A lackluster integration process is often to blame if a merger or acquisition doesn’t create new value for a combined entity. However, despite the prevalence of the problem, the dreaded post-merger integration (PMI) failure still slips under far too many corporate radars.
Thankfully, just a few insights and best practices on data and systems integration can transform inadequate PMI into a dynamic value driver, maximizing synergies for a new combined entity.
Start With a Framework
Successful M&A transactions don’t fall out of the sky fully formed. Instead, they depend on multiple entities efficiently converging into one, no easy task given the separate people, customers, data, information systems, and processes.
Therefore, one of the items highest on a typical post-merger to-do list should be consolidated reporting. Without it, decision-makers are largely flying blind. However, given the disparate systems and data repositories involved, leadership must first answer some critical questions:
What components and systems are involved in the combination?
Who and what is involved in combining those components, systems, and work streams?
What intermediate steps are needed to run the two companies and roll them into one business model?
What and when is the convergence for the entities?
Further, without foresight and effective change management guiding the way, something as crucial as consolidated reporting could slip through the integration cracks.
To avoid such miscues, the parties should focus on creating a framework to lead the post-merger efforts, ideally well before a deal closes. Such a framework should focus on four main areas:
Establishing communication channels across teams
Creating and appointing particular people to a steering committee, integration teams, and a project management office
Engaging with experienced third parties to assist and guide the integration
Defining the desired end-state — assigning PMI targets, goals, and relevant metrics
Creating fast, reliable systems for critical functions like consolidated reporting, identifying and generating operational key performance indicators (KPIs), or staying on top of debt covenants suddenly becomes infinitely more difficult without that framework in place. Fortunately, a sound data integration strategy addresses many, perhaps even most, of these common integration pitfalls.
Key to Value-Driving Efficiencies
It’s far too easy for a deal to fail without a data integration game plan because of disparate data and disconnected systems. However, choosing the right approach to data integration depends on several variables, including available resources, timelines, and complexity, to name just a few. Still, most transactions are best served by one or a combination of three solutions: in-house teams, automation tools, and outside specialists.
Thanks to the self-sufficiency involved and potential implementation cost savings, a DIY approach to data integration is usually appealing. Thankfully, business leaders aren't lacking in possible solutions to merge and manage data pipelines running through different people, departments, and offices, even ones spread across a large footprint.
However, such a strategy requires specific in-house expertise to identify the right tools, implement them, and then integrate the tools into the combined organization’s processes. Along the way, entities risk losing key people and critical insights when timelines compress, patience thins, and stress levels rise.
A self-directed, in-house-driven strategy is usually better for smaller, more specific PMI projects that don't require a massive amount of manual, long-term heavy lifting or in-depth skill sets.
Businesses increasingly rely on automated solutions to bridge data chasms in M&A transactions. As a result, automation technology is becoming essential for making sense of vast amounts of disconnected information and transforming it into insights for leadership.
For example, automated solutions shine when a transaction requires merging customer data between a SaaS-based platform and a transactional, on-premises database for management reporting. Assuming the parties identify and implement the best automation tools for their needs, the result is scalable, transparent, and reliable.
As importantly, automation provides real-time business intelligence to leadership, usually through an app or web-based data dashboard on a mobile device.
Many organizations now turn to specialists to identify, implement, and maintain the proper data integration tools for specific needs and goals. Business leaders don't have to worry about choosing the wrong tools or relying on in-house teams for critical roles.
Instead, the right specialists familiarize themselves with even the most complex data environments and particular integration needs and establish a roadmap to get to that ideal future state. Of course, implementation costs with a third party could be higher than a DIY approach. Any decision should include budgetary restraints, in-house capabilities, and operational complexities.
However, an effective PMI game plan isn’t simply about hitting the ground running. It should also focus on long-term strategizing, including using the best data management tools and finance transformation initiatives for the combined entity — ERP, CRM, and robotic process automation, to name three.
Stacy Galligan is managing director of business advisory firm Embark.