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The age of analytics and illiquid assets: how can data help?

15 December 2020

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Businesses have access to huge amounts of data these days – but not all of them are making the most of it. Experts discussed how to navigate complexity during Intertrust Group’s recent webinar at the ALFI PE & RE conference.

Increasingly data is seen as a tool to inform decisions among the investment community but, currently, sources of good data tend to be fragmented and difficult to manage. Edwin Chan, Director Business Development at Intertrust Group, recently moderated a webinar on this topic at the ALFI PE & RE Conference on 1 December. Referring to data as “the new oil”, he reflected that “just like oil, gathering and storing it are not the useful part… it’s what we do with it that’s important. The need to provide actionable data is paramount to the success of investing in illiquid assets.” The discussion went on to cover the use of data from a General Partner’s point of view and the best ways to focus data spend.

For Andrew Hampshire, COO and CTO at Gresham House PLC, data helps investment teams to focus their time better on the opportunities that best fit their investment criteria – particularly when identifying early stage businesses with the most potential: “It’s about freeing up as much time as possible so people working in those areas can focus on those areas that deliver most value.”

One consequence of using data at the top of the funnel in this way is also to generate more opportunities – to catch more potential gems. “Without data on your potential universe of investments,” says Mr Hampshire, “you’re limited to a smaller subset of deals – usually the deals you’re introduced to through your relationships and network or those in a process. If you add data, analytics and tools into the mix then suddenly the potential universe of business you can look at is greatly increased. Using data means you can use your time more smartly when building the initial funnel.”

Keeping track

But how do firms start using data: what are the priorities they should focus on when setting up a data pool of their own? Arnaud Bon, Director at Deloitte, observes that a rather functional or technological approach to data has given way to a more focused approach. He says: “First and foremost you need to know what data you need and what KPIs are most important. Then it’s a matter of organising your data properly so you can best manage and – above all – exploit the data lake you have created.”

Gresham House’s Mr Hampshire agrees. What’s important is to give your data context, he says. Investors have relatively simple and straight forward answers to questions – for example, what effect will any portfolio of investments have on the environment or society? Or, how could the governance of the target business be improved and what are we doing it about it? Answering these questions, however, can require a lot of data points. “Your value-add from a data perspective,” he says, “is being able to combine and utilise the different data points you have to present something meaningful to investors, something simple to understand and in context.”

Collecting the correct data, however, is made more difficult by a general lack of standardisation in the industry. Deloitte’s Mr Bon argues that “the source of data from one investment to another can be different and they may require ETL (Extraction/ Transform/Load) solutions to ensure data harmonisation.”

The starting point for Benjamin Lamping, Founder at Reframe Capital, is to identify precisely what the problem is that you are trying to solve with data. “You can have an entire data lake but without any application in your business. It absolutely begins with that fundamental question: ‘Does the data present a USP for your business in some way?’ That informs not only how you develop your data platform but determines whether you develop internal resources or rely on third parties.”

Expert support

Outsourcing is actually a huge issue for firms. Where once data scientists and analysts would have gone straight into investment banks, these days they are increasingly setting up their own FinTech companies. According to Mr Lamping, this is creating a “democratisation” of data science: “It makes the tools that were previously available only to those with the deepest pockets available to a wider audience so there are many options to use third party vendors for the data solutions you need.”

But outsourcing does bring challenges. Most firms will employ a variety of vendors for their data needs which makes it hard to integrate the data into a centralised resource. It’s a dilemma Mr Lamping solves like this: “If data presents a USP for your business then there is an argument of building a data capability internally. However, in all other cases, all the tools you need for leveraging data are there through third parties.”

Despite data’s huge potential, Mr Bon reflects that “A lot of players are in the questioning phase rather than the implementation phase… Many fund managers continue to sit on a pile of data which they only exploit to a very small extent. Much more value could be extracted if only the data could be consolidated… but we’re not there yet.”

At the end of the day, choosing a data strategy is a commercial decision that should be aimed at improving the bottom line one way or the other. Mr Hampshire offers some simple advice for calculating the value of any data solution: “If a deal is worth ‘x’ to you, work back from that to calculate how much you can afford to spend to fund that deal. What will be the return on income?” Or, in the words of Mr Bon: “It’s not rocket science”!