Most Indian acquirers never budget for what comes next – Subhani Shaik led the data workstreams for a high-stakes Fortune 500 technology pre-merger transformation and now explains the integration problem boardrooms keep ignoring
India's deal market opened quietly in 2026. Grant Thornton Bharat's Dealtracker recorded 73 M&A transactions in January, worth $3.1 billion – a 33% drop in volume and a 64% decline in value compared to December 2025. One deal, however, stood apart: Coforge's $2.4 billion acquisition of Encora, a global IT services company, accounted for nearly 40% of the month's total M&A value. When a single technology acquisition can dominate a national deal market that way, the question worth asking is not how much was paid, but what happens to all the data once the ink dries.
For Subhani Shaik, a DC Manager at Deloitte Consulting and Technical Program Manager at Google Cloud, these are not theoretical concerns. Over a decade at Deloitte Consulting , where he ranked consistently in the top 5% of performers firm-wide, he directed the data migration for the de-merger of a global information services giant from a major mass media conglomerate, and the IT transformation of a Fortune 500 enterprise technology leader ahead of its merger with a global software firm. Since 2022, he has served as Technical Program Manager at Google Cloud in Mountain View. He has been inducted as a Fellow of the Hackathon Raptors, a peer-vetted, invite-only fellowship reserved for professionals who have demonstrated exceptional leadership and innovation across at least five years of continuous achievement, selected through a multi-stage review by existing Fellows. We asked him to explain what India's acquirers are getting wrong before the deal closes and what it costs them after.
Subhani, the Coforge-Encora deal is a benchmark for strategic acquisitions in India's IT sector. Beyond the balance sheet, what is a buyer actually inheriting from a data standpoint in a deal of this magnitude?
In any high-stakes acquisition, the buyer inherits more than revenue — they inherit years of accumulated data entropy. Every firm has fragmented logic embedded in its systems: fields repurposed for functions they were never designed to handle, naming conventions that differ by geography or by whichever engineer originally built the system, customer definitions that conflict across product lines. None of that appears in financial or legal due diligence unless someone specifically goes looking for collective intelligence. Most buyers audit the technology stack and stop there – the data architecture underneath stays unmapped. Left unaddressed, that invisible technical debt can erode 20 to 30 percent of projected post-merger synergy value within the first year alone. What I focus on is getting ahead of that problem – treating data architecture as a forensic exercise before the integration phase begins, not after the deal has already closed.
You successfully led the data migration for the de-merger of a global information services giant from its parent media conglomerate, one of the more complex separations of its kind. What made that program particularly difficult to manage?
A de-merger is in some ways harder than an acquisition, because you are separating systems that were deliberately built to be shared. Clarivate had core financial modules running inside Thomson Reuters infrastructure – they were not designed to stand alone. Migrating all of that to a new NetSuite ERP, while keeping live operations uninterrupted, required a sequencing logic that could not afford a single error. Foundational data had to move first, then core master records, then transactional history in that order, without exception. Reversing that sequence mid-migration, or attempting to run both environments simultaneously without a strict isolation layer, is exactly where organizations lose months of reconciliation work they never budgeted for. Every decision carries downstream consequences, and at that level of complexity, the margin for improvisation is zero.
The Autonomous Semantic Harmonization Framework (ASHF) you developed at Deloitte Consulting were formally codified into Standard Operating Procedures and incorporated into client proposals for organizations such as Compassion International, Hewlett Packard Enterprise, and Clarivate Analytics. That kind of institutionalization is rare – what does it tell you about a methodology when a firm decides to do that?
It signals a transition from a successful delivery of Cloud CRM data migration projects for our clients to an industry gold standard. When Deloitte Consulting codified those frameworks into its National Office Standards, the firm was formally validating them as its definitive technical blueprint for this class of work, not just something that had worked once on a particular engagement. In the volatile environments of M&A and de-mergers, where the margin for error is zero, that distinction matters enormously. The migration architecture became a Global Knowledge Asset: used as the primary technical proof of concept in high-value proposals, and the basis on which the firm competed for new contracts. Other teams tackling similar programs were not starting from scratch – they were working from a methodology already stress-tested in some of the most complex restructurings on record. By institutionalizing that work, Deloitte Consulting took what was individual expertise and turned it into a firm-wide competitive differentiator that extended well beyond any engagement I personally ran.
Commercial success is not the only way to measure what data architecture can do. One of your projects served a global nonprofit supporting several million children across dozens of countries. How do you measure the result of that kind of work?
In humanitarian work, the measure of success is what I would call the elasticity of aid, whether an organization can scale its reach without losing track of the individuals it exists to serve. Before the transformation, siloed legacy systems were creating data orphans: records that fell through gaps between platforms, severing the link between donors and the children they were sponsoring. Building a unified Salesforce and NetSuite architecture across all 29 countries simultaneously required 100% data fidelity for the 1.3 million records already in the system, because any loss at that stage meant a real child losing their sponsorship support. Getting that right created the technical foundation for the organization to scale safely from 1.3 million beneficiaries to 2.3 million. The architecture did not just improve efficiency – it directly expanded the organization's capacity to deliver aid to people who had no alternative.
Given everything you have seen at both the consulting and operator level, what is the single most important thing Indian companies should do differently before signing a major acquisition?
Mandate a pre-deal data architecture audit as a non-negotiable pillar of due diligence — not something to revisit after the deal closes. A target company's master data standard, or its collection of undocumented workarounds, is the most reliable predictor of what integration will actually cost and how long it will take. Assessing how systems communicate before signing is the difference between capturing value and inheriting architectural debt. Beyond the audit, enforce a strict sequential transformation standard: no live migration should run without an isolated transformation layer and a documented fallback plan for every critical data domain. Running old and new systems simultaneously during migration is precisely where data drift begins and once it starts, it compounds in ways that are very difficult to unwind. A controlled environment and strict sequencing are what allow a merger to capture its projected value on Day 1, rather than spending the next two years trying to recover it.
