling to catch up after the technology is already standard. The AI-Ready Data Platform work Blue Margin does is built specifically on this foundation.

Please note that Blue Margin’s views of Microsoft products are entirely our own. Although we are a Microsoft Partner, we are not compensated to promote Microsoft tools.

How to Navigate AI Responsibly

You talked about hallucinating, that’s making up data. That’s because these large language models are not necessarily building intelligence as much as they’re building this neural network of likely probabilities of what the next sentence is, or what the next information is. And where are they getting that data? They are feeding in massive amounts of information. And you know the internet: not everything out there is 100% correct. Some of that data is flawed, has wrong information, and is socially biased.

CIO John Manzanares

Brick and John discuss how to lead organizations thoughtfully through AI adoption, covering LLM awareness, code development risks, privacy considerations, corporate policy, and the need for employee education. Two specific risks in AI-assisted code development are security flaws and potential copyright issues, since these tools can write flawed code and draw from protected repositories. Privacy is a parallel concern: users should treat all information entered into AI tools as potentially shared, and should never input proprietary data without understanding the tool’s data handling policies.

I think you need to understand more about the tool to be able to effectively communicate the challenges of using it. Go in with your eyes open, understanding the risks and the challenges.

CIO John Manzanares

Companies should develop clear AI usage policies and invest in employee education before widespread adoption. As John observes: there is no evil in the technology. It is how it is used.

How to Minimize Business Intelligence Project Delays

This project is not something that you can just give to IT. You’re the business leader. We want to build a solution that meets your needs and expectations. You’re the expert here. How can I partner with you, so this is our project, not IT’s project?

CIO John Manzanares

IT and BI projects have earned a reputation for delays. John shares five practical approaches to avoiding them. The first is to start with the standouts: identify the areas of biggest business impact, the biggest pain points, the teams most eager to participate, and the functions seeking results. Starting there builds momentum and generates visible wins that justify continued investment.

Second, set clear and measurable expectations for business leaders before work begins. Specifying what will be required of them, including time commitments and attendance at steering meetings, transforms concerned onlookers into engaged executives who own the outcome alongside the technical team.

Third, show incremental improvements along the way. An agile approach that prioritizes speed to market and iterative progress beats a long build toward a single reveal. John’s advice: start small, build into it, and show progress continuously rather than perfection at the end. Adam Coffey, Tania DiCostanzo, Tracy Hockenberry, and Andy Scott all echo this approach from their own experience.

Fourth, appoint an experienced change management leader. This person rallies the team, handles internal communications, engages stakeholders, and builds genuine organizational excitement for the project rather than resistance to it.

If you would like to explore how Blue Margin can help your organization build the data foundation for faster insights and better decisions, contact our team here.