Jonathan Simnett is a managing director at Hampleton Partners, co-host of The Difference Engine tech strategy podcast, and an M&A dealmaker with decades of experience helping tech founders and investors to scale, fundraise, merge and exit. We spoke to him about one of the most significant, yet currently undervalued and under-hyped, aspects of the AI revolution.  

Q: Since the emergence of ChatGPT, the AI hype cycle has focused on the seismic capabilities of LLMs and their associated agents and apps. But you’ve had your eye on what you’ve described as “the hidden power behind the AI boom”. What is that?

Jonathan Simnett: While breakthroughs in generative AI models are continuing to fuel an unprecedented wave of mergers and acquisitions, an arguably more valuable revolution is unfolding outside of the media spotlight. This involves the infrastructure that feeds the AI algorithms, namely data management. The brutal truth about AI is that no model, however sophisticated, can outperform the quality of the data it consumes. Open-source LLMs are rapidly commoditising, and the next differentiator in AI will not be who has the best model, but who has the cleanest, most traceable data pipelines. Because without clean, compliant data, AI outputs are unreliable, legally risky or commercially unusable. 

Q: What are some notable companies in this space, and why are they less well-known in the public discourse than they deserve to be?

JS: Informatica, Collibra, Alation, Talend, and Databricks are among the prominent specialists in ensuring data is accurate, compliant, discoverable, unbiased and shareable across increasingly complex digital ecosystems. I think one of the chief reasons they fly under the tech media radar is their work is fundamentally “unglamorous” compared to the razzle dazzle of the generative tools and AI agents we interact with directly. Data management firms essentially provide the “picks and shovels” of the AI gold rush, and it’s worth noting that historically speaking those who sell picks and shovels have fared far better than the miners chasing veins of hype. 

Q: Is the relative lack of “hype” reflected in market valuations?

JS: It’s a fact that, despite their centrality to the AI industry, and the fact that data is frequently proclaimed as the lifeblood of AI companies, data management firms do trade at deep discounts compared with their AI counterparts. Publicly listed firms often command 5-10x forward revenue multiples, compared to 20-40x and more for AI software vendors. This gap is all the more striking when you consider that many businesses in the latter category have yet to turn a profit. By contrast, data management specialists are typically defensible, cash-generating businesses with recurring enterprise revenues and renewal rates above 90% and margins which are often north of 70%. 

Q: Are tailwinds set to bring more visibility to this segment?

JS: It’s certain the data compliance revolution will drive billions in enterprise budgets towards the services of data management firms. A good example of the new regulatory frameworks fuelling demand is the EU AI Act, which requires organisations to demonstrate the quality and provenance of the data feeding their algorithms. That will necessitate spending, not on new models, but on data governance, cataloguing, and lineage.

We’re also seeing M&A moving down the stack, from the visible layer of model developers and application startups to the data layer. A standout recent example is Salesforce’s acquisition of Informatica, with Salesforce CEO Marc Benioff saying that “You have to get your data right to get your AI right”. This essential truth will likely fuel an acquisition wave where investors stop chasing shiny models and start buying the rails that make AI deployment viable, and allow AI to deliver on the hype. At the same time, we’ll also see a consolidation wave as data management firms snap up smaller competitors to enhance their product suites and enter new markets.

To put it another way, we’re witnessing a familiar pattern, with infrastructure – initially underpriced – ultimately set to capture the majority of long-term value. In the same way that cloud computing giants like AWS and Microsoft Azure now underpin the digital economy, today’s data management players will underpin the AI economy.

Salesforce and Informatica

A blockbuster deal in this subsector was completed in November, when Salesforce acquired Informatica for USD 8bn. A legacy player in the data management space which was founded back in 1993, Informatica’s solutions for data governance, integration and quality will be leveraged by Salesforce to bolster its agentic AI capabilities.

Alation and Numbers Station

In May, enterprise data intelligence platform Alation acquired Numbers Station, a startup specialising in AI-native data applications, for an undisclosed amount. By incorporating Numbers Station’s data analytics technology, Alation intends to optimise how its clients’ AI agents interact with structured data, boosting accuracy and enabling them to scale their AI processes more rapidly. 

ServiceNow and data.world

May saw another major deal in the form of ServiceNow’s purchase of cloud-native data governance platform data.world, Struck for an undisclosed amount, the deal is part of Service Now’s larger strategy to use M&A to enhance its ability to make its clients’ data “AI-ready”. A ServiceNow spokesperson encapsulated the company’s rationale in evocative terms, saying the “path to agentic AI heaven goes through some form of data hell.” 

If you’re a founder, executive or investor in the data management space, please reach out to Jonathan Simnett to discuss the next potential steps in your M&A strategy.

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