According to a study published by JP Morgan, the value of global M&A deals amounted to $4.1 trillion in 2018 and a total deal count of 2342 deals. Companies tried to leverage these opportunities to innovate core business models, maximize growth and mitigate technology disruption. However, one thing is certain; every merger and acquisition doubles the Information Technology (IT) infrastructure that must be managed and maintained to support the new organization. The total data management responsibility is enormous. Without an effective enterprise information management strategy and early IT involvement to plan for integration challenges, the merger or acquisition may be doomed to failure. The inferior quality of data infrastructure can degrade and significantly slower down the M&A process.
In most M&A deals, the management fails to involve IT in an earlier stage, overlooking the risks and costs of IT integration. Apart from failing to realize the value of data intelligence, what are other typical challenges encountered when no harmonized and streamlined data are in place?
- Unforeseen costs: According to Gartner research, “the average financial impact of poor data quality on organizations is $9.7 million per year.” The crux of the problem with M&A growth is that business-critical data becomes fragmented and scattered across applications. The sheer diversity and complexity of the data infrastructure drives costs, bad reputation and lost opportunities.
- Duplicate data: Duplicates might lead to incorrect perception of business performance with suppliers, customers, trading partners, and products, which results in incorrect decisions. The INSTITUTE OF INTERNAL AUDITORS suggests that duplicate payments make up between 0.5% and 0.1% of annual invoice payments. It means that for an average company with 1 billion USD in payables, duplicate payments result in 1 million USD.
- Fraud risk: Master data management ensures access control, which segregates the roles and responsibilities of those who create data and those who consume this data across business functions. The absence of either access control processes or master data management poses a high-risk of fraudulent schemes.
- Lack of data ethics: Regulations are growing stringent and require compliance in reports and documents, published by an organization. Ability to perform auditing across end-to-end business processes and standardizing compliance requires established practices in master data management. Such practices ensure accuracy and consistency of data, used across all business domains.
The right data strategy and execution can ensure smooth execution of business processes after a merger, spin-off, divestments or acquisition, drive economies of scale, and maximize productivity. But first, that means getting your data infrastructure and data right, that drive operating practices within both companies. A holistic data management approach using data-driven and modern applications enables a 360-degree view of all data available.
CAMELOT provides M&A data advisory solutions across the strategic life cycle of our clients.
Strategic assessment in the due diligence phase
- Evaluation of target’s IT assets and data capabilities
- Assessment of fit to acquisition from data perspective
- Potential synergies and costs of data integration
Enhancing business value
- Recommendation of M&A IT infrastructure concept, increasing speed-to-data intelligence
- Preparedness to avoid unforeseen costs
- Change impact assessment and risk mitigation measures
Realization of data gains post M&A
- Design and implementation of integrated data infrastructures
- Data harmonization across information domains such as products, vendors, finance etc.
- Adherence to stringent regulations and standardize compliance across domains
Why CAMELOT
Our bespoke solutions combine:
- In-depth knowledge of industry sector, regulated environments and data dynamics of M&A process
- Ability to partner with diverse data technology providers and customization of solutions to fit your business needs
- Cutting edge solutions to automate data migration processes and accelerators to maximize the speed of data infrastructure readiness
