We are continuing to see strong growth in mergers and acquisitions (M&A) activity across the UK. According to PwC, deal values rose 37% in 2024, led by financial services, technology, media, telecoms, and services. UK deal volumes also outpaced the wider EMEA region. Datasite and Mergermarket data revealed that nearly 20% of the EMEA companies for sale at the start of 2025 - 299 in total - are based in the UK and Ireland.
This ongoing surge is likely to highlightIt’s the need for more innovative data architectures – especially in supply chain organisations with complex global operations. If newly merged businesses want to deliver for their owners and achieve competitive advantage through AI and increased automation, then the old playbook is out.
The need for cutting-edge technology in supply chain operations will become more obvious as the next 12 months roll on. In a review of M&A in the logistics sector in 2024, management consultants McKinsey said companies that cannot keep pace with the necessary investment in digital capabilities should invite acquisition because having the right technology confers “daunting” competitive advantage.
M&A activity always presents significant challenges with the integration of different systems. In the supply chain sector, operations are by their nature geographically dispersed and involve many partners. Integration is vital, but the challenges can be acute.
Businesses within this sector involved in mergers are going to find integration difficult if their data is distributed across numerous locations within their organisation. It is often under the firm control of separate departments who put it into a format that suits their own specific purposes.
In the current climate where decision-intelligence and AI are critical to greater supply chain efficiency and innovation, this is a significant problem. Without uniform data governance and the technology to bring all this data together in a universally clean and standardised format, major advances such as AI and decision-intelligence are near impossible.
Ad hoc solutions that have been rigged up to provide some sort of data pipeline for one purpose are insufficient if supply chain organisations are to become truly data-driven and achieve their potential in orchestration and optimisation. Reacting to one particular requirement at a time is not the way to operate. But neither is signing up a bunch of data scientists to harmonise and analyse data.
Not only is this inordinately costly, it is also slow and unresponsive. Line-of-business teams in supply chain management need their own access to data so they can get fast answers to the business questions that matter to them. Teams need self-service access to analytics. To enhance their decision-making in the face of disruptions, they need predictive and prescriptive insights extracted from the automated analysis of near real-time data. This data is streaming in from a huge variety of sources including supply chain partners, market and third-party data providers along with news feeds.
To access these advanced capabilities, line-of-business teams should not have to refer to IT technical staff, or chief data officers. The chief supply chain officer needs to have all this at her or his fingertips. Frontline teams should have a fully-managed self-service gateway that enables them to get the information they want rapidly.
To achieve this, newly-merged organisations need more innovative architectures so they can bring together all the data they need and harmonise it. This obviously must include the data that is in existing siloes which may be spread around the globe in a wide variety of formats among subsidiaries or departments that have never worked together and must quickly achieve a high level of integration to start delivering better results.
The most effective approach is the smart data fabric, which is perfectly suited to the challenges of merging supply chain operations. Without the installation of any new systems the smart data fabric will clean and harmonise data from any location, enabling the use of automation to streamline the necessary processes.
Automation also ensures data conforms to data governance standards so that everyone trusts the information they are looking at. Improved data governance through automation is also a major benefit in terms of regulation and reporting, especially in relation to customer data.
There is no copying of data in this approach, which enables the use of embedded analytics and provides the clean data fuel for AI and decision-intelligence. The advantages are that instead of reacting to disruptions and opportunities, organisations can foresee them and taken action to get ahead and prepare thoroughly, achieving gains in optimisation and orchestration.
In a highly competitive market these are major benefits overcoming the hurdles to integration and streamlined operations in newly-merged organisations. As 2025 progresses, we will see how organisations that do away with the old data management playbooks stride ahead.
