5 ways Fivetran accelerates mergers and acquisitions

Every merger or acquisition (M&A) moves through a recognizable sequence of phases, from the first sector screen that identifies a potential target through to the post-merger integration work that determines whether the deal actually creates the value the investment thesis promised. At each phase, data plays a different role, and the quality, speed, and completeness of data access consistently separates the acquiring organizations that win from those that overpay, miss integration timelines, or realize far less synergy than projected.

Studies consistently show that many M&A transactions fail to deliver expected value. The most commonly cited causes — overpayment due to incomplete diligence, integration delays, data inconsistencies in financial consolidation, and synergy capture failures — all trace back to data problems. 

The 5 points that follow map Fivetran's specific capabilities to the phases where data quality and data speed most directly determine deal outcomes.

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1. Market due diligence

The most effective acquiring organizations do not wait for investment bankers to bring them deals. They maintain a running list of priority targets — continuously refreshed, financially modeled, and ranked by strategic fit — so that when a process opens or a direct approach becomes possible, they can move immediately with proprietary insights rather than starting from scratch. This outside-in diligence approach is how the most acquisitive companies consistently make better offers faster than their competitors.

Building and maintaining that target screen requires ingesting and analyzing public market data at scale. But none of the data arrives through a single API — it requires pulling from multiple sources, normalizing it to a consistent schema, and loading it into a warehouse where financial models and competitive analysis can run against it.

Fivetran's Connector SDK is the mechanism for building these pipelines rapidly. The SEC EDGAR API is a REST API with free access and well-documented endpoints — you can build a working SDK connector that fetches quarterly filings, parses financial statement data, and loads it into Snowflake or Databricks in an afternoon. File connectors handle the structured data that comes as CSV exports from financial databases or scheduled file drops from data providers. The Fivetran ad hoc CSV upload capability handles one-time datasets that arrive from advisors or research providers. Together, these provide the ingestion layer for a continuously updated target intelligence database that gives corporate development teams a proprietary view of their sector that competitors without this infrastructure simply cannot replicate.

2. Initial due diligence of the target

Once an NDA is signed and a target enters formal dialogue, the acquiring company typically sends an initial information request, like a structured list of the data needed to construct a preliminary valuation and identify any immediate red flags before committing significant diligence resources. Speed matters here because the company that can process and analyze initial data quickly sets the pace of the process and demonstrates the organizational capability that builds seller confidence.

These datasets typically arrive in Excel files, accounting system exports, or shared folder data rooms — formats that Fivetran's File Connectors and CSV datasets can ingest directly into a structured warehouse schema.

The analytical objective at this stage is to answer 5 core questions before committing to a binding bid: 

• Is the revenue quality what we think it is — recurring, diversified, and growing? 
• Are the margins sustainable, or are there cost-side issues the headline EBITDA conceals? 
• Is working capital management healthy? 
• Are there any financial risks — concentration, covenant triggers, or off-balance-sheet liabilities — that materially change the valuation? 
• Is the business trajectory accelerating or decelerating? 

With data loaded into a warehouse via Fivetran, the acquiring company's finance team can model these questions in SQL, connect BI tools for visualization, and share a structured view of the target's financial health with decision-makers within hours of data receipt rather than spending days manually reformatting spreadsheets.

3. Deep dive due diligence

Initial financial diligence answers the valuation question. Deep dive diligence answers the harder question: is the business what we think it is? This phase expands scope across all functional domains — the four P's that determine whether the investment thesis is sound: 

• People
• Product
• Process
• Performance

Each domain generates its own data stream, and the ability to pull those streams into a single analytical environment — rather than managing them as isolated spreadsheets in a shared folder — is the difference between surface-level diligence and the kind of proprietary insight that research identifies as the defining characteristic of the most successful acquirers.

Product and technology diligence examines the target's software architecture, technical debt profile, development velocity (commit history from GitHub or GitLab connectors shows release cadence and engineering productivity), customer usage patterns (product analytics from Amplitude or Mixpanel connectors reveal feature adoption and retention drivers), and the scalability of the technology stack. A target claiming to have a modern, scalable platform whose development data shows increasing defect rates, slowing release velocity, and declining user engagement is telling a very different story in the data than in the management presentation.

Customer and commercial diligence examines pipeline quality (CRM data from Salesforce or HubSpot connectors reveals stage distribution, conversion rates, and deal velocity), customer health (support ticket volume and resolution time from Zendesk shows the operational load the customer base creates), and marketing efficiency (ad spend data from Google Ads, Meta, and LinkedIn connectors shows CAC and channel efficiency over time). HR and people diligence uses headcount data, org structure exports, and employee engagement survey data (Qualtrics connector) to assess cultural health and retention risk. Each of these data streams arrives in a different format from a different system; Fivetran's managed connector catalog covers the vast majority of them without custom engineering.

4. Hit timescales and reduce risk

Between signing and close, the integration team typically has 60 to 90 days to make the acquiring company operationally ready for legal ownership to transfer on Day 1. The primary goal is not yet synergy realization; it is continuity. Products must ship, invoices must be sent, payroll must run, and regulatory reports must be filed. One misstep can mean thousands of invoices going unpaid, customer contracts falling through gaps in reporting, or regulators flagging a failure in consolidated financial presentation. Every integration team working against this timeline is optimizing for one thing: reducing the risk of surprises.

Fivetran's contribution to this phase is speed on repeatable processes. The acquiring company already has Fivetran connectors for its own systems — Salesforce, NetSuite, Workday, Stripe, and the other tools in its operational stack. Setting up equivalent connectors for the target company's systems is not a new technical challenge; it is the same process repeated against a new set of source credentials. If the acquirer has already used Fivetran to connect Salesforce in their own environment, connecting the target's Salesforce instance takes hours, not weeks. The schema consistency that Fivetran provides means that the acquirer's existing dbt models — the transformations that produce the management reporting tables the executive team relies on — can run against the target's data with minimal modification.

The 100-day integration plan is where synergy realization begins: consolidated reporting across both entities, unified customer data, combined financial consolidation, and operational metrics that show whether the integration is on track. Each of these requires data from both the acquirer and target's systems flowing into a common destination — a Snowflake or Databricks environment that serves as the integration's analytical foundation. Fivetran pipelines established during the diligence phase can be repurposed directly for this integration layer, with no re-engineering. The Transitional Services Agreement (TSA) period — when the seller continues to provide IT and operational services to the carved-out entity — creates its own data requirements: the acquirer needs visibility into the target's operations even while they are still running on the seller's infrastructure. Fivetran's Hybrid Deployment handles the case where the target's data sources are behind the seller's network boundary during the TSA period.

5. Consolidation and divestitures

M&A creates two distinct IT and data challenges that look like opposites but require the same underlying capability. Consolidation — bringing two data estates together into a unified operational picture — requires reliable pipelines from both source systems, schema normalization so data from different CRMs and ERPs can be compared and combined and a clear governance model for what the combined entity's canonical data looks like. Divestiture — separating a business unit from a parent company to create a standalone entity — requires the reverse: identifying which data belongs to the carved-out entity, extracting it cleanly from shared systems, and ensuring the divested company can operate independently on Day 1 without taking proprietary parent data with it.

Fivetran's filtering capabilities are the mechanism for divestitures. Every Fivetran connection allows inclusion and exclusion of specific schemas, tables, and columns. When a business unit is being carved out from a larger enterprise, the data engineer configures the connector to include only the schemas, tables, and rows that belong to the divesting entity — using table filters, column exclusions, and row-level filters where the source supports them. The result is a Fivetran pipeline that extracts precisely the carve-out's data from a shared source system, without pulling proprietary parent data that must remain with the seller. 

For a SQL Server database shared across the parent and carve-out, for example, Fivetran's schema selection means only the tables that belong to the divesting entity are replicated to the carve-out's destination, and that selection is managed in the Fivetran dashboard without modifying the source database or requiring the seller's DBA to implement custom views.

For consolidation, Fivetran Activations closes the loop. Once data from the target and acquirer's systems is unified in the warehouse, Activations pushes the consolidated datasets back to the operational tools that each team uses daily. Sales reps in the combined organization see a unified customer view in Salesforce — not two separate CRM instances with different records. Marketing teams see combined audience segments built from the merged customer database. Finance teams receive consolidated management reporting in their financial planning tool without manual data assembly. This is the data activation layer that makes integration visible to the people who need it most: the business teams operating the combined entity, who can now act from a single source of truth rather than reconciling data from two legacy systems that have not yet been migrated.

M&A value is created or destroyed by data

Be it market intelligence built from public data APIs that gives your corporate development team a proprietary view of the sector or financial and operational diligence that moves at the speed of data room updates rather than manual re-processing, deep dive analysis reveals the real picture behind the management presentation. It reveals Day 1 readiness built on repeatable pipeline patterns that eliminate the integration's most common data-driven surprises, clean separations, and unified consolidations that make TSA exits faster and operational integration visible to every team in the combined entity.

The companies that win in M&A are not always the ones with the highest offers. They are the ones whose diligence is faster and deeper, whose integration is more predictable, and whose post-close operational performance more closely matches the investment thesis. Data infrastructure — specifically the ability to connect any source, transform it consistently, and activate it in the right operational tools — is how that advantage is built and maintained.

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