Technological advancements are driving mergers and acquisitions (M&As) at an unprecedented rate. Companies aim to extend their market reach, acquire new technologies, and achieve cost synergies through these deals. For instance, in 2023, nearly 40,000 mergers and acquisition (M&A) deals were completed worldwide.

For a successful merger, companies should make enterprise data management a core part of the due diligence phase. This provides a clear roadmap for addressing data quality issues, identifying integration challenges, and assessing the potential value of the target company’s data.

Data quality in M&As is essential from the initial phase to its completion. During the integration phase, maintaining consistency and high quality in data is key for seamlessly merging systems, processes, and cultures. For the post-merger stage, leveraging data is important for optimizing operations, evaluating performance, and achieving the expected mutually advantageous conjunction.

Accurate and reliable data are important in the context of M&As for conducting thorough due diligence, assessing the target company’s financial health, identifying potential compatibility, and determining the deal’s value.

Why Data Quality is Crucial for M&A Success

Data quality means ensuring that a company’s information is precise, complete, consistent, timely, and relevant. Organizations need to maintain high data quality in M&As to merge operations smoothly or transfer assets.

High-quality data provides the foundation for informed decision-making, efficient integration, and long-term value creation.

1. Accurate Valuation and Deal Structuring

High-quality financial data is essential for accurately valuing a target company. It underpins accurate valuation by providing a reliable basis for financial analysis and forecasting future performance. Inaccurate or incomplete data can lead to overpaying for a business. Reliable data also helps in structuring the deal terms, such as the purchase price, payment terms, and earn-out provisions. Solid financial data ensures that both parties have a clear understanding of the deal’s value and potential returns.

2. Effective Due Diligence

Due diligence is a thorough examination of a target company. High-quality data is crucial for uncovering potential risks, liabilities, and opportunities, as it reveals hidden patterns, inconsistencies, and anomalies in data. Accurate financial, operational, and legal data enables buyers to make informed decisions about the deal. Poor data quality can lead to missed red flags and unexpected costs after the deal closes.

3. Seamless Integration

Merging two companies involves combining different systems, processes, and data. High-quality data is essential for aligning and integrating these disparate systems. Clean and consistent data accelerates the integration process, reduces errors, and minimizes disruptions to business operations.

4. Identifying Synergies and Cost Savings

A successful M&A deal depends on realizing mutual compatibility and cost savings. High-quality data helps identify opportunities for improvement by providing insights into operations, customer behavior, and market trends. Accurate data enables companies to optimize their combined operations, reduce costs, and increase revenue.

5. Risk Mitigation

M&A deals involve inherent risks, like valuation errors or integration challenges. High-quality data helps identify and assess potential risks, such as regulatory compliance issues, legal liabilities, and operational challenges. By understanding these risks, companies can develop strategies to mitigate them, like contingency planning and due diligence enhancements.

Role of Data Quality in the Stages of M&A

Data quality is about accurate, complete, consistent, and timely data. In mergers and acquisitions, high-quality data supports every stage of the M&A process, from the initial valuation to integration after the merger.

1. Pre-M&A Preparation Stage

Before starting a merger and acquisition process, it’s essential to check and confirm the accuracy and reliability of the data. Identifying data issues early on or verifying data accuracy is key to supporting valuation decisions and preventing problems. Detailed and reliable financial statements, operational metrics, and customer data enable a comprehensive understanding of the target company’s performance. This information is essential for:

• Valuation Accuracy: High-quality financial data ensures accurate valuation and prevents overpaying for the target company.
• Risk Assessment: Detailed operational data helps identify potential risks and challenges that can arise post-merger.
• Synergy Identification: Accurate customer data and market analysis can reveal potential synergies and growth opportunities.

To ensure the accuracy and reliability of data, thorough validation is essential. Key validation methods include:

• Data Consistency Checks: Verify data consistency across different data sources and systems.
• Data Completeness Checks: Ensure all necessary data elements are present and complete.
• Data Accuracy Checks: Compare data against reliable external sources to verify accuracy.
• Data Timeliness Checks: Assess the currency of the data to ensure it reflects the current business situation.
• Data Format Checks: Verify that data is in a consistent format and structure for analysis.

2. Mid-Merge Integration Stage

The mid-merge stage represents the period between the announcement of a merger or acquisition and the full integration of the two companies.

Organizations should prioritize high data quality during the mid-merge stage as it helps in:

• Master Data Management (MDM): High-quality data is essential for creating a single, authoritative source of truth (master data) across the combined organization. This includes customer data, products, employees, and other critical entities.
• Data Cleansing: Identifying and correcting data errors, inconsistencies, and duplicates is a key to successful integration. Clean data ensures accurate and reliable information flows between systems.
• Data Standardization: Establishing standard data definitions, formats, and structures is vital for seamless data exchange. Data standardization minimizes discrepancies and improves data compatibility.
• Data Transformation: Converting data from one format to another is often necessary during integration. High-quality source data ensures accurate and reliable transformed data.
• Data Migration: Moving data from legacy systems to new platforms requires accurate and complete data to avoid data loss or corruption.
• Data Validation: Verifying the accuracy and completeness of migrated data is essential to ensure data integrity.

3. Post M&A Performance Monitoring

Enterprises can speed up their decisions on new assets if their core data is clean, stable, and well-governed. Implementing effective ongoing data observability becomes quick and easy when the necessary steps are taken before and during a merger.

During this stage, organizations can prioritize data quality by:

• Establishing Data Governance: Implementing clear data ownership, stewardship, and quality standards. By defining these factors, organizations ensure that data is managed consistently across the combined entity. Data governance is critical for performance monitoring as it provides a framework for tracking data accuracy, completeness, and consistency over time. Transparent data governance can improve data quality, leading to accurate performance metrics and guided decisions.
• Conducting Regular Data Audits: Identifying and correcting data quality issues. Organizations can pinpoint areas where improvements are needed by regularly assessing data accuracy, completeness, and consistency. This helps to maintain data quality over time and ensures that performance metrics are based on reliable data. With regular audits, data quality can be maintained, leading to accurate performance measurements and suboptimal decision-making.
• Investing in Data Management Tools: Data management tools automate data cleansing, validation, and standardization processes, significantly improving data quality and efficiency. These tools help to identify and correct data errors, inconsistencies, and duplicates, ensuring that data used for performance monitoring is accurate and reliable. Without data management tools, manual data cleaning and validation processes can be time-consuming and error-prone, impacting the overall quality of performance data.

M&A Data Quality Challenges

Data quality is important for achieving success in mergers and acquisitions but achieving high data quality is a task that is fraught with challenges.

Data Inconsistency

Different departments and systems often use varying data definitions, formats, and standards. This data inconsistency makes comparing and integrating data from other sources difficult, hindering accurate analysis and decision-making.

Data Completeness

Data gaps and missing information are common in many organizations. Incomplete data sets limit the scope of analysis and can lead to inaccurate conclusions. Identifying and filling these gaps is time-consuming and resource-intensive.

Data Accuracy

Errors and inaccuracies in data can have significant consequences for M&A deals, like missed liabilities and integration failures. These errors can arise from human input, system glitches, or data transfer processes. Ensuring data accuracy requires validation and cleansing procedures.

Data Timeliness

Outdated or stale data can provide a misleading picture of a company’s performance. Businesses demand timely and up-to-date information for effective decision-making.

Data Security and Privacy

Protecting sensitive data during an M&A process is crucial, as data breaches can damage a company’s reputation and lead to financial losses. Implementing security measures while facilitating data sharing is a complex challenge.

Benefits of High-Quality Data in M&A

High-quality data is essential during an M&A deal and in the crucial post-merger integration phase. It has multiple benefits throughout the entire M&A lifecycle.

Streamlined Data Management

High-quality data is instrumental in breaking down data silos and streamlining data management processes, particularly crucial in M&A. When data is consistent, accurate, and complete across different systems and departments, it becomes easier to integrate and manage. Organizations can create a unified data environment by establishing standardized data definitions, formats, and quality metrics. This facilitates efficient data sharing, analysis, and reporting, improving decision-making and operational efficiency. In the context of M&A, breaking down data silos is essential for a smooth integration process, as it allows for a comprehensive view of the combined entity’s data landscape.

Faster Integration

High data quality accelerates integration by ensuring consistent data formats and standards across both organizations. Accurate data on customers, employees, and operations streamlines the alignment of systems and processes, minimizing disruptions and allowing quicker collaboration.

Improved Investor Confidence

Building trust with investors and stakeholders is essential for M&A success. High-quality data demonstrates a company’s commitment to transparency, accountability, and financial integrity. This transparency builds investor confidence, making raising capital and attracting strategic partners easier.

Improved Financial Performance

High-quality financial data supports accurate financial reporting, budgeting, and forecasting. By identifying trends, cost variances, and revenue opportunities, businesses can make informed decisions to improve profitability. Additionally, reliable financial data is essential for investor relations, creditworthiness, and compliance with financial regulations.

Enhanced Customer Experience

High data quality allows for a unified customer view, combining data from both companies. This accurate and complete picture enables tailored marketing campaigns, personalized product recommendations, and consistent customer service interactions. By understanding customer preferences and behaviors across different touchpoints, businesses can deliver superior experiences, increasing customer satisfaction and loyalty.

Conclusion

Data quality is undeniably a central characteristic driving the entire M&A journey. From the initial stages of due diligence to the ongoing post-merger performance monitoring, accurate, consistent, and complete data provides a foundation for informed decisions.

Organizations usually seek comprehensive solutions to navigate and streamline data management in mergers and acquisitions. Astera offers a unified suite of data management tools designed to simplify the entire M&A process. With built-in data quality capabilities, Astera’s 100% no-code UI makes data validation, cleansing, and profiling easier for every business user. Organizations can use Astera to handle large-scale data transformations with a few clicks, while its data consolidation capabilities create a unified view of organizational data.

Schedule a demo today to see how leveraging Astera’s solutions can accelerate high data quality and achieve optimal outcomes from mergers or acquisitions.