Goldman Sachs: The data differentiator

Private equity firms that are not data fluent will lose their edge within the next decade, says Luke Flemmer, managing director at Goldman Sachs.

This article is sponsored by Goldman Sachs

Luke Flemmer

What is driving private equity’s enhanced focus on data analytics?

There is currently a real and sustained focus on increasing the use of data and data analytics in the private equity industry. That is being driven by a number of factors. The asset class itself is becoming more competitive. Private equity is attracting record inflows of capital. More and more players are coming into the market and, as competition intensifies, the stakes become higher.

The underlying investors are also demanding change. We live in an increasingly data rich and analysis driven world, and limited partners have their own extended stakeholder networks to satisfy. LPs are therefore increasingly expecting greater levels of transparency and granular detail around investment activities, just as they do for their public market exposures. An example of this, which is extremely topical, is the focus on environmental, social and governance considerations. There are a number of regulatory mandates emerging around ESG, as well as a lot of voluntary participation. Data analytics is necessary to provide an appropriate level of reporting that conforms to regulation and meets stakeholder demands.

Risk analysis is another area where the use of data analytics is growing. Sophisticated private equity LPs are now demanding the same kinds of risk decomposition and risk correlation that they expect to see in their listed investments. Private equity is starting to face some of the same pressure to provide transparency that the public markets have faced over the years and that is driving enhanced use of data analytics in the asset class.

Is data analytics equally important for all private markets investment strategies?

Some investment strategies are intrinsically more data driven than others. If you look at real estate as a segment, for example, desirability of an investment in a commercial property may be influenced by consideration of local industry dynamics, customer segmentation and proximity to local attractions as well as climate risk and other complexity around ESG. That is a sector that has been at the forefront from a data analytics perspective.

On the other hand, you might reasonably think that the buyout segment is less data reliant. But, interestingly buyout managers are starting to derive benefit from the data available within their portfolio companies in terms of their own operating activities and in order to gain a better understanding of a sector. That then provides them with a competitive advantage throughout the investment life cycle, from origination and transaction through to value- add and exit.

The use of data differs across private markets asset classes. In some cases, it is more about diligence and understanding risk exposures, in others it is more about the operational excellence of the asset itself. Overall, however, the ambient level of data analysis is definitely trending upwards.

What are the biggest challenges firms face in developing data analytics capabilities?

The key challenges that firms face can broadly be divided into two buckets. There are the technical and operational challenges, and then the cultural and execution challenges. The technical challenges are always around data integrity. The minute firms start trying to consume data to drive decision making, the onus is on ensuring that data is accurate and appropriately timely for the use case. It is like an iceberg. The segment above the waterline is the fun part, where you are actually using the data, but the heavy lifting goes on underneath. How do you bring the data in? How do you normalise it? How do you cleanse and validate it? Those are the entry-level technical issues that any business faces when starting out on a data journey. It does not matter if you are talking about a private equity firm or a consumer packaged goods company, the challenges are the same.

The organisational cultural challenges emerge because embracing data analytics means bringing in a new mix of skills. Investment managers tend to have a defined culture built around a certain set of commercial behaviours that have made that franchise successful. Yet the expertise that managers are going to be looking for if they want to build a data analytics platform are going to be quite different. That is going to require an increase in organisational flexibility, as investors and sales teams will need to learn to collaborate with software engineers and data scientists.

How competitive is that skills market and how can private equity firms make themselves attractive to top talent?

If managers want to recruit world-class data analysts and data scientists, they will need to create an environment where those people can thrive. Leaving compensation aside as a given, the nature of the work is a key determining factor for talented people deciding where they want to spend their time.

Private equity offers a very interesting problem space. The asset class provides a wealth of diverse information and, particularly if you are working at a firm running multiple strategies on a global basis, the complexity and exposure to information is vast. You could be dealing with building a bridge in South-East Asia, alongside an office building in Houston and a growth equity company in London. That creates fascinating opportunities to engage with data sources to deliver genuine insight. If the position is appropriately packaged and marketed to the right candidates, I think there is every chance private equity firms will be able to attract the best in the business.

How should firms go about easing that cultural transition?

You could liken it to an industrial-sized franchise that is chugging along, and then there is this much smaller team looking to be creative, to drive new insight and apply new techniques. There is more of a research and development culture involved, so it is important to be able to create an environment where the people responsible for that feel comfortable experimenting and playing around with ideas that might not be fully formed. That open-ended mandate of driving insight through data can be at odds with the rhythm of the rest of the organisation, which means it is important that new talent does not feel alienated.

What are the pros and cons of a buy versus a build approach to data analytics and which route are most private equity firms taking?

I think there is a mix. Everyone is buying data to some degree. Many firms are also buying a lot of the tooling around the data science. There are a number of very sophisticated platforms in the market and there is some great opensource software out there as well. It is a rich ecosystem. Some firms are also using consultants. All the big advisory firms have data analytics practices that will go in and help a business consume and drive data, and in many cases offer their own proprietary datasets as well. It is an end-to-end buy model.

However, if a firm sees data analytics as an area of innovation, then building may be the answer. Depending on the use case, a firm may or may not care if a vendor is selling the same product and service to their competitors. It may be perfectly adequate to buy the capabilities required to execute some areas of due diligence, for example. Yet if a firm sees data as a differentiator, then some degree of proprietary build will be necessary to gain that edge.

Are there skills existing team members should be developing alongside the data scientist hires?

Yes, firms should be developing the skill base of their existing teams. There are a lot of great educational materials out there. Talented software engineers, and particularly those with a mathematics background, are often very open to embracing new techniques and are making themselves strong contenders in this space. The team itself is something that can be built. Bringing in one or two individuals with the expertise and experience of doing this at scale can act as an accelerant, but there are clear advantages to working with existing team members in terms of culture, as these people are already integrated and trusted within the organisation.

To what extent do you think data analytics could play an even greater role in private equity in the future?

I think the secular trend is clear. In every dimension of commerce, everywhere in the world, we are seeing an increased focus on data and I do not see that trend going into reverse. Expectations around transparency and reporting, enhanced risk analysis and the ability to satisfy extended stakeholder mandates, including complex requirements around ESG, are all driving the momentum behind the use of data analytics.

Meanwhile, the asset class continues to become increasingly competitive. The ability to find and transact on differentiated deals is becoming more difficult. That requires insight and understanding based on data. The value creation process is becoming more demanding and so operators need to be canny about using data to boost performance. Even the timing and execution of an exit can be enhanced through data use.

We live in a world where there is only going to be more data and the demands on that data are going to grow. Firms that find themselves behind the curve when it comes to data proficiency will increasingly start to look like dinosaurs – they will become less attractive to investors. I do think this will be a real competitive dynamic. It will not happen overnight but within five to 10 years, firms that are not data-first and data fluent will no longer be able to compete.