M&A, Business Models and Ecosystems in the Software Industry

Karl´s blog

Posts tagged processes
M&A digitalization: We need a domain model for M&A

A domain model for mergers and acquisitions will save us.

The clash of domains

Two companies shall be merged. Two workforces and two administrations have to be aligned, changed and integrated. The first problem that comes up is corporate language and a missing joint domain model and integration plan for people involved but also for applications, companies, locations and countries.

Domain models

Domain models are semantic models that show objects and their relationship in a specific domain. For the M&A domain there will be representations of the M&A strategy, the business case, the integration plan and of course, of both companies to be merged. In addition we need a model of the integration project and all the tasks to be carried out in the different phases of the M&A process. The domain model does not only cover company data, but also data about the M&A process, the involved people and other attributes of the integration like goals and objectives and how they are measured.

Mapping out phases and tasks in the M&A process

Soon , the PMI workgroup of the German M&A association will publish a reference model for all tasks in the M&A process. Here is a short preview of what will be included:

  • Phases: are part of the M&A process like a due diligence phase or the phase between sign and close.

  • Tasks: describe the activities to be carried out during the M&A process. Plus there will be goals and objectives for each task that steer the execution of the tasks and measure success.

  • Concepts: Describe the domain data of the different companies, like companies, departments, the project management domain

Wow, that might be a lot of data types to be modeled, how can you ensure consistency? It all comes together on a task level. Tasks are executed to transform companies to reach an end state, called goal. Goals and objectives will be different by merger and companies involved.

In our case the goal is that the organizations are integrated. Success is measured with two objectives: integration success is maximized and risk is minimized.

task:             execute_merger_integration_project
phase:            merger integration
goal:             buyer and target organizations are integrated.
objectives:       integration success is maximized, risk is minimized.
task description: Resources for the project will be allocated.
                  and the integration project will be executed.
concepts:         target, buyer, organization, budget, project plan, resources

Domain model APIs foster integration of tool vendors

Domain models can be used to drive transparency in the heterogenous world of M&A tools. Tool vendors can easily show the coverage of tasks within the domain model

The description of phases, tasks, concepts, goals etc. will be formalized and can be easily used to generate API descriptions that could be used to integrate different tools in the M&A process like data rooms, project management tools etc. So stay tuned for more progress.


Digitalization of M&A processes: let´s talk about the data

Digitalization of M&A is about data and data analytics, but also about confidentiality, authorizations and access rights.

  • Establishing a clearly defined, phased, end-to-end M&A process with clearly defined tasks and roles in the different phases (seems obvious, but is not yet implemented, esp. in small and medium countries);

  • establishing a higher degree of automation of tasks (like automated analysis of contracts which needs all contracts to machine-readable), an important prerequisite is to have digital data as much as possible;

  • have one large data set along the end-to-end M&A process (to leverage big data analytics) and clear rules which data are safe to be accessed from the following phase.

So what can you achieve if all these prerequisites are fulfilled, here is my vision:

  • combine structured and unstructured data for unimagined insights : you have financial data, but are they solid and trustable? do the revenue numbers projected reflect the existing contracts with customers? In due diligene, by combining structured information (revenue forecast) with unstructured information (text in contracts, information about pipelines in the data room) you can easily compare both to establish additional trust or to ask tough questions.

  • leverage data across phases of the M&A process: there are restrictions which data from due diligence can be used in later phases. But the data that you can use from target screening and due diligence can be combined and compared with data. Current data you are looking at could be augmented with historical data automatically.

  • actionable insights across M&A projects: the data from all phases and all M&A projects can be used to determine actions in a specific situation. Based on machine learning, an automated assistant could propose what to do, what has been done in other projects in similar situations, could propose who to talk to to leverage the lessons learned from other projects.

So the call to action is: Unite your data on and end-to-end platform to build the foundation to leverage the data for better insights, better execution and more success in M&A processes.