The data revolution waiting to happen
20 Aug 2025
Justin Partington of IQ-EQ speaks to Zarah Choudhary about the rising role of predictive analytics, and why it is becoming essential for firms seeking to evolve and remain competitive
Image: IQ-EQ
AI is becoming deeply embedded across financial markets – not just to automate operations – but to help fund managers forecast outcomes, manage risks and shape investment strategy in real time. While public markets have been quicker to adopt technology, according to Justin Partington, global head of fund and asset managers at IQ-EQ, private markets have been traditionally slower to embrace technology.
He explains: “It’s partly because in private markets you have a real focus on people processes. It tends to have low volume, high complexity transactions. Traditionally, firms have not applied as much technology because processes and outputs have been a bit more bespoke and each firm dealt with it differently.”
He further expands on how each deal is unique in private markets which is why it is hard to build reusable models. In the past 18-24 months, the larger private markets players have been investing in dedicated digital operational teams inside the management company. These teams are then deployed into each portfolio company to carry out the digital transformation by setting up data platforms and applying predictive analytics and business intelligence tools.
Partington believes it is most likely going to trickle down to the smaller fund managers. “I believe that over time, as predictive analytics becomes more efficient and better understood, it will trickle down to smaller and mid-sized managers as well. These managers are still competing with the big players for investor capital, and we’re already seeing that show up in fundraising. I do think adoption will gradually spread across the broader market.”
Beyond spreadsheets and PDFs
According to Partington, the biggest firms in private markets are leading the charge on digital transformation – not just because of their vision, but because they have the scale and resources to make it happen. Larger fund managers have built global teams in locations like India, giving them access to specialised tech talent.
“In terms of technology, what I’ve seen is that some of the large players have brought in experts in data and predictive analytics from other industries – particularly the tech sector – and introduced them into private markets to help cross-fertilise the environment with the knowledge they bring from elsewhere.”
He adds on to say that the investment is mostly made by the bigger firms because they have the scale and complexity to justify the outlay.
The two sides of predictive performance
Partington believes there are two buckets he would put the benefits of predictive analytics in – portfolio or asset level, and general partner (GP) or management company level.
“Predictive analytics can help both the manager and the portfolio company forecast performance over the next 6 to 12 months, and make adjustments in real time. If they see revenue targets aren’t likely to be met, they might expand the sales team or develop new products. If costs are set to rise, they might look at outsourcing or investing in AI to drive efficiency. It’s a powerful way to take early action and steer the business toward its goals,” explains Partington.
Partington gives an example of how IQ-EQ also uses Predictive Analytics as a portfolio company. “We’re a private equity-backed portfolio company, and the analytics we have around revenue, costs, and forecasts are excellent – really helpful for us to help manage the business.”
He then explains the usage of Predictive Analytics on the GP side. He says that their clients are primarily focused on two areas when it comes to predictive analytics. The first is cash flow forecasting – understanding how much cash they will receive from distributions and how much they need to deploy into investments, helping them manage liquidity. The second is fee forecasting, where managers predict their future income from management and performance fees. Both are key to helping firms plan ahead and run their businesses more effectively.
IQ-EQ opening the door for smaller firms
One of the major concerns when it comes to Predictive Analytics is how it would reach the smaller firms who do not have the same scalability or resources. Partington highlights how IQ-EQ has taken the initiative to help them out.
“We see some of those clients struggling to find either the talent or the budget to invest in digital AI technology, data platforms, and teams of data engineers. In our case, we’ve got over 35 AI data engineers based in India. We leverage our modern platform that’s already scaled and continues to grow and evolve. That’s a skill set not every manager has – and it’s not something that grows on its own. As an administrator, we can make those capabilities available. Some clients come to us and, effectively, we can extend – or rent – that capability to them, whether that’s people or data capacity. The other side of it is data infrastructure.”
He also points to IQ-EQ’s Snowflake-powered data platform, which clients can use in a data warehouse as a service (DWaaS) model. “We’ve got some clients who like to use our platform in a sort of data warehouse as a service model, because they don’t want to set up and maintain their own warehouse – so we can do that for them as a service. I think administrators are in the best position to provide best practice – or at least a view on best practice – to offer infrastructure and data capability,” he says.
What’s stopping the companies
When it comes to adopting new technology, Partington says that the mindset is no longer the issue. “Most people now recognise that data and AI are important, even if the value of predictive analytics is not always fully understood. It’s such a hot topic that on the GP client side, everyone knows they need to be aware of what’s going on. The real question they’re asking themselves is: how much should we be doing now, and how early do we want to be in this space?” says Partington.
There are three main challenges according to him. First, the data itself needs to be digitised and everything needs to be in the digital environment. Once the digitisation has taken place, it needs to be placed on a clean and structured platform. Even after the data is stored, there still needs to be strong data governance. Partington explains that even if the data uploaded starts out as high quality, the quality can deteriorate over time. Robust data governance is essential for reliable insights.
The last challenge is the tools. “Most people are familiar with data visualisation tools like Power BI, Tableau, or QlikView. But predictive analytics requires more advanced tools – we use Python and other tools for example – and there are fewer widely accepted models. At IQ-EQ, we use Preqin’s cash flow forecasting model, which is based on the Yale model framework. While the tools are out there, a lot of people in the industry are still deciding where they want to focus,” explains Partington.
The LPs are watching
The Limited Partners (LPs) are demanding more transparency. Partington explains “I’ve been in private markets for 25 years and in the early days we used to issue static documents for reporting. You’d get a PDF that summarised performance. What’s changed now is that investors are saying, ‘We’ll take your PDF and process it – but what we really want is the underlying data.’”
For LPs, data could mean reports in various formats or performance files added to a document repository. Partington explains that, in his view, LPs want the data for two key reasons. The first being, so that they can have their own assessment of performance and benchmark of the fund managers or the GPs. The second is so that they can look at metrics that matter to them, not just what is highlighted and shown to them.
“What we haven’t quite seen yet – but I think is coming – is LPs saying, ‘Don’t just give us a bit of data in a file. We want access to a portal where all the relevant data is located, and we want to run our own queries and build our own reports from that full dataset.’ That’s where we think the industry is heading – toward natural language query,” he notes
Traditionally, Partington says this kind of reporting has been done by analysts and data scientists compiling tables in Power BI or Tableau. He believes that this will change with natural language query (NLQ). Anyone will be able to write natural language instructions and generate predictive analytics or reports in real time.
He adds: “In my view, this shift is being driven by what we’re seeing in performance reporting. Everyone wants to be ‘top quartile,’ and when you read the reports, you find more managers claiming top-quartile performance than is mathematically possible. LPs are pushing for more transparency and independence. They want to make objective comparisons between GPs – and that’s why you see third-party providers like Preqin, Burgiss and CEPRES providing that kind of independent benchmarking.”
LPs are becoming increasingly sophisticated according to Partington and their users – whether they are in pension plans, sovereign wealth funds, or insurance companies – are under pressure from regulators or stakeholders to demonstrate oversight and report on performance.
“The whole industry is moving toward more transparent, self-serve, and independent data access,” analysis Partington.
There is also a possibility of a pushback from the GPs, estimates Partington. In his view, the largest players acquiring in private markets often come from traditional investment or mutual fund backgrounds. They are publicly listed and used to operating with a high degree of transparency and they also see this shift as a competitive advantage, especially when it comes to fundraising.
Partington suggests: “I think transparency will become part of the mindset at the top. And once mid-sized managers see the big players succeeding with that approach, they’ll start to adopt it too. I expect that the adoption curve will start with the largest firms and trickle down through the rest of the market.”
If you don’t adapt, you lose more than data
Partington cautions that there are two key risks for the firms if they fail to evolve technologically.
“If the world I just described really does become a prerequisite for the biggest investors to invest, then the risk – clearly – is being less successful in future fundraising,” says Partington.
“It is not expected immediately but over a period of time, it could become harder to attract quality institutional capital.”
He adds that predictive analytics enables management to better run the business and improve returns – and not adopting it would be a missed opportunity. The firms that do not embrace it might see lower returns in the long run.
“The information helps you make better decisions and manage businesses more effectively. That’s where the real interest lies,” suggests Partington.
Looking ahead
When asked what a future ready private market firm looks like to Partington he emphasised four key factors. First, the firm needs a strong data platform - a solid foundation where the data can be digitised and integrated properly. Secondly, strong data governance to maintain the quality of the data.
“You also need a data engineering team capable of maintaining the data, building reports and data pipelines, developing data visualisations, and supporting all the downstream elements that ensure the data is being used effectively by the manager and their investors,” says Partington.
Lastly, Partington says GPs need to invest in digital capabilities or teams that can do the same job for the portfolio companies – similar to what he has seen larger players do.
“The whole industry is moving towards more transparency – but that also means more self-serve access to data and more independent data analysis,” concludes Partington.
He explains: “It’s partly because in private markets you have a real focus on people processes. It tends to have low volume, high complexity transactions. Traditionally, firms have not applied as much technology because processes and outputs have been a bit more bespoke and each firm dealt with it differently.”
He further expands on how each deal is unique in private markets which is why it is hard to build reusable models. In the past 18-24 months, the larger private markets players have been investing in dedicated digital operational teams inside the management company. These teams are then deployed into each portfolio company to carry out the digital transformation by setting up data platforms and applying predictive analytics and business intelligence tools.
Partington believes it is most likely going to trickle down to the smaller fund managers. “I believe that over time, as predictive analytics becomes more efficient and better understood, it will trickle down to smaller and mid-sized managers as well. These managers are still competing with the big players for investor capital, and we’re already seeing that show up in fundraising. I do think adoption will gradually spread across the broader market.”
Beyond spreadsheets and PDFs
According to Partington, the biggest firms in private markets are leading the charge on digital transformation – not just because of their vision, but because they have the scale and resources to make it happen. Larger fund managers have built global teams in locations like India, giving them access to specialised tech talent.
“In terms of technology, what I’ve seen is that some of the large players have brought in experts in data and predictive analytics from other industries – particularly the tech sector – and introduced them into private markets to help cross-fertilise the environment with the knowledge they bring from elsewhere.”
He adds on to say that the investment is mostly made by the bigger firms because they have the scale and complexity to justify the outlay.
The two sides of predictive performance
Partington believes there are two buckets he would put the benefits of predictive analytics in – portfolio or asset level, and general partner (GP) or management company level.
“Predictive analytics can help both the manager and the portfolio company forecast performance over the next 6 to 12 months, and make adjustments in real time. If they see revenue targets aren’t likely to be met, they might expand the sales team or develop new products. If costs are set to rise, they might look at outsourcing or investing in AI to drive efficiency. It’s a powerful way to take early action and steer the business toward its goals,” explains Partington.
Partington gives an example of how IQ-EQ also uses Predictive Analytics as a portfolio company. “We’re a private equity-backed portfolio company, and the analytics we have around revenue, costs, and forecasts are excellent – really helpful for us to help manage the business.”
He then explains the usage of Predictive Analytics on the GP side. He says that their clients are primarily focused on two areas when it comes to predictive analytics. The first is cash flow forecasting – understanding how much cash they will receive from distributions and how much they need to deploy into investments, helping them manage liquidity. The second is fee forecasting, where managers predict their future income from management and performance fees. Both are key to helping firms plan ahead and run their businesses more effectively.
IQ-EQ opening the door for smaller firms
One of the major concerns when it comes to Predictive Analytics is how it would reach the smaller firms who do not have the same scalability or resources. Partington highlights how IQ-EQ has taken the initiative to help them out.
“We see some of those clients struggling to find either the talent or the budget to invest in digital AI technology, data platforms, and teams of data engineers. In our case, we’ve got over 35 AI data engineers based in India. We leverage our modern platform that’s already scaled and continues to grow and evolve. That’s a skill set not every manager has – and it’s not something that grows on its own. As an administrator, we can make those capabilities available. Some clients come to us and, effectively, we can extend – or rent – that capability to them, whether that’s people or data capacity. The other side of it is data infrastructure.”
He also points to IQ-EQ’s Snowflake-powered data platform, which clients can use in a data warehouse as a service (DWaaS) model. “We’ve got some clients who like to use our platform in a sort of data warehouse as a service model, because they don’t want to set up and maintain their own warehouse – so we can do that for them as a service. I think administrators are in the best position to provide best practice – or at least a view on best practice – to offer infrastructure and data capability,” he says.
What’s stopping the companies
When it comes to adopting new technology, Partington says that the mindset is no longer the issue. “Most people now recognise that data and AI are important, even if the value of predictive analytics is not always fully understood. It’s such a hot topic that on the GP client side, everyone knows they need to be aware of what’s going on. The real question they’re asking themselves is: how much should we be doing now, and how early do we want to be in this space?” says Partington.
There are three main challenges according to him. First, the data itself needs to be digitised and everything needs to be in the digital environment. Once the digitisation has taken place, it needs to be placed on a clean and structured platform. Even after the data is stored, there still needs to be strong data governance. Partington explains that even if the data uploaded starts out as high quality, the quality can deteriorate over time. Robust data governance is essential for reliable insights.
The last challenge is the tools. “Most people are familiar with data visualisation tools like Power BI, Tableau, or QlikView. But predictive analytics requires more advanced tools – we use Python and other tools for example – and there are fewer widely accepted models. At IQ-EQ, we use Preqin’s cash flow forecasting model, which is based on the Yale model framework. While the tools are out there, a lot of people in the industry are still deciding where they want to focus,” explains Partington.
The LPs are watching
The Limited Partners (LPs) are demanding more transparency. Partington explains “I’ve been in private markets for 25 years and in the early days we used to issue static documents for reporting. You’d get a PDF that summarised performance. What’s changed now is that investors are saying, ‘We’ll take your PDF and process it – but what we really want is the underlying data.’”
For LPs, data could mean reports in various formats or performance files added to a document repository. Partington explains that, in his view, LPs want the data for two key reasons. The first being, so that they can have their own assessment of performance and benchmark of the fund managers or the GPs. The second is so that they can look at metrics that matter to them, not just what is highlighted and shown to them.
“What we haven’t quite seen yet – but I think is coming – is LPs saying, ‘Don’t just give us a bit of data in a file. We want access to a portal where all the relevant data is located, and we want to run our own queries and build our own reports from that full dataset.’ That’s where we think the industry is heading – toward natural language query,” he notes
Traditionally, Partington says this kind of reporting has been done by analysts and data scientists compiling tables in Power BI or Tableau. He believes that this will change with natural language query (NLQ). Anyone will be able to write natural language instructions and generate predictive analytics or reports in real time.
He adds: “In my view, this shift is being driven by what we’re seeing in performance reporting. Everyone wants to be ‘top quartile,’ and when you read the reports, you find more managers claiming top-quartile performance than is mathematically possible. LPs are pushing for more transparency and independence. They want to make objective comparisons between GPs – and that’s why you see third-party providers like Preqin, Burgiss and CEPRES providing that kind of independent benchmarking.”
LPs are becoming increasingly sophisticated according to Partington and their users – whether they are in pension plans, sovereign wealth funds, or insurance companies – are under pressure from regulators or stakeholders to demonstrate oversight and report on performance.
“The whole industry is moving toward more transparent, self-serve, and independent data access,” analysis Partington.
There is also a possibility of a pushback from the GPs, estimates Partington. In his view, the largest players acquiring in private markets often come from traditional investment or mutual fund backgrounds. They are publicly listed and used to operating with a high degree of transparency and they also see this shift as a competitive advantage, especially when it comes to fundraising.
Partington suggests: “I think transparency will become part of the mindset at the top. And once mid-sized managers see the big players succeeding with that approach, they’ll start to adopt it too. I expect that the adoption curve will start with the largest firms and trickle down through the rest of the market.”
If you don’t adapt, you lose more than data
Partington cautions that there are two key risks for the firms if they fail to evolve technologically.
“If the world I just described really does become a prerequisite for the biggest investors to invest, then the risk – clearly – is being less successful in future fundraising,” says Partington.
“It is not expected immediately but over a period of time, it could become harder to attract quality institutional capital.”
He adds that predictive analytics enables management to better run the business and improve returns – and not adopting it would be a missed opportunity. The firms that do not embrace it might see lower returns in the long run.
“The information helps you make better decisions and manage businesses more effectively. That’s where the real interest lies,” suggests Partington.
Looking ahead
When asked what a future ready private market firm looks like to Partington he emphasised four key factors. First, the firm needs a strong data platform - a solid foundation where the data can be digitised and integrated properly. Secondly, strong data governance to maintain the quality of the data.
“You also need a data engineering team capable of maintaining the data, building reports and data pipelines, developing data visualisations, and supporting all the downstream elements that ensure the data is being used effectively by the manager and their investors,” says Partington.
Lastly, Partington says GPs need to invest in digital capabilities or teams that can do the same job for the portfolio companies – similar to what he has seen larger players do.
“The whole industry is moving towards more transparency – but that also means more self-serve access to data and more independent data analysis,” concludes Partington.
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