Make Data a Strategic Priority in Your Post-Merger Integration

Philipp-Andrin Sgier, D&A PwC Switzerland

Philipp-Andrin Sgier, D&A PwC Switzerland

How do you avoid chaos within your data operations and instead benefit from the new data assets you have acquired? PwC applies its data strategy framework to support its clients in extracting value from data in the process of post-merger integration: strengthening data operations through synergies and enabling future data-driven growth. 

Drawing from the insights of the annual PwC Switzerland M&A industry trend survey, we see that the Covid pandemic has impacted M&A activities across various industries. The challenging business environment and the insecurity regarding future industry trends increase the risk associated with transactions. Hence, it creates the need to make M&A deals a value-adding success. While different strategic considerations drive M&A activities, data has emerged as a leading asset for businesses. This bears potential to transform industries, strengthen business operations, and create additional value in M&A that goes beyond adding the pieces of two companies together. PwC supports its clients in maximising the value gained from well-designed data operations and thus provides guidance to incorporate relevant transformation activities in the extraordinary process that M&A and a post-merger integration pose.

'Our clients start making data a C-level priority in their Post-Merger Integration considerations. In uncertain times such as a pandemic, companies are pressured to make M&A a success story.'

Matthias Leybold, Partner, Data & Analytics Leader, PwC Switzerland
M&A success story

Case Study: Post-M&A Data Strategy of a Swiss Company in the circular economy industry


We supported our client, a mid-sized Swiss company founded in the early years of the 20th century, in redesigning its data & analytics operations in the process of a post-merger integration. The company is a hidden champion in the circular economy industry. After starting as a traditional family business, it pursued an inorganic growth strategy from the early 2000s onwards. This involved several large-scale takeovers of other family businesses as well as private equity backed companies which resulted in a multiplication of its operations. Due to the business’ growth, the IT and data resources were exposed to new capability needs and a substantial increase in workload, revealing structural and capability gaps in the client’s organisation.

Defining a future vision for value from data

As a response to the client’s need to reshape its data handling efforts, PwC conducted a wide-ranging assessment of the client’s approach to working with data using our proprietary data strategy framework. This involved scrutinising the As-Is situation and deriving a categorisation of the current maturity level compared to industry standards. Furthermore, PwC mapped out the To-Be state based on the target maturity level to provide the client with guidance for advancing its data operations and showcase resulting benefits (e.g. maximising value resulting from M&A, enabling new business models, strengthening business resilience). The in-depth recommendations involved enhancements in governance structures, process redesigns, and a reference data architecture.

Leveraging the Data Strategy Framework to create value from M&A

PwC leverages its data strategy framework to address and resolve data challenges in the process of a post-merger integration. This approach is based on PwC’s outside-in research which provides a guiding framework to structure data operations. It combines the expertise of 20+ data experts covering industries such as finance, pharma and life sciences, automotive, manufacturing, and the public sector across four continents. The study’s insights are gathered from 50+ relevant companies with the following profiles:

In order to ensure a holistic coverage of all data-related challenges within a company, the data strategy framework clusters its approach into six dimensions ranging from technology to people topics. These are further broken down into 27 capability subdimensions, forming actionable decision spaces. The wide-ranging impact of M&A activities on the involved business units matches the multi-dimensional view of the data strategy framework. In order to showcase how PwC leverages the data strategy framework to offer its clients support with data challenges during a post-merger integration, this article provides a deep dive on Data Governance as well as on Data Architecture and Quality related scenarios.

Enterprise Data Management Framework

Data Governance

While the Data Governance dimension of PwC’s data strategy framework sheds light on multiple aspects such as the data catalogue or the data access philosophy, we identified the changes of roles and responsibilities as a result of M&A activities as a central challenge in the process of a successful post-merger integration. Generally, even a well-defined and established data governance structure experiences disruption during M&A. In terms of roles and responsibilities, this results from three possible, non-exclusive scenarios:

  • the need for defining new temporal roles and assigning responsibilities for tasks that only exist during the post-merger integration process (e.g. data engineers focusing on the integration of data sets from the two companies merging);
  • the need for roles and responsibilities with permanent existence handling the growing workload as well as reflecting the new company structure; and
  • the need for roles and responsibilities that existed before the merger and have now become obsolete (e.g. duplication of efforts). Moreover, the sudden growth a business experiences through M&A, oftentimes underlines the drawbacks of unorganised Data Governance structures that existed already prior to a merger.

'Data Governance is a key aspect of PwC’s data strategy approach as it sets a foundation for people's trust in data.'

Berit C. Gerritzen, Director, Data & Analytics Strategy Lead, PwC Switzerland
Trust in data

Case Study: Clear Data Governance Structures Build the Basis of a Successful Integration


Already prior to engaging in M&A activities, PwC’s client had only organically-grown structures in the data governance field. This was due to the company never having performed an appropriate analysis of its data governance needs and a structured allocation of roles and responsibilities within and across the company’s divisions. Following the company’s M&A growth strategy, structures and processes became more inefficient and existing problems had a bigger impact on the company’s business as the workload multiplied and existing resources were not capable of compensating for missing roles anymore. This resulted in an increase in project delivery time and a decrease in project delivery quality, which both reflected negatively on the company’s trust in data as a strategic asset to support decision making.

Reducing uncertainty through clarified roles and responsibilities

In order to transform the company’s data governance set-up and specifically the composition of roles and responsibilities, PwC performed an As-Is assessment of the existing allocation of tasks and accountabilities. This covered both formally and informally established roles and responsibilities of the client’s company. Based on this analysis, PwC identified gaps and captured organisational needs, benchmarking them against industry best-practices and PwC research resources. Subsequently, PwC designed a To-Be situation for its client’s roles and responsibilities set-up, including guiding principles for actionable implementation steps. This was facilitated by compiling a high-level organisational structure for the client’s data governance organisation as well as by deep-diving into the definition of role-specific job requirements. This allowed PwC’s client to materialise synergies from its M&A activities by reducing double efforts and clarifying data and analytics responsibilities, which resulted in streamlined dataflows and better value generation from existing and newly acquired data assets.

Data Architecture and Quality

Globally, there are a large number of dominating IT providers of ERP systems, CRM systems, cloud services etc. This diversity is also reflected in the systems deployed at companies engaging in M&A. After considering strategic choices such as keeping IT systems separated to maintain flexibility for potentially spinning off business units, synergies - and thus extensive value - can be realised from integrating the IT landscape.

When pursuing this ambition, two challenges arise: firstly, the reduction of duplicate systems and data, and secondly, the scaling of systems. To address this, a suitable data architecture builds the foundation for subsequent considerations of a platform landscape and more tactical issues such as the management of data quality. In recent years, cloud services have taken a bigger stand in data architectures, accelerating the integration after a merger and preparing for future flexibility. This stems from owning and maintaining less hardware as well as from having an un-capped potential for future scaling. The usage of cloud services in post-merger integration is particularly beneficial due to its potential to unify multiple previously existing systems both in storage capacity as well as in connectivity to platforms. This also facilitates the strategic choice to potentially create a single source of truth while maintaining a high level of security.

'When it comes to speeding up business processes and strengthening overall business resilience, our work with clients across industries shows us the value of an integrated Data Architecture.'

Berit C. Gerritzen, Director, Data & Analytics Strategy Lead, PwC Switzerland
Data Architecture

Case Study: Integrated Data Architectures Promote Synergies and Accelerate Operations


PwC’s client had no formalised data architecture before engaging in M&A. The complexity was further increased by acquiring other companies that used different IT systems which resulted in the usage of multiple ERP systems (built inhouse and bought externally), multiple CRM tools that were partly integrated into the ERP, different financial data systems for different locations, and the manufacturing operations running entirely without a standardised IT solution. On top of the existing systems, the company’s employees unofficially developed a large 'shadow IT' to compensate for gaps in official applications. This situation accounted for a duplication of data storage, maintenance, and analysis. Furthermore, dataflows were unspecified which led to lengthy processes in identifying the correct data assets, retrieving the desired datasets, and performing data analytics.

Enhanced data quality through state-of-the art cloud data architecture

PwC mapped the currently used systems across the company identifying duplications that resulted from the mergers. This also included an identification of shadow IT solutions used by individuals and entire departments. Together with the client’s Head of IT and Head of Digital Transformation, PwC designed a To-Be state of the data architecture providing state-of-the-art industry references and insights on latest technology solutions in the cloud space. This enabled the client to address data quality issues resulting from bad architectural design (e.g. adaption of critical data quality changes across systems) and tackle the integration of duplicate data sources and data sets. Addressing data architecture in the course of the post-merger integration reduced costs for the client and improved IT solutions, which resulted in faster and more thorough data analytics.

PwC Supports Your Post-Merger Integration Data Strategy

Approaching the integration of data-related functions, infrastructure and assets strategically after a merger offers potential to create value beyond adding the pieces of the previous companies together. This is emphasised by the fact that solid data operation acts as an enabler for new business models, data products, and data-driven decision making which strengthens business resilience. Applying the PwC data strategy framework gives guidance in taking a holistic view which not only allows to ensure a successful post-merger integration but also tackles previously undiscovered problems in data operations. Hence, making data a strategic priority in post-merger integration directly translates into added value for your business.

We would be keen to understand how we can help you get value from your data and support you on your post-merger and integration journey, and are very much looking forward to hearing from you.


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Matthias Leybold

Matthias Leybold

Partner, Cloud & Digital, PwC Switzerland

Tel: +41 58 792 13 96

Joscha Milinski

Joscha Milinski

Partner and Data Strategy & Management Leader, PwC Switzerland

Tel: +41 58 792 23 58

Nina Wolf

Nina Wolf

Senior Manager Data Transformation & Analytics, PwC Switzerland

Tel: +41 79 193 07 00