ANALYTICS
FINDINGS & INSIGHTS:
DATA ANALYTICS: DO THE OPPORTUNITIES OUTWEIGH THE CHALLENGES?
9 minute read time
KEY FINDINGS
79% of all surveyed respondents describe their attitude towards advances in data analytics and its impact on the relationship between general partners and limited partners as positive or somewhat positive
83% of all surveyed firms cite the need for continuous investment in next-generation technology as a main challenge
ANALYTICS
FINDINGS & INSIGHTS:
DATA ANALYTICS: DO THE OPPORTUNITIES OUTWEIGH THE CHALLENGES?
9 minute read time
KEY FINDINGS
79% of all surveyed respondents describe their attitude towards advances in data analytics and its impact on the relationship between general partners and limited partners as positive or somewhat positive
83% of all surveyed firms cite the need for continuous investment in next-generation technology as a main challenge
Analytics—which already plays a role in dealmaking in private markets—looks set to become an increasingly important ingredient, especially among managers of larger funds.
Data analytics is the science of interrogating data to gain insights. At one level, this can be as simple as using visualisations to bring raw data to life. But sophisticated data analytics goes further, revealing why things have happened, predicting what might happen next and even suggesting a course of action.
Not surprisingly, the predictive aspects of data analytics are of particular interest to participants in private markets. Our survey shows that three-quarters of firms agree that data analytics can help to create a model to predict deal prospects. A similar proportion says that data analytics can produce detailed historical financial performance.
“Potential revenue streams are uncovered when we use advanced analytics,” says the CEO of a US-based multi-asset firm with an average target fund size between €250 million and €500 million. “We know the company’s operations and productivities. Ascertaining the best revenue streams depends on data to a great extent.”
From an investment / deal standpoint, how do fund managers think data analytics can support valuation efforts?
SOURCE: Aztec Group & Acuris Report – Differentiation Through Data (Nov. 2022)
CREATING A MODEL TO PREDICT DEAL PROSPECTS
PRODUCING DETAILED HISTORICAL FINANCIAL PERFORMANCE
AVOIDING BIASES
PROVIDING REAL TIME MULTIPLES
Better relationships through analytics
It is reasonable to assume that insights derived from data analytics can help firms to make more informed investment decisions. They can also be transformative in a wider sense—particularly when it comes to attracting and retaining investors. Sharing insights and data improves transparency and helps to satisfy investors’ growing demands for information.
This is reflected in our survey: 79% of respondents describe their attitude towards advances in data analytics and its impact on the relationship between general partners and limited partners as positive or somewhat positive. For firms with average target fund sizes of more than €1 billion, this rises to 88%.
But there is a flip side. Respondents representing smaller average fund sizes are not quite as convinced, with 52% commonly saying that they are “somewhat positive” regarding advances in data analytics. Among the different sized firms in our survey, they are the most cautious (32%), citing concerns around things like keeping up with investor demands for data.
This point is taken up by the CFO of a US-based firm with an average target fund size of less than €250 million: “The main challenge is dealing with investor expectations. They want more transparency in financial reporting, even though we already provide them with comprehensive reports.”
How many respondents describe their attitude towards advances in data analytics and its impact on the relationship between general partners and limited partners as positive or somewhat positive?
Average target fund size €1bn +
Average target fund size €500m – €1bn
Average target fund size €250m – €500m
Average target fund size <€250m
SOURCE: Aztec Group & Acuris Report – Differentiation Through Data (Nov. 2022)
"When sourcing data, the most important challenge is the lack of reliable information... Although providers claim that the data has been verified, we often find that it is unreliable.”
Partner of a Germany-based firm with an average target fund size less than €250 million
Data is only useful if sources are trustworthy
While some firms are making advances in their use of data, they are also conscious of the risks involved in finding trustworthy data, especially from outside sources.
“When sourcing data, the most important challenge is the lack of reliable information,” says the partner of a firm with an average target fund size of less than €250 million based in Germany. “Although providers claim that the data has been verified, we often find that it is unreliable.”
Indeed, 60% of all investment firms say that the reliability, completeness and freshness of data and its sources is a top challenge.
Data handling is also highlighted as a challenge. Just over half of respondents argue that technological limitations in holding and extracting data are a challenge, while the same proportion points to difficulty in aggregating data from fragmented infrastructure to create a single, centralised data source.
As the partner of one UK-based private equity firm with an average target fund size of less than €250 million points out, there are limitations when using technology to extract data: “When it is unstructured, we cannot use it without further sorting and aligning the information.”
Which challenges do fund managers face when sourcing data?
Surveyed managers were asked to identify their top three challenges and rank in order of importance.
SOURCE: Aztec Group & Acuris Report – Differentiation Through Data (Nov. 2022)
RELIABILITY, COMPLETENESS AND FRESHNESS OF DATA SOURCES
TECHNOLOGY LIMITATIONS TO HOLD AND EXTRACT DATA
DIFFICULTY AGGREGATING ACROSS FRAGMENTED INFRASTRUCTURE TO CREATE SINGLE, CENTRALISED SOURCE
UNSTRUCTURED DATA SOURCES
OBTAINING REAL-TIME, HIGH QUALITY DATA
GETTING CONSENSUS FROM INTERNAL STAKEHOLDERS
LACK OF FLEXIBILITY AMONG DATA VENDORS
LACK OF SKILLED STAFF
Inaccurate and incomplete data are a major obstacle
Respondents are mixed on what they consider to be the main barriers to extracting meaningful insight from their data. The top concern (according to 55%) is the issue of inaccurate and/or incomplete data.
“The quality of data in the market is not always top-notch,” observes the senior managing director of a private debt firm based in the US with an average target fund size between €500 million and €1 billion. “We have to analyse the quality of data once we receive it. We need to check if the format and the records match our expectations.”
Data complexity is also highlighted as a barrier, per 47% of respondents. The challenge is managing often disparate sources of data. As the partner of a private equity firm based in Sweden with an average target fund size between €250 million and €500 million points out, given the amount of unstructured data that comes through, the firm must spend time sorting through it all for it to be practically applicable—"Otherwise, the information will be stored without any use.”
Effective analytics comes with a cost
While data quality is a cause for concern among many fund managers, others are equally worried about the size of the task involved in building advanced analytics into their firm—and the consequences if they fail to do so.
“For us, one issue is scale,” says Ross Waide, finance and operations partner at venture capital (VC) firm Stride. “Our portfolio includes an e-commerce/delivery company and I receive a board information pack once every two months from them. But a company like this handles ten million data points via machine learning and AI in the background, which most people won’t fully understand. How am I going to best capture and enrich all that information? The key is to use the company’s narrative plus data and most systems don’t capture both very well. In some cases, you might be better just to read the information pack again because, without it, you lose the history.”
The main challenge is seen as the need for continuous investment in next-generation technology. This is cited by 83% of respondents, with 39% citing it as the single most challenging aspect.
On a related note, respondents also have strong views on the costs associated with upskilling: “It is not only the technology on which we will spend our dollars,” notes the managing director of a US-based multi-asset fund with an average target fund size exceeding €1 billion. “We have to think about training our employees so that they can use the new systems in a favourable manner.”
"Meeting investor demand for data is tough... Investors ask for information across the value chain and also specific information about third-party providers and associates.”
CFO of a private debt firm based in France with an average target fund size between €500 million and €1 billion
Investors, cybersecurity and human error pile on the pressure
The second most selected challenge with being a data-driven organisation is that of satisfying increasing investor demand for data, cited by 74% as a top-three challenge. As noted earlier, matching investor expectations is easier said than done.
“Meeting investor demand for data is tough,” confirms the CFO of a private debt firm based in France with an average target fund size between €500 million and €1 billion. “Investors ask for information across the value chain and also specific information about third-party providers and associates.”
Cybersecurity is recognised as a challenge, but only 8% rank this as a number-one concern. This suggests that cybersecurity is not getting the attention it deserves. The fact is that sharing data with third parties, such as investors, is a risk multiplier and requires careful management—a point emphasised by the managing director of a private debt firm based in the US with an average target fund size between €250 million and €500 million: “If investors are not careful, we could be dealing with cybersecurity challenges, and this will affect our reputation.”
Human factors are another area of concern. Half of respondents say the loss of human interaction is a challenge in a data-driven organisation, while 46% think overreliance on data and removal of instinct is a drawback.
“Machines cannot read minds,” says the CFO of a multi-asset fund based in the US, with an average target fund size exceeding €1 billion. “People can identify whether someone is not being honest or forthcoming. This instinct is lost when we become a completely data-driven unit.”
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