You're staring at a screen, probably caffeinated, trying to find that one specific deal multiple from a 2022 mid-market tech buyout. It's frustrating. Most people think finding a mergers and acquisitions database is just about paying a subscription fee and getting a magic spreadsheet. It isn't. Honestly, the "data" out there is often messy, incomplete, or just plain wrong because private companies don't exactly love shouting their valuations from the rooftops.
Dealing with M&A data feels like detective work. You have the big players like Bloomberg and Refinitiv (now LSEG), but if you're looking for that $50 million manufacturing deal in Ohio, those platforms might leave you hanging.
The reality of the M&A world is that information is fragmented.
Why Most M&A Data is Kinda Trash
Let’s be real for a second. Public company data is easy. You check an SEC filing, you see the 8-K, and the purchase price is right there in black and white. But the vast majority of the global economy happens in the private sector. When a private equity firm buys a family-owned distribution business, they aren't obligated to tell you the EBITDA multiple. They might not even disclose the total enterprise value.
This is where a mergers and acquisitions database earns its keep, but also where it fails.
Some platforms rely on "estimated" data. They use algorithms to guess the revenue based on employee count or office square footage. It's shaky ground. If you're building a valuation model based on a "guess," your entire DCF or comparable company analysis is basically a house of cards. You've got to know where the data comes from. Is it sourced from press releases? Is it self-reported by the PE shops? Or is it scraped from news articles that might be exaggerating the "billion-dollar" status of a unicorn?
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The Heavy Hitters: Capital IQ vs. PitchBook vs. Crunchbase
If you've spent any time in investment banking or corporate development, you know the "Big Three" or "Big Four" struggle.
S&P Capital IQ is the gold standard for many. It’s dense. It’s expensive. It’s powerful. But its strength is primarily in public markets and deeply vetted corporate structures. If you need to see the cross-holdings of a multinational conglomerate, CapIQ is your best friend. But for "scrappy" M&A? Maybe not.
Then there’s PitchBook.
Everyone loves PitchBook because they have researchers who actually pick up the phone. They hunt down those elusive private equity deal terms. They focus heavily on the venture capital and PE lifecycle. If you’re looking for a mergers and acquisitions database that tracks the "dry powder" of a specific fund or the IRR of a 2015 vintage, PitchBook is usually the winner. But again, you're paying a premium for that "human-verified" tag.
Crunchbase is the weird cousin. It started as a tech-only database. Now, it's trying to be everything to everyone. It’s great for seeing who led a Series B, but for a complex cross-border merger involving earn-outs and stock swaps? It might be a bit too shallow.
The Mid-Market Blind Spot
Small and medium enterprises (SMEs) are the backbone of M&A volume, yet they are the hardest to track.
Consider a "bolt-on" acquisition. A larger company buys a smaller one to get their software or their customer list. These deals happen every single day. Most databases miss about 40% of these because they don't hit the "materiality" threshold for a public news blast.
If you're a broker or an associate at a boutique firm, you can't just rely on one source. You have to cross-reference. You check a mergers and acquisitions database, then you check local business journals, and then you might even look at LinkedIn to see when the executive leadership at the target company suddenly changed their titles to "Former CEO."
It’s about stitching together a narrative from crumbs.
High-Quality Data vs. High-Quantity Noise
We live in an era of "big data," but in M&A, big data is often just big noise.
- Verified Deal Values: Only trust databases that cite the source of the price.
- Sector Granularity: A "technology" tag is useless. You need to know if it's SaaS, FinTech, or HardTech.
- Adviser Credits: Knowing which law firm or boutique bank worked on a deal is huge for networking and verifying the "vibe" of the transaction.
- Multiples: If the database doesn't offer EV/EBITDA or EV/Revenue, it’s just a news feed, not a tool for valuation.
The nuances matter. For instance, a deal might be announced at $500 million, but $100 million of that is an earn-out contingent on three years of growth. A lazy database just lists $500 million. A professional-grade mergers and acquisitions database will break out the cash at closing versus the contingent consideration.
The AI Revolution in Deal Sourcing
Everyone is talking about AI, and yeah, it’s changing M&A too.
Newer platforms are using Large Language Models to scan millions of local news sites in 50 different languages to find deal rumors before they hit the mainstream. This is "proprietary deal flow" at scale. Instead of waiting for a banker to send you a CIM (Confidential Information Memorandum), you can see that a company is "positioning for a sale" because their job postings suddenly shifted from "Growth Engineers" to "Compliance Auditors."
But don't get too excited. AI can hallucinate. It can think a "strategic partnership" is a "merger." You still need a human to look at the data and say, "No, that's not a change of control; that's just a joint venture."
Actionable Steps for Choosing a Database
Don't just sign the first three-year contract that lands on your desk.
- Audit the "Last 10" Test: Take the last 10 deals you know happened in your specific niche. Search for them. If the database only found 6 of them, or got the prices wrong on 3, move on.
- Trial the Export Feature: Some platforms make it a nightmare to get data into Excel. If you can't manipulate the data, it's just a pretty picture.
- Check the Geography: A database that's amazing for US deals might be completely useless for DACH (Germany, Austria, Switzerland) or Southeast Asia.
- Ask About the Research Team: Find out how many actual humans are verifying the data. If it's 100% automated, expect 20% error rates.
Ultimately, a mergers and acquisitions database is a tool, not a strategy. It can tell you what happened yesterday, but it won't tell you what to pay tomorrow. That part still requires a bit of gut instinct and a lot of math.
Start by defining your universe. If you’re doing $5M–$20M deals, look at specialized platforms like DealSifter or even Axial. If you’re chasing unicorns, stick to the heavyweights. Just remember that the most valuable information is usually the stuff that isn't in a database yet. Use the tools to find the trail, then do the legwork to find the truth.
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Next Steps for Deal Professionals
- Identify the top 3 competitors in your specific sub-sector and see which platform tracks their historical bolt-ons most accurately.
- Request a "trial" period where you specifically look for "Deal Multiples" rather than just "Deal Volume."
- Verify the data lag—ask the sales rep exactly how many days it takes for a closed deal to appear in their system.