Virtual deal rooms represent large blobs of data content, but granular data lineage, integration outcomes, and decision traceability are needed for auditability, investor demands, and regulatory oversight — Enter MAD data factories
Key points:
-
-
-
Granular data and MAD data factories — Granular data lineage, integration outcomes, and decision traceability are critical for auditability, investor demands, and regulatory oversight — and MAD data factories could be a solution to these challenges.
-
Problems with traditional virtual data rooms — Traditional virtual data rooms, while an improvement over previous methods, are now considered outdated, lacking the advanced data capabilities needed for modern MAD processes.
-
Evolution of MAD processes — The shift from traditional MAD processes to data-driven, AI-powered approaches underscores the importance of modularity, compartmentalization, and creating reusable building blocks to adapt to competition, customers, and regulatory changes.
-
-
In an all-too-often reoccurring story, the merger deal was identified, architected, and solid. The deal checked all the boxes — valuation, cultural fit, synergies, and even innovation enrichment. Between the announcement and close, however, granular data reviews revealed a material, long-buried litigation risk that was missed in the due diligence mainly because of siloed, conflicting data from previous mergers and acquisitions.
The ensuing fire drill was too common — weeks of delay, additional costs, investor angst, and damaged trust across organizations. Unfortunately, this is not an isolated case study. The operational and decision support data buried within organizations often can represent material legal, compliance, and audit risks.
With corporate, industry, and governmental data now measured in zettabytes (that is, a 1021-multiplier compared to a terabyte at 1012), traditional data analysis methods are inadequate for today’s corporate technology environments, and even worse for exploding data-driven AI capabilities.
When combined with fragmented corporate and industry data infrastructures, cyber and IT security challenges, technological advancements, and application-centric solutions, the M&A legal, compliance, audit, and operational experts of today are unprepared for the bevy of red flags now on the horizon.
The rise of MAD data factories
For the last 15 years across mergers, acquisitions, and divestures (MAD), the use of virtual data rooms has been a cornerstone of due diligence and secure document sharing. This legacy usage had improved the deal pipelines, focused legal and compliance risks, and shortened post-deal integration playbooks across private equity and corporate acquisitions, and even for MAD brokers and advisors.
When introduced, virtual data rooms represented a quantum shift of how MAD data was identified, reviewed, and compiled, eliminating the demand for so-called sensitive compartmented information rooms within legal and corporate facilities. It was a significant burden to assemble, validate, analyze, and track document-based artifacts, which created additional risks, expenses, and security challenges. Virtual data rooms were a better method, but still not ideal.
Today, traditional virtual data rooms represent the past — analogous to paper-based digitization common across application systems. Why? Technological data advancements are offering MAD insights and integration capabilities previously considered too expensive, too laborious, and too disrupting.
Virtual data rooms represented large blobs of data content, but what about all the granular data lineage, integration outcomes, and decision traceability needed for auditability, investor demands, and regulatory oversight? Today, the challenges of original virtual data rooms design principles when combined with advanced data technologies can deliver a robust, adaptable approach for MAD pre- and post-deal teams — MAD data factories.
To understand the fundamental shifts, Table 1 compares and contrasts the differences of principles and designs across nine MAD playbook categories.
The comparisons also highlight the next-generation outcomes sought with the inclusion of advanced data capabilities (that is, the fuel) and AI solutions (the powerful engines). For legal, audit, and regulatory personnel, they can no longer just check boxes — they must be continuously and proactively addressing deal accountability, risk intelligence, and anticipating ever-changing outcomes.
How a MAD data factory works
Straightforwardly, the MAD data factory represents the significant evolution of traditional virtual data room designs with the incorporation of repeatable data-driven AI capabilities, which analyze and predict outcomes, risks, and deficiencies.
This MAD automated assembly-line architecture turns messy operational systems and document artifacts into smart, adaptable decisions using natural language processing and advanced data solutions, such as data meshes, fabrics, and weaves. An analogy would be a 1970s-era auto factory compared to a 2025 gigafactory.
From pattern recognition to smart data rooms, document summarization, and operation data, the MAD data factory represents highly specialized domain knowledge weaved into advanced leadership conversations. It is the data necessary to support leaders who command the goals, synergies, budgets, and designs delivering MAD strategy and outcomes.
Table 2 showcases the underlying mindset shifts underway with the explosion of data complexity and new technologies, coupled with the MAD impacts on deal identification, integration, and value realization.
Table 2 also demonstrates that MAD actions throughout the remainder of this decade are no longer transactions, but highly complex and continually changing connections even after the deal is announced. MAD discovery used to be about document sharing, resulting in reactive outcomes based on highly selective information, such as was found in traditional virtual data rooms. The problem with prior MAD playbooks and checklists, often based on months-old data, is that they don’t provide in-depth real-time analysis, interpretation, or an ability to connect the dots within artifacts, deal teams, or across the combined entities.
The eruption of AI — through generative AI, retrieval-augmented generation (RAG), agentic AI, and agentic RAG — combined with an equal explosion of data storage and design solutions (that is, the fuel feeding individual AI solutions) has broken the traditional methods and approaches that were prized by boards of directors, C-Suite executives, and their advisory firms. Instead, what is emerging at the edges of data-driven AI capabilities and benefits for private equity, corporate acquirers, and MAD system integrators are the importance of data fragments that need to be encapsulated into the pre- and post-deal events.
MAD today is about modularity, compartmentalization, and creating reusable Lego-like building blocks that can be innovatively improved or delivered to expand scale and market penetration. These architected Legos can then be stacked, using advanced technology and data architectures, to rapidly adapt to competition, customers, regulators, and even reshoring of manufacturing capabilities.
The principles for MAD are now focused on delivering a seamless pipeline from the target to the final integrations, all driven by solutions within the emerging MAD data factory designs. Using these factories, MAD leaders are able to deliver continuity even during changes. Indeed, modern MAD processes are not one-offs but rather flow-based, interoperable, and governed. Each step is stackable, auditable, and reportable that supports accountability and MAD value delivery.
Since 2022, before a 45% to 55% contraction of annual deal volumes, the pre- and post-deal playbooks were about legal checklists, long-tail revenue targets, brute-force operational integrations, and robust program management against pre-deal synergies. Yet, the reality of MAD is that it is no longer an art form or transactional event — it is about data science.
Past to present
For the remainder of this decade, MAD is about data, intelligence, prediction, traceability, auditability, modeling, and adaptability all leveraging the playbook pillars of the past. As a result, each phase of the MAD process — from red flags to real time — is auditable and reportable, possesses lineage, and is backed by evidence.
How many organizations and leaders are preparing for the future MAD explosions using legacy ideals, skill sets, and methods? Experience does matter, but only if it uses the MAD data factories to create outcome certainty, deliver new revenue streams, instill regulatory consistency (not just compliance), and offer a model for speed and automation. In fact, I discussed the growing realities of M&A digital demands using AI back in 2021. At that time, the technology was embryonic, the MAD data principles long tailed, and the imperatives unproven. That was then.
Now, the mainstreaming of MAD data factories has arrived — all the pieces are there for assembly. The MAD roadmaps are available, but the map readers are scarce. And MAD will never be traditional or transactional again.
Register now for The Emerging Technology and Generative AI Forum, a cutting-edge conference that will explore the latest advancements in GenAI and their potential to revolutionize legal and tax practices
