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Data-driven Dealmaking: Debunking 3 Major Myths Holding Your Firm Back

Debunk data myths and learn about the next stage of data-driven dealmaking

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August 29, 2022

The pandemic gave dealmakers a reminder of what it takes to survive during an economic downturn. No longer able to depend solely on intermediary deals, many firms turned to direct sourcing. Along with this shift came a newfound need for data to help teams proactively identify and close the right opportunities.

But just as these direct sourcing engines started humming, dealmakers experienced the largest deal surge in recent history, giving way to several data misconceptions and bad habits that limit the success of direct sourcing efforts. Flooded with inbound opportunities, many firms had to put direct sourcing on the back-burner. Suddenly, data-driven decision making became less about tuning investment theses and unearthing proprietary deals, and more about vetting inbound opportunities as quickly and efficiently as possible.

But the recent market slowdown is leading firms everywhere to once again reevaluate their sourcing strategies and think about what it truly means to be "data-driven." With more than $2 trillion in dry powder chasing investments, firms must prove to their LPs that last year's deal boom wasn't just luck. They must come up with creative and strategic ways to bridge the widening valuation gap and take advantage of declining multiples.

Most importantly, dealmakers have realized that harnessing the latest data and technology to create a sustainable and proprietary advantage is the only way to stay competitive and safeguard future success in an increasingly volatile market. "We knew that building a proprietary deal machine would majorly differentiate our firm, not just to our LPs, but also to our current and potential portfolio companies," says Colin Raws, Boathouse Capital's Partner, Head of Business Development and Investor Relations.

The first step is to break the bad habits and shed light on the untruths that have taken root over the last several years. In this post, we're debunking these data myths and giving firms a head start on embracing the next evolution of data-driven dealmaking.

Myth #1: More Data Is Always Better

The easiest and most logical first step for any firm looking to become data-driven is to start buying and compiling data. But as Facebook's former VP of Engineering and Infrastructure Jay Parikh once famously said, "Big data really is about having insights and making an impact on your business. If you aren't taking advantage of the data you're collecting, then you just have a pile of data, you don't have big data."

Data quickly becomes a commodity without a clear strategy and plan in place. Taking full advantage of available information requires firms to step back and think about the purpose it will serve and how it will be used. This should be done before making a land grab.

To avoid spending time and money stockpiling low value data, we recommend dealmakers evaluate each source and the information it provides using these five key attributes:

Myth #2: Artificial Intelligence (AI) Is the Answer

From creating augmented realities to enabling cures for diseases, science is conquering more impressive feats each day. It's easy to assume that feeding AI-enabled technology as much data as possible will result in ground-breaking insights. However, machines can't tell whether the data they're receiving is accurate, and they have a long way to go before they're able to make contextual and reliable decisions for people. 

For example, imagine your firm is searching for add-on acquisitions for a platform company in the nanotech space. You download a lead list from your data service provider, and it comes with the following insights:

  1. 10% of the companies on this lead list start with the letter T.
  2. 5% of the companies on this lead list are in the nanotechnology industry.

As a human in this scenario, it's easy to see that the second insight is more relevant than the first. But they're of equal value to AI "unless it's been programmed to "think" otherwise.

Many firms get caught up in the promise of AI, but Sourcescrub CTO Jon Dodson warns against looking for a "quick fix" by simply buying technology that promises AI and machine learning functionality. "It's not magic, and there isn't generally available AI that will solve everyone's problems," he says. "Data is error-prone, and eventually everything has to be supervised to ensure data quality and accuracy." 

Achieving the next level of data-driven dealmaking requires firms to invest in people as well as technology. This includes choosing data platforms and services that harness the best of humans and technology by involving knowledge workers in the data validation process. It also means knowing when it's time to hire data analysts, engineers, and scientists to take your insights to the next level

Myth #3: Out-of-the-Box Reports Are Enough

You have to learn how to walk before you can run, as they say. And the pre-built reports and out-of-the-box dashboards that come with most data platforms and CRM tools area great way to start understanding data and using it to help make basic decisions.

However, looking at the same high-level analytics as everybody else will only get you so far. These insights are just the tip of the iceberg. Firms that want to use data to proactively find and close more of the right deals faster will need to go much deeper than basic pipeline forecasts and team productivity metrics. 

The true value of data lies in its ability to yield proprietary insights that create competitive advantage. This requires connecting data from multiple sources including your CRM, private company intelligence platform, third-party marketplaces, and more to build models, frameworks, and algorithms that no one else has.

The goal is to pinpoint the types of deals that are most likely to close and generate high IRR for your firm, and then accurately predict which companies fit the mold as early in their life cycles as possible. Once you're able to generate proprietary insights that help your firm win more of the right deals, you can begin to develop them for portfolio companies as incentive for these businesses to choose your firm over competitors.

For example, a leading private equity growth firm decided to generate exclusive market intelligence to pinpoint companies that align with its portfolio companies' add-on strategies. By combining data across several sources and leveraging a small team of data scientists, the firm was able to develop a data model that surfaces highly relevant opportunities that would otherwise remain hidden. This proprietary approach to sourcing has resulted in a 3x increase in deal volume.

Data-driven 2.0

The next evolution of data-driven dealmaking is upon us as the market slows, dry powder abounds, and LPs expect firms to systematically recreate last year's success. Dealmakers must reconsider the data technology they use, the sourcing strategies they have in place, and how they will harness insights to create sustainable advantage.

Being purposeful about the data your firm collects, leveraging the best of humans and AI together, and developing proprietary data resources are non-negotiables in this new era. What other data myths and bad habits could be holding your firm back from outpacing the competition?

To figure out exactly where your firm stands when it comes to data-driven deal sourcing and how to continue pushing forward, download this guide, Building a Proprietary Advantage in Dealmaking: A 3-Stage Maturity Model.