Dealmaking in India is entering a high-stakes phase. In calendar year 2024, companies announced over $110 billion in mergers and acquisitions (M&A), which was a significant rebound after the 2023 slowdown. With the RBI lowering the policy repo rate in February 2025, borrowing costs may fall, supporting deals and consolidations. As forward-looking enterprises will stay encouraged to pursue expansion, M&A value is expected to grow further in 2025 and beyond.
The size of these deals indicates more than market optimism – it raises the bar for post-merger outcomes. Investors and boards are now vying for dynamic synergy culture, flawless integrations, and tangible value creation. And the real differentiator is data: how effectively organizations gather it, interpret it, and turn it into action. On the deal signing landscape, the winners will be the ones who transform information into insight and insight into impact.
Why Data Matters in Modern M&A
M&A headlines in the media often highlight the purchase price, but the real narrative emerges from the details that lie beneath the surface. It is the information that only careful analysis uncovers. During target screening, rich datasets reveal where markets overlap, where customers churn, and which products/services quietly improve margins.
With due diligence, analytics also flag hidden liabilities, such as supplier concentration that threatens continuity or a revenue stream too dependent on a single client. Such facts are not footnotes – they can change valuations by millions.
Once a deal is inked, the stakes rise even higher. Integration plans succeed only if finance, operations, and customer records are harmonized quickly and accurately. An example to consider is how HP wrote down a US$8.8 billion deal after acquiring the UK software firm Autonomy. HP cited serious discrepancies in financial reporting and data integrity. Post-acquisition audits revealed revenue recognition and accounting issues that were not fully visible during due diligence, making integration nearly impossible. The write-down proved that a headline valuation means little if the underlying facts are not tested and reconciled across every ledger and system.
For leadership, data-driven forecasting is used to compare projected savings or cross-sell growth to real-time performance, rather than relying on last quarter’s estimates.
In addition, regulators and investors seek transparent reporting. Clean, auditable data shortens approval cycles and strengthens credibility with shareholders who demand proof of post-deal value.
Summarily, while numbers on a balance sheet close a transaction, it is trusted, timely data that determines whether a transaction actually creates value.
The Data Playbook: Steps M&A Teams Should Take
Turning a signed agreement into real value demands a disciplined data strategy that starts long before the closing day. Let’s look into the key points here:
- Pre-deal readiness: Before negotiations go deep, the acquiring entities must gauge the target data’s health: where do the records live, how structured they are, and can systems share information securely? Building clauses into the letter of intent for API access or controlled data-room testing helps to identify potential issues that may arise later.
- In-depth due diligence: Advanced data analytics involves a detailed study of transaction-level data on the target’s cash flows, revenue quality, customer retention, the supply-chain exposure, and how the margins have evolved for specific products, locations, and customers. AI and machine learning can flag anomalies or hidden dependencies that traditional audits often overlook.
- Day-1 to Day-100 integration: A well-planned 100-day integration strategy is critical to minimize risks, build trust, and demonstrate the deal’s business value. The focus here should be on harmonizing master data – for finance, customers, suppliers – so M&A teams can operate around a single source of truth. A common real-time dashboard enables everyone to monitor synergy goals and spot deviations, if any, as soon as they appear.
- Continuous governance: Post-close, organizations must treat data stewardship as ongoing. Regular audits, automated quality checks, and clear ownership ensure that the information driving critical decisions stays reliable.
Following this playbook keeps the merger journeys on track, allowing leadership to move from lofty forecasts to quantifiable impact without losing momentum.
The Tech Stack Behind Data-Driven Deals
Data integration is only effective when it has a strong technology foundation for support. M&A deal teams need a layered stack that records, cleans, processes, and mobilizes information fast enough to guide real-world decisions:
- Cloud Data Solutions: Platforms such as Snowflake, Azure, or AWS Redshift provide a secure, scalable space where both parties can share and analyze information during diligence and after closing.
- Master Data Management (MDM) Systems: With MDM tools, it is simpler to reconcile customer, supplier, and product-related details into a single version of the truth.
- Advanced Analytics and AI: They provide alerts on anomalies, forecast revenue quality, segment customers for cross-sell or retention strategies, and even predict churn.
- Low-Code and Automation Tools: Companies can leverage them to accelerate tedious integration tasks like mapping legacy ERP data and syncing HR systems.
- Business Intelligence Dashboards: Real-time visual reporting via Power BI or Tableau transforms data into accessible insights while automatically monitoring synergy targets and flagging deviations.
- Secure Data Rooms & API Connectors: They make way for compliance and smooth, controlled data exchange during due diligence and post-close phases.
Data-centricity is already shaping successful deals. When Microsoft acquired Nuance Communications in 2022, both firms leveraged a combined Azure-based data lake and machine learning pipeline to sync healthcare-specific datasets. It enabled their product teams to integrate clinical speech-recognition tools within months and meet aggressive synergy targets.
How a Quick Data-Driven Pivot Helps
Advanced data analytics surface issues missed by traditional diligence. In one scenario, if the acquirer merges customer and distributor records into a unified model, AI and machine learning tools may reveal patterns such as a heavy revenue dependence on just a few partners or regions. Discovering such concentration early gives deal teams a chance to adjust valuation models, add protective earn-out clauses, or reshape the first-100-day integration plan. In most of the transactions, these timely insights don’t derail the deal; they just redefine its economics and refine the post-close roadmap.
Making Every Merger a Growth Catalyst
The earliest data signals shape success in M&A: identifying targets, validating assumptions, and continuing through the critical post-close phases. That’s where expert guidance matters.
Practus offers M&A advisory and valuation services at all stages of a deal to plan and execute data-powered strategies, from early analysis to post-merger integration. Our solutions are underpinned by our deep experience in finance and digital transformation technologies. By understanding each client’s unique business environment and goals, we help convert information pools into sustainable value.
By Practus Expert
