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

Karl´s blog

Posts tagged digitalization
Denkfabrik Wirtschaft

M&A Aktivitäten sind für viele Unternehmen nach wie vor eine wichtige strategische Option zur Steigerung des Unternehmenswertes und des Unternehmenswachstums. Nichtsdestotrotz ist ein Unternehmenskauf aber oftmals eine große und risikoreiche Investition und stellt insbesondere in der Post Merger Integration eine große Herausforderung für Unternehmen dar.

Es lohnt sich, dieser Phase ein hohes Maß an Management-Aufmerksamkeit zu widmen, denn nachhaltige Wertschöpfung erfolgt nicht durch den Kauf an sich, sondern durch erfolgreiche gemeinsame Integrationsarbeit.

Der Jahreskongress PMI des Bundesverbandes M&A und der angegliederte interaktive Design Thinking-Workshop „Denkfabrik Wirtschaft“ beleuchten die kritischen Erfolgsfaktoren und typische Vorgehensweisen bei der Unternehmensintegration:

  • Standardisierung und Best-Practices für den Integrations- und M&A-Prozess

  • Transformation des Integrations- und M&A-Prozesses durch Digitalisierung und
    digitale Tools

  • Kulturelle Integration im M&A-Prozess

  • aktuellste Erkenntnisse aus der internationalen wissenschaftlichen Forschung und Beratung im Bereich M&A und PMI aus europäischer und amerikanischer Perspektive

Die Teilnehmer des Workshops können Ihre Themen und Fragen aktiv einbringen und zusammen mit anderen Teilnehmern Lösungen erarbeiten und haben so die Möglichkeit, von den praktischen Erfahrungen anderer Unternehmen zu lernen und diese für sich zu nutzen.

Zwei kurze Keynotes runden das Programm ab. Dieses Format gibt es seit drei Jahren und es hat hervorragende Bewertungen der Teilnehmer erhalten.

Und das Erlebnis endet nicht beim Workshop, die Teilnehmer können in dem Arbeitskreis PMI des Bundesverbandes M&A weiter mit den anderen Experten zusammen arbeiten und sich austauschen.

Agenda und weitere Informationen finden Sie unter www.mergerintegration.events


M&A digitization: Imagine you can easily review existing tools to automate M&A...

Everybody feels the pressure to digitize. How can you best handle it when you are responsible for the M&A process? Say, you would like to get information which tools are available to automate this task. You could start searching, find some tool vendors, look at a few in more details by coordinating meetings. A tedious process.

Domain model can help

I am working on a domain model, which will be available in an online tool, that does two things: first, define all tasks in the M&A process. Second, it allows to map existing tools to this model, showing which tools are available for which tasks.

What would it look like?

Let us have a look at an example. In the early phase of the M&A process there is a task called pipelining. You want to automate it. This task aims at providing a long list of potential targets and ways to select a subset of targets resulting in a shortlist. So, how would the information about a tool look like? See the information below on a single tool that partially automates this task.

Tool/Service: EY Embryonic
Provider: EY
USP: EY has licensed all expensive databases like CapIQ, CBInsights, ThomsonReuters and has an impressive user experience.
EY Embryonic is offered as a consulting service. 

Great information right? This is just a snippet of the available information. The plan is to list several tools to enable you to select the right tooling to digitize the M&A process.

Stay tuned for more news about the domain model and its many uses. Like what you read? Click on one of the topics or buy one of my recommended books below.

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.

M&A digitalization: Forget data rooms for M&A: what we need is a data lake and a data warehouse during due diligence and PMI

In M&A processes, data rooms are all over the place. They are a storage for unstructured and structured data. But these structured and unstructured data are not up-to-date, not complete and they might even be contradicting each other. They might even be aggregated in a way we don´t know and cannot reproduce and we don´t know the underlying data at all. Not a perfect situation to judge based on the numbers and documents. Making sense of this information is tedious and making decision based on this information is very risky. So, what can we do about it? Let me brainstorm a little about that….

Big data is a no-brainer

There are solutions out there who can easily and quickly analyze wast amounts of structured and unstructured data. They can analyze and interpret contracts and other documents, they can find critical clauses in business documents and find e.g. indications of fraught. They can relate information to get analytics about outlyers in financial data, from which business transactions this outlyer originates and by the way, which employee is responsible and accountable for this business transaction. In seconds. This is not a vision, the technology to do this is there and can be used that way. So we should make use of it.

What is possible today?

No matter if you do the analysis during due diligence (with limited information) or post close (with access to all information), you are able to do automated scans that provide you with the following information:

  • Technical IT landscape: which servers run where and how are they connected, which software runs on which servers

  • Business system information: which ERP systems are running, what is the business structure, through which APIs are the different business systems communicating, which companies are there, how are they interacting, which business models are implemented. You can compare different systems with each other or with a best practice template or to-be system easily.

  • Business status information: which processes are being run, how often and in which speed are they executed, how do they perform and how often are process exception handling activities executed.

To summarize, using these automated tools can increase the level of detail and precision of IT and business due diligence and provide a sound basis for a joint IT and business integration planning as early as possible in the M&A process.

Data analysis and interpretation is just the beginning

Life will be easier. Here´s my vision for next generation due diligence work based on data. Now that you found items that are interesting and you analyzed them in due diligence, you have to figure out what actions to take during due diligence and post merger integration. Machine learning is here to help. Based on a set of earlier acquisitions and the plans for the current acquisition, a machine-learning-based algorithm will propose which actions are required by the buyer or the target and/or proposed clauses in contracts to deal with this situation. Let´s imagine new ways of running due diligence and PMI

In due diligence: just give us access to a data lake of structured and unstructured information and give us access to your data warehouse structure and we can analyze the company structure, the business models and the steps needed to transform the business and to plan the integration of the business with the acquirer´s business.

In post merger integration: In addition to data lakes and data warehouses we have access to business systems details which allow to analyse, optimize, transform the acquired business and automatically get proposals which steps should be taken during the integration phase on a detailed level.

Follow me on twitter @karl_popp or stay tuned for more blog entries on innovations in the M&A process.

Sollen Roboter Beiträge zur Rentenversicherung bezahlen?


Unter Robotern verstehe ich physische Roboter und Software-Roboter, die Entscheidungsaufgaben übernehmen. Software-Roboter basieren häufig auf predictive analytics oder machine learning oder einer Kombination daraus. Mit Hilfe von data augmentation sind die dem Menschen bei der Entscheidung möglicherweise überlegen, da Ihnen mehr Daten zur Verfügung stehen und diese auch schneller verarbeiten können.

Während Sherry Turkle sich mit der Frage beschäftigt, ob ihre Patienten Roboter heiraten sollten, beschäftigt mich die Frage, ob Roboter in die Rentenversicherung einbezahlen sollten.

Warum sollten Roboter in die Rentenversicherung einbezahlen?

Durch Machine Learning nimmt die Automatisierbarkeit von betrieblichen Aufgaben dramatisch zu. Roboter werden stetig zunehmend Aufgaben von Menschen übernehmen. In allen Branchen, unabhängig vom Ausbildungsgrad und ja, auch in Berufen von Akademikern, wie z.B. Ärzten und Anwälten.

In Kürze führt das aus meiner Sicht zu drei Effekten:

  1. zu einer Erhöhung der Automatisierungsgrade aller Aufgaben in Unternehmen. Ich vermute, dass langfristig die Anzahl der automatisierten Aufgaben extrem zunimmt und der Bedarf an Arbeitskräften extrem abnimmt. Das betrifft insbesondere Aufgaben, deren Aufgabenträger Entscheidungen treffen sollen.

  2. Die intellektuellen Anforderungen an die verbleibenden Arbeitnehmer werden stark zunehmen. Diese Arbeitnehmer werden die Aufgaben teilweise oder vollständig übernehmen, die nicht durch Roboter und auf machine-learning basierenden Software-Roboter erledigt werden können.

  3. Die auf machine-learning basierenden Software-Roboter lernen anhand von Beobachtung der in 2. genannten Arbeitnehmer, lernen daraus und werden auch diese Aufgabenträger vollständig ersetzen können.


Die Beiträge zur Rentenversicherung werden von Arbeitenden getragen. Dazu zählen heute auch Roboter in Form von physischen Robotern und Software-Robotern. Deswegen lasst uns dafür plädieren, dass sie auch in die Rentenversicherung einbezahlen.

Ich freue mich über Kommentare. #robotsaresavingus

Digitalization of M&A processes: How to integrate best of breed solutions into one M&A process platform

We have to move forward quickly to disrupt existing M&A processes and get the best innovations to get to a digital M&A process. So here are my thoughts, some might be drafty, but i want to get my requirements out now to ensure we all are facing the right direction for digital M&A.

Requirement: we need several vendors to provide innovations

Can the best innovation for all phases of M&A come from one vendor only? Probably not. So how do companies get the best functionality in a unified, end-to-end M&A platform? The platform has to be open, has to have OData based APIs to allow integration with the best of breed functionality for the different phases of the M&A process.

Requirement: We need a metamodel of end-to-end M&A processes and objects

Thirty years of object modelling for businesses are paving the way to create a metamodel of M&A processes. This metamodel should contain the objects and relationships to be used in the M&A process like buyer, target, companies, which are contained in longlist, shortlist, have relationships with data rooms, documents like contracts, patents, financial data etc. etc. In addition we need

Requirement: Standardization is needed

Establishing a standard metamodel for end-to-end M&A processes is key to success. There are three ways to establish it: via the market or via standardization committees or by creating a winner takes it all market for the end-to-end M&A process platform. it will be interesting to see which vendor chooses which approach.

Requirement: An ecosystem of extensions of the end-to-end M&A process platform

Based on the standardization and the OData-based metamodel, M&A process platform vendors can start to foster an ecosystem of innovations for the M&A process. Today, we would need e.g. the following ecosystem of vendors to engage: end-to-end M&A process platform, data room vendor, company information providers, contract analysis providers, machine learning application providers etc.


With the listed requirements in place, we can move forward quickly to leverage innovations from different vendors. From my point of view, establishing a winner in the end-to-end M&A process platform market is paramount to provide massive innovation to many companies. Several large corporates in Germany are considering to choose such an M&A process platform today to streamline their operations. I will keep you posted if there is one vendor that wins the market or if there are several vendors fighting for larger marketshares at customers.

Like my way of thinking? So feel free to read my book about M&A: M&A due diligence in the software industry. Do also feel free to comment, happy to receive the feedback.

Let us cover the final frontier of digitalization: M&A processes!

While many business processes are automated, use big data analytics and digital assistants, we seem to run M&A processes like it is 1999. Imagining what is possible today, we are on the verge of disruption in M&A.

What is needed?

Here is the list of requirements to massively digitize the M&A process:

  • end-to-end process support from early phases to end of the integration project,

  • Digital learning for M&A knowledge,

  • Semantic analysis of available data of acquirer and target and then leverage the semantic data to navigate the data via assistive technologies, like automatic analysis of legal documents,

  • assistive technologies like chatbots, robotic process automation and digital assistants that help managers watch risks, ask the right questions and propose proper next steps,

  • big data analytics: data rooms are a large data set, so why dig through it manually?,

  • Use of forensic technologies for understanding and investigating data room content

  • Automate IT due diligence by using scanners for analysis of networks, applications and interfaces,

  • Automatically analyse content of existing ERP systems for due diligence, merger integration and migration of ERP systems.

What is already digital?

  • Learning: see PMI2GO: digital online learning for post merger integration

  • Data rooms: Trusted file stores for due diligence are digital. But is file store digitalization driven far enough? No. Not yet.

  • Process digitalization: There are M&A process tools that allow partial automation of management tasks. But do we get digitalization with chatbots, assistive technology based on machine learning? No. Not yet.

The opportunities are massive but are not yet leveraged. I think the M&A community has to provide guidance to vendors to achieve a vision i call the Digital M&A Manifesto. Stay tuned for more details.