Success for private middle market dealmakers ultimately hinges on quality data.
To stay competitive, investors have to maintain a steady flow of deals. That means they have to:
- Find companies that fit their investment criteria
- Evaluate those companies’ strategic and financial operations to see if and how the deal would add value
But this is often much more difficult than it sounds. Private middle market data is hard to find, and the quality of what is available from company databases is often lacking. Large language models (LLMs) like ChatGPT and Midjourney can make the process even more challenging with inaccurate or outdated information.
To avoid these challenges, some firms with deep engineering expertise and resources opt to build their own proprietary M&A data infrastructure. This allows them to tailor a solution to their exact needs. Having exclusive data can also give firms a competitive edge.
So what exactly does it take to build your own M&A data ecosystem and how do you know if this path is right for your firm?
We dig into these questions — and more — below.
Where Most Prebuilt Solutions Fall Short
While there are a plethora of company databases and deal marketplaces available for purchase, most of them are not built specifically for M&A workflows.
For example, ZoomInfo offers a contact database with over 150M records, but information on company owners, founders, and principles — which are crucial for M&A conversations — is limited.
Company databases like Sourcescrub are limited in how granular their data can get because they rely on broad industry classifications and human processes. Pitchbook has a wealth of deal data for upper-middle market VC- and PE-backed companies, but it also has a massive blindspot when it comes to the middle market.
What Investors Can Gain from Building
If executed correctly, building proprietary systems can fill in these gaps and bring private middle market investors the exact data they need.
To continuously identify new targets, and to get to them before their competitors do, dealmakers need to be able to dig into niche markets that fit their investment thesis. By building a proprietary M&A data ecosystem, investors can zoom in on the areas that prebuilt solutions tend to overlook.
Having a unique in-house system also streamlines workflows for the team. Instead of having to maneuver multiple alternative data platforms to get the granularity they need, investor teams can rely on a single tool. That allows them to be more collaborative and agile, giving them a competitive edge. Reaching targets before the competition is crucial for winning deals.
Another benefit of building out a proprietary M&A data infrastructure is that investors can pitch themselves to limited partners (LPs) as users of differentiated sourcing. LPs want access to new pockets of their markets, which can yield high returns. Unique strategies help provide that.
LPs also want to back firms that can find deals at more attractive prices. If investors can make the case that their system can 1) expose them to new market segments at an advantage over the competition, and 2) integrate niche, verticalized, unique data sources, LPs will likely be more willing to open their wallets.
Some investors also choose to build their own systems for added layers of security. Instead of relying on external data storage solutions, some prefer to house their data on their own systems for full control. In-house servers can also be configured to meet specific needs, which could be a plus for investors concerned about the level of customization offered by third-party solutions.
What Investors Need to Build Successfully
Make no mistake — building your own proprietary M&A data infrastructure is no small feat. It’s complex, time-intensive, and expensive.
Pulling it off requires a deep bench of engineering, devops, data science, and AI and machine learning (ML) capabilities. The cost of assembling that pool of resources alone prohibits many firms from buying their own system. Consider that the salary range for a single big data engineer is $150,000 to $250,000, depending on their level of experience.
Your team will also need to license datasets to achieve the level of granularity you need to identify the right investment targets and conduct due diligence. These could include:
- Relationship data, including a history of your first-party data from your CRM, showing interactions with target companies and similar companies
- Company rankings, such as “Best Company” lists
- Annual reports, SEC filings (for public companies or public comparables)
- M&A transactions and past deals
- People data, such as employment history
- Public records and government disclosures
- Industry specific sources like web traffic, consumer reviews, credit card data, healthcare claims, and more
Add up the cost of paying multiple engineers, licensing multiple datasets, and maintaining the infrastructure to make the whole thing work, and you’re easily at $1M.
If you’re spending that kind of money, you want to minimize costly errors and delays. To make sure the building process is as time- and cost-efficient as possible, here are some key steps that your team should take:
- Develop a clear, specific vision for the database’s end result. Who will use it and how will they use it? Define specific criteria and parameters for measuring success so that you can validate each release against those metrics.
- Define what a “company” is for your purposes, and build a set of companies based on that definition. Make sure the set does not contain duplicates resulting from foreign countries, mergers, products under different brand names, etc.
- Identify the best source for each necessary data point and build prioritization rules to organize the data display in the system interface.
- Establish a schedule for the updates needed for each data point to achieve the desired end results.
- Create a plan to iteratively build the system, test it with internal teams, and refine the data representation based on what your test results show.
After you have sufficiently tested, adjusted, and improved your system, you’re ready to release it. Once it’s up and running, train the teams at your firm on the system and mandate that they use it for their internal workflows. This ensures that usage is consistent across your firm, driving efficiency across your dealmaking workflows.
How Grata Can Help
Fortunately, Grata can help make your building process as smooth as possible. Grata’s API lets you easily access all of the data and algorithms you need in your own systems:
- Search API: Ideal for building company lists and using keywords to find targets that match your investment thesis
- Similar API: Perfect for similar company discovery, comp set generation, benchmarking, and list creation
- Enrichment API: Designed for single company firmographic enhancement and using company domanis to add substantive profile data points
- List API: Allows users to tag companies into custom segments
If you need bulk data feeds to enrich your firm’s existing algorithms and company data, Grata’s Data Warehouse can give you access to 25 data points on over 12M difficult-to-find private companies, including:
- The most accurate employee counts and private company revenue estimates
- NAICS6 classifications with over 95% accuracy and a custom taxonomy for software
- More than 35 categories for vertical and horizontal software business models
- Accurate and comprehensive business point of interest (POI) location data
- Contact information for 8M+ private company executives
- Over 7,000 annual events, along with conference attendee lists
- Private market transaction data for PE, corporate, and add-on acquisitions, as well as VC and growth rounds
- Grata also captures all of the relevant real-time web data that you can’t get from LLM APIs. We use machine learning to ensure our data system is always up-to-date and that it meets diligence-grade standards.
You can use our entire data universe or define your own subset based on what your firm needs.
To learn more about how Grata can help you leverage the best data to win more deals, schedule a demo today.
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