This article is sponsored by Alter Domus.
Both GPs and LPs are now calling for better data to inform private equity decision-making. What is the problem with data today?
As the alternatives marketplace matures, demands for ‘better data’ have grown. Those demands are coming from both LPs and GPs, each with their own unique data needs. The market clearly has a need for ‘better data’, but what exactly does that mean?
Based on our discussions with many market participants, there is no clear consensus on what the right data solution looks like in daily practice. The right solution is in the eye of the beholder and is dependent on the unique needs of each investor.
Nonetheless, there are at least three key dimensions to the data challenge. The first is raw data. Then there is the ability to draw insights from that data, and to deliver that information so that decisions can be made. Any one dimension is not all that useful without the other two – all three dimensions combined are essential.
Some data challenges include data coming from disparate sources and in different formats. The data itself is often not digitised (unstructured), and not normalised or enriched in any meaningful way. The data is generally not delivered with unique identifying information so that it could be mapped to other sources.
And to top it off, data is often maintained in disparate systems, often spreadsheets, by different people, which poses a host of risks around accessibility, completeness, reliability, auditability, security and other factors.
This state of affairs is insufficient for a growing and increasingly demanding and sophisticated investor base.
What sort of data is the industry looking for?
Everyone wants ‘good data’ or ‘better data’, but what that looks like is often different for each investor and investment strategy. But there is generally a need for a single source of truth, data that can be connected to other data sets, and data that is easily accessible in a variety of user-defined formats. There is also the need to incorporate analytics on top of data, to allow for reporting to investors and internal decision-makers to enable the creation of insights to drive better decision-making.
Investors want data to support, among other things, risk-return analysis, investment portfolio and look-through analytics, cashflow forecasting, ESG measurement and portfolio aggregation. Historical information is critical to analyse performance trends, comparisons and correlations, and to build analytic models. But that is not enough.
The alternatives marketplace is not a one-size-fits-all market. The definition of what is ‘good’ becomes more nuanced and unique to an investor’s specific needs, strategy and investment philosophy. Those unique features need to be considered in addressing what the market is looking for.
What does an ideal data solution look like?
The ideal data solution is in the eye of the beholder, but there are some common themes.
The first is clear data dictionaries and cataloguing so that a ‘data inventory’ is available that describes, among other things, where and how data can be accessed and by whom, and what is missing. The second imperative is data integrity, which often requires dedicated teams checking the quality, timeliness and completeness of data sets and looking for ways to improve. Here you will see some AI or other automation to ensure quality and enhance operations, but human eyes will also be needed.
The third characteristic would be some system of unique identifiers for investment holdings at the granular level, to enable joining the dots across data environments and with relevant third-party sources.
Then comes tech. Technology needs run across the spectrum of the ideal data solution – from collection, to analytics, to delivery. Advances in cloud technology, and software design and architecture offer computational possibilities that were almost impracticable just a few years ago. Whatever data solution is built should be designed to be modular, lightweight and capable of incorporating future advances, which will keep coming. Desktop portals may continue to be relevant, but machine-to-machine delivery, with the ability to build customised solutions on-demand, will rapidly grow and likely define next-generation data solutions.
All of what we just discussed will be guided by an overarching industrial-strength data governance framework.
What steps should managers be taking today to get in good shape for this data transformation?
For some who have been making big investments and are well into their data transformation journey, an area of focus would include how to transition to an environment that embraces and adopts the best of what is available today while preserving what has been created. That is not an easy exercise, but at least those investors have a good grasp of their needs. For those who are earlier in the process, their focus will be about defining their data needs, designing the ideal system and starting on the journey to put those designs in place.
Either way, steps could be taken today. This is not a cheap process, and there are no quick fixes, so there is a need for data governance and organisational commitment. Then there is embracing gradualism to the transformation – as an ongoing exercise, the focus should be on milestones that deliver constant tangible benefits. Change management is also important, with everyone’s key needs and objectives recognised. Given how much is at stake with such a transformation, proper oversight, guidance and performance measurement should be prioritised.
How fast do you expect the industry to progress on its data journey? Where might we be in one, two or five years’ time?
The good news is that the right data discussions are happening now – what is needed, how can it be done, what technology is available, who is going to help, and how to build solutions that are sustainable. These discussions, which were often relegated to the backburner, are front and centre today.
What comes next is the building phase. Within the next two or three years many will be well on their way towards implementing a data strategy that covers many of the topics we discussed today. I expect many will have retooled the way they collect, store, view, analyse and report their data. Importantly, their solutions will suit their unique needs as they continue to expand their alternatives investment capabilities. It is a very exciting time, and we at Alter Domus are thrilled to be part of it.
Gus Harris is director and global head of the data and analytics products group for alternatives at Alter Domus