The importance of getting your data house in order

What are the biggest problems with data and reporting?

Whatever the challenge you are dealing with, it eventually becomes a data problem. We see people solve the small problems in the most operationally efficient manner. But they often also create silos in the process. Standardization helps you use data across your organization and look at it from unique perspectives and different levels of granularity. When you start taking your data problems seriously, you start to break down the silos.

And what about the quality of the data?

You must be proactive about data quality by checking it early before it percolates through the entire system. When we work with clients, a big focus is understanding what good data looks like.

We like to do this in a way that highlights lineage. If you see something wrong downstream, then you can swim upstream and find where it came from. Understanding why and where bad data came into the system lets you work to prevent it. You need to be proactive about data quality.

Are there any particular customer challenges you are focused on at the moment?

We are receiving numerous requests for NAV oversight. It’s fundamental to the business and to investments and, again, it comes down to the data. Where did it come from? What does it look like? Do we agree with the calculations?

Then, of course, everybody is trying to figure out how to leverage AI. There’s been a lot of noise. But AI is not just hype. Firms are experimenting with AI to see how the tools can generate extra value from their data or empower their staff. What everyone’s finding is that your data house has to be in order. If the models are trained on bad data, the outputs are bad; if you put garbage in you get garbage out.

Having the fundamental data correct is what allows AI to be a treasure trove.

Do you think we are at an inflection point in how data in private markets is used?

People can see an inflection point is coming. We are excited about it. I think some people are a little scared of it.

Investors invest in private markets to get returns, but they want to be able to re-invest – it’s not enough to just see the internal rate of return (IRR). LPs want the real numbers. GPs must be able to answer questions from their customers and regulators faster and in more detail.

We talk about how data is power. You can use the data accumulated over years to power AI models that will allow you to work faster, do better analysis, get better results, and eliminate some of the drudgery so you can focus on the things that matter.

What specific AI technology is exciting you right now?

It’s not about any particular model or training technology, because the tooling landscape is changing so fast. We look at fundamentals and foundations, then we experiment on top with new tools. Sometimes experiments pan out, sometimes not. That’s why you need humans, because this requires judgement.

The question then becomes: how can I use AI to supercharge my humans who have good judgement, experience, have seen the world, and can understand what is going on?

About
As Arcesium’s Field CTO, Matt leads Arcesium’s Forward Deployed Software Engineering and Client Success teams. His work to empower clients and simplify technical challenges stems from a 25-year career in financial technology, working with clients and software. Outside work, he enjoys books, bikes, and boards.

This article originally appeared in Private Equity Q2 2025: Preqin Quarterly Update.

_{This is a sponsored opinion by Arcesium. The views expressed are provided as of August 2025, do not constitute an endorsement, recommendation, or any other advice, and are subject to change. The following content does not necessarily reflect the views of BlackRock, Preqin, or any of its affiliates. Arcesium is not affiliated with Preqin. Preqin received compensation from Arcesium in exchange for publishing this content.}