By Gary Allemann, MD at Master Data Management
In recent years across the globe there has been a substantial amount of consolidation spanning various industries, from multiple acquisitions by Google and consolidation of manufacturing in many areas to the recent local buyout of Avusa by the Times Media Group. Mergers and acquisitions are typically driven by opportunities to increase revenue, for example cross selling into each customer base, or to increase operational efficiency by leveraging economies of scale in the new, larger business. However when it comes to the actual integration of two disparate companies, organisations often focus exclusively on the commercial perspective, attempting to leverage synergies between the businesses while other areas such as data are left by the wayside. The reality though is that data is a critical component in the success of any merger or acquisition, and a comprehensive data strategy it vital in ensuring a smooth transition and expedient realisation of business goals.
There are many high-level considerations to take into account, and any acquisition is fraught with complexities. Consolidation in any market is typically as a result of a need to remain agile and competitive. However, without data integration, a sound data strategy and data quality initiatives, this agility is difficult to achieve. This is particularly true in a situation where a company either buys out another or attempts to merge two separate entities. Whenever a merger or acquisition takes place, business objectives need to shift and data needs to support these business objectives.
The challenge lies in integrating data from the two entities, as if this cannot be successfully achieved data within the new merged entity will be incomplete or inconsistent, leading to compromised business decision-making, potential non-compliance with data legislation and even degraded customer service levels. In order to ensure the success of mergers and acquisitions, it is imperative to define frameworks and methodologies around data, linked to the business goals of the organisation.
From a data perspective, the challenges around integration are the same for any organisation, regardless of industry or vertical sector. Companies often have vastly different business processes, as well as different financial and legal constraints and different data management practices. If data quality is poor it can have negative implications for the newly merged organisation, and this needs to be addressed. The integration of the business data from the acquired company needs to take into account the legal and financial constraints of the acquirer, and must also incorporate the acquiring business’ best practices, data standards and business rules. This helps to ensure that data quality is maintained before, during and after the integration, to guarantee business continuity and to reduce business risk.
Data integration is often forgotten in the process of a merger or an acquisition, as a result of the ever-present disconnect between business and IT. Often, business will assume that this is IT’s problem, and IT will think that this should be a part of the business side of the acquisition. This means that frequently data conversion and integration is an afterthought and is not handled effectively, which can have significant consequences for a business. For example, if payment data is inaccurate, payments can be delayed, the data must be reworked which takes time and costs money, and organisations can end up in bad debt situations. If data is incomplete, financial reporting will be inaccurate which has several consequences of its own, including legal implications.
In order to address these issues, key business rules need to be identified and applied to avoid business impact during data conversion. Data integration is critical to the success of a merger or acquisition. If the data is not consistent with or does not support the business rules, it becomes increasingly difficult for an organisation to meet and achieve the business case of an acquisition in a timely fashion. A data excellence framework, which encompasses common processes, best practices and common methodology around data quality, data integration and data governance, provides a step-by-step approach to the integration of data to enable acquiring companies to fully leverage on the expected value of an acquisition.
The business-driven rules of a data excellence framework ensure the successful and effective integration of data by providing data quality metrics, exception management and thresholds for data quality after integration to enable smooth future operations. Business rules are defined as rules which data must comply with in order to execute business processes, addressing areas including accuracy, completeness, consistency, duplication and obsolescence. The data excellence framework also ensures that a practical approach to data is taken that does not unduly delay matters, by focusing on critical business rules. For example, rather than seeking 100% quality, which may not be achievable given the acquisition timelines, the optimal data quality level can be determined and targeted to balance integration, quality, governance and given time frames.
Key components of such a framework include data management processes which are interlinked with business, standardised data structures, defined best practices, a standardised data integration platform, and increased levels of ownerships and best practices in business functions. It offers a toolkit for managing change, particularly related to data, within an organisation.
A core function of the data excellence framework is to provide a simple, straightforward process governing data quality business rules as well as to link data quality objectives to their impact on business, to ensure that data integration initiatives can be prioritised accordingly. The framework also defines roles needed for effective data governance and accountability, and enables the execution of multiple data integration projects in the same timescale.
Ultimately the success of the business aspect of mergers and acquisitions relies on the successful integration of data. A detailed data excellence framework and best practices along with a data governance model that includes both business and IT are critical in ensuring this success. In today’s data driven environment, business strategy can only succeed if it is supported by data, which means that high quality data is vital to a merged or acquiring organisation’s sustainability and growth. Data is a business asset, and particularly in the case of mergers and acquisitions should be treated as such.