Data is king: The saying has become a cliché precisely because we all recognize its truth. But it masks the complicated reality that the universe of data is cluttered and complex. Trying to assemble many hundreds of different pieces and shape them into a coherent whole can feel like a massive obstacle.
In the dealmaking universe, the challenge is particularly acute. How do we make sure we’re looking at the data points that will help us make better decisions, whether identifying the most promising niches for a new platform investment, or picking the highest-potential add-on deals?
And how do we assemble all of those tidbits – the reliable, the obscure, the confusing pieces – so that they paint a vivid picture? In other words, how do we build a compelling mosaic out of tiles of myriad shapes, sizes, and colors?
If the word “mosaic” rings a bell, then it’s probably because you’ve run across it not just in reference to floor decorations in the ancient world, but in the world of finance. In a nutshell, mosaic theory argues that an investor or analyst who assembles the broadest possible range of information can develop the most robust investment decisions.
While this sounds straightforward, it can be immensely complicated, requiring a person to first acquire, process, contemplate and assemble information from across many disparate locations. Then they must separate the pieces that are useful from those that don’t make the overall picture any clearer.
It’s a particularly handy metaphor for dealmakers, especially in the midst of a tough investment climate and at a time when we’re all awash in more data than at any point in history. Just as a single misshapen or discolored piece of tile can ruin a mosaic, so can relying on an incorrect, inaccurate, or outdated piece of data distort your dealmaking strategy and cause you to miss out on great opportunities.
Each piece of data must be questioned and quality-checked to see if it can shed new light on an investment thesis or fill in a gap in what’s known about a specific acquisition target. And often the most critical pieces of data are those that require a bit more context and supplemental information to understand.
For example, you may see a target company’s headcount rising – but is this happening in reaction to a new round of fundraising, or in anticipation of it? Has a company that caught your eye just opened a new office in a new part of the world and begun receiving media attention from international press? The ability to connect the dots across these various pieces of data and see the bigger picture is what enables modern dealmakers to infer a company’s growth and intentions and get there before the competition.
Every fresh piece of information – tiny and possibly immaterial on its own – can transform a potential acquirer’s overall picture of a company and its investment potential. But to accomplish this, they must be in a constant search for information from all possible sources that contribute to that mosaic — a task that has always been much easier said than done.
While assembling a complete view of any market or company was once an entirely manual process, 77% of dealmakers now use data service providers to help them get up to speed more quickly. However, the sheer quantity of all the information that’s become so readily available can be a liability, hampering clear strategic thinking.
Adding to the noise is the recent surge of AI-powered technologies that promise to deliver dealmakers game-changing insights. In reality, advancements like generative AI are still very much prone to misleading hallucinations, unintentional bias, and other data challenges that often cause more harm than good.
If a mosaic artist is creating an image of a storm at sea, they’re not in search of tiles of any color, they want blue ones — and ideally a way to separate them by size, shape, and the specific shade of blue. Similarly, as data providers and their tools have multiplied, dealmakers’ question is increasingly no longer ‘’do we have data about this?” but “do we have the right data to move forward?”
That’s why more dealmakers are seeking technologies and vendors that offer sources-first data. Sources-first data is generated using a combination of AI and data experts to connect and cross-check information across thousands individual sources, including industry buyer’s guides, conference lists, and more — a process known as human-in-the-loop machine learning.
When categorized and linked correctly, sources provide a web of insight that not only delivers verified, high quality company profiles, but also offers the context necessary to better understand entire industries and derive deeper, less obvious data signals about companies’ growth intent. Just as tiles cannot spontaneously organize themselves into a beautiful and cohesive mosaic, this complete, actionable picture is impossible for dealmakers to achieve with AI alone.
Mosaic artists of the ancient world transformed a motley assortment of tiles into a vivid image that is more than the sum of its parts. 21st century dealmakers can use the same approach to assemble today’s countless tidbits of available data into complete pictures of target markets and companies that guide their investment strategies.
Sourcescrub’s sources-first, AI-powered deal sourcing platform can help. If you’re curious why organizations like Tide Rock, Vaquero Capital, and Tennant Company choose Sourcescrub to help them see and win more deals, let’s talk.
