What is the job to be done? The job to be done is a concept invented by Clayton Christensen in his book "Competing Against Luck: The Story of Innovation and Customer Choice". It is a new way to look at the needs of customers and why they are "hiring" a product to fulfill their needs. The key concept is to focus on the customer and to avoid the viewpoint of the product. By doing so, you get a wider view what the needs of the customers are, what the customer should hire to help him and who your real competitors are.

How does it influence tool design? As soon as you know the job to be done and the context of the customer, you are able to design a product or service that has maximum value for the customer. As mentioned in an earlier blog, the context of an M&A professional is his office, the work environment on his desk, his smartphone, desk phone and computer. An M&A process platform must respect and enhance this work environment, not add another tool. So let us use this approach to define two requirements for M&A process tools.

From tool to pain reliever: One pain i heard most from fellow M&A professionals is to fill the same data like target valuation data into several different Powerpoint presentations which have different formatting but basically should reflect the same data. So an M&A platform must store the financial data of a business case and generate data into different powerpoint templates. An end-to-end M&A process platform should have a data management component for the financial data of the transaction that can intelligently export parts of the financial model into presentation formats.

From tool to productivity boost: assistive technology helps you to perform better. The pain of the M&A professional is that he has to research market and company data, bring them together and evaluate the opportunities. How can an end-to-end M&A platform help here? Market data feeds are provided automatically for business case creation. The platform offers research as a service data feeds to accomplish that.

Summary

End-to-end platforms supporting M&A processes are the basis for the digital future of M&A processes. The job-to-be-done approach helps to define service of these platforms. These value creating services, which are built on top of this platform, help M&A professionals to get their job done. Stay tuned for more or meet me at Platform Economy Summit in Berlin in November.

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.

Summary

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.

The European Workshop on software ecosystems will be held as part of the Platform Economy Summit in Berlin, we will have two sessions on the second day of the European Platform Economy summit.

November 21st

11:15am Challenges and success factors for creating digital platforms

14:30 Network Effects & APIs: Their role in driving platform value

The first session is called “Challenges and success factors for creating digital platforms”, moderated by me: Insights from studies, real life projects and Uberization“ and will feature three short motivating presentations by Peter Buxmann, Thomas Curran and Sebastien Dupre followed by topic-bases workshops.

Peter Buxmann, Head of Software & Digital Business Group at Technical University of Darmstadt, will present the topic “Data Economy, Platforms, and Privacy: Insights from multiple empirical studies“. He will provide insights into challenges and success factors for software platform providers regarding the value of customer data, customer privacy and tradeoffs between data privacy and data farming by platform providers.

Thomas Curran will present the transformation of a financial industry heavyweight to becoming an open, digital platform. In a traditionally closed industry, what do you do to turn a company into a digital, open platform. Thomas has done just that in a three year project and will talk about how to do that successfully.

Sebastien Dupre from Coresystems (now SAP) will present the topic “Uberization of field service: a software platform for crowdsourcing service technicians and show how companies can build an ecosystem connecting field service technicians, partners, own employees and customers to scale their field service operations, increase revenue and provide unmatched customer experience.

The second session in the afternoon is called “Network Effects & APIs: Their role in driving platform value “ and will be moderated by Slinger Jansen - Software Ecosystems Research Lab, Utrecht University. It will focus on questions like “What is the role of APIs for platforms? How do you build API-based platforms?  What are the success factors and pitfalls when building API-based platforms? How to explain their power to non-technical executives and shareholders?”

The session will start with a short introduction about APIs in general by John Nethans from Google. Then Slinger will present the essence of latest research on API approaches. After that, the panel will focus on pragmatic aspects of creating successful API platforms. After a short while, the panel will open up and take questions from the audience.

This session will feature the following speakers:

Slinger Jansen - Software Ecosystems Research Lab, Utrecht University

John Rethans - Head of Digital Transformation Strategy, Apigee, Google

Nik Willetts - President & CEO, TM Forum

Andreas von Oettingen - CTO Factor10

This session will start with short statements from the panel and will transition to a discussion with questions from the audience.

hope to see you there. please make use of discounted tickets as of below.

Dr. Karl Popp

Join now and you get a special 15% discount off the booking fee. Just quote the discount VIP Code: FKN2652EWOSEL to claim your discount.
 
For more information or to register for the Platform Economy Summit Europe, please contact the KNect365 team on: Tel: +44 (0) 20 3377 3279 | Email: gf-registrations@knect365.com | Register here.
 
Remember to quote the VIP code: FKN2652EWOSEL to claim your 15% discount.

Machine learning is everywhere - except in M&A processes. Let´s change that. Let us imagine the impact of machine learning in different steps of a typical M&A process. Let us start by sharing some of my ideas to trigger your imagination. I am convinced that the technologies needed to achieve this vision are in place today, they are just not being used in this context.

Early phases of the M&A process, shortlisting phases

Let´s say you have five companies in your shortlist. Machine learning can help finding and selecting potential targets e.g. by predicting which of the companies considered will be the unicorn, i.e. the most successful company in the list. Approaches for doing that exist, e.g. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3159123

Preparing the Letter of Intent

Based on past projects, machine learning can help to predict deal breakers, find missing or potentially wrong data in the financial valuation of the target and propose deal structure and clauses for the letter of intent based on the existing, available data about the target and the acquirer.

Due diligence

A vital part of the job to be done in due diligence is that you are looking for missing data, for deal breakers and risks in documents in the data room BUT you only have limited time and a huge data lake in the data room. So let us see how automation and machine learning could help us here.

Day one of due diligence: the data room is available. Day 2 of due diligence: Information about missing data, deal breakers and risks is already available.

How is that possible? Using automated document/contract analysis based on machine learning as well as data about deal breakers and historic projects, a machine learning application can provide this information. There is a huge value in this: you get more time in due diligence to work on missing data, for deal breakers and risks, so quality of due diligence results will massively increase.

No more reporting: During due diligence, digital assistants will automatically keep the lists of tasks, risks, issues and results, will create automatic reporting from that and propose next steps.

Merger integration

Results from the due diligence are automatically distributed digitally to all integration team members. Machine learning based digital assistants propose the integration plan, the integration timeline and which next steps should be taken. They analyze due diligence data and propose the set of data that should be doublechecked and validated. They validate that data by extracting information from the target´s ERP systems automatically and present deviations in digital dashboards and propose next steps.

Learning assistants analyze the learning needed by the involved integration managers based on their CV and proposes digital learning lessons based on PMI2GO.

No more reporting: During due diligence, digital assistants will automatically keep the lists of tasks, risks, issues and results, will create automatic reporting from that and propose next steps.

Let us imagine the impossible - and make it work

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. Like this article to get more inspiration!

While we are used to physical robots vacuuming our homes, software robots are not in widespread use yet. The term used for software robots is robotic process automation. (RPA)

What is RPA? 

RPA is defined as tools to build automation for everyday tasks and processes  on a computer screen using Software Robots.   This can start with a simple sequence of clicks on the screen that you can replay automatically. But RPA can also cover more complex workflows with decision points. RPA  tools usually contain a recorder that tracks  certain work sequences on your computer screen and can replay it this sequence later.

What is RPA combined with machine learning? 

Recording workflows with current RPA tools is a manual process. If combined with machine learning, a digital assistant will track your online work and will propose automation of routine processes you do every day. This will lead to a step by step increase of the level of automation in processes.

How does RPA help in M&A processes?

 It frees up time to focus on the really important topics instead of routine tasks  and sequences of clicks on a computer screen.

Corporate M&A teams don´t have the time and bandwidth to research and follow up with a number of vendors and service providers to get an overview of the latest and greatest innovations for M&A processes.

To solve this issue within one afternoon, Xperience Connect organized an event at Frankfurt School of Finance last week providing several pitches of innovative products and services for next generation M&A processes.

So twenty-two corporates met to have a look at ten vendors, 15 minute pitches by the vendors helped getting an overview within an afternoon, followed by a joint dinner to discuss.

Here are my four highlights of the afternoon:

Target screening

• an interesting presentation from a researcher how to reduce the number of potential targets based on acquisition goals, they also use an augmented set of company data. This is a startup in stealth mode but they presented anyway…

Automatic contract analysis

• RR Donnelley, a vendor of data room called Venue, showed their product eBrevia, which is a tool to automatically analyze contracts in many different languages based on machine learning.

• eBrevia contains about 150 provisions it is able to find and analyze, customers can build AND share new provisions with other customers if they like to.

• eBrevia can be used with Venue, but also with other data rooms.

Digital valuation

• PWC has an open platform for valuation of startups, https://www.pwc-evaluation.com/

Smart M&A

• Midaxo did a very interesting presentation of their innovative, cloud-based, end-to-end M&A process platform.

• With this platform, all parties collaborate seamlessly following repeatable, systematic processes based on their specific, corporate playbooks.

• Several large corporates, including Daimler and Philipsh have adopted this solution.

Thank you, Stefan Gerhard Schneider for organizing this event. He offered to have follow-up meetings with deep dives, which was well received by the corporates.

If you like this content, please also have a look at www.digitalmergers.com

The emergence of platforms is significantly changing the organizing logic of software

development. Platform owners are increasingly engaging vibrant ecosystems around

their platform to foster third-party innovation. Despite all the potential benefits for

complementors, however, innovation in platform ecosystems also introduced essential

new risks that remain under investigated.

Find more information here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3245777

Talk about it and discuss at Platform Economy Summit https://goo.gl/GAJmg1

The proceedings of EWSECO are out now! The European Workshop on Software Ecosystems http://www.ewseco.org is an annual event which connects researchers and fellow professionals in the field of software ecosystems.
Presentations in 2017 included:

1. Software M&A Ecosystems -- Industry Keynote by Julis Telaranta, Corum

2. Sandbox vs. Toolbox - Analyzing boundary Resource in B2B Software Platforms -- Maximilian Schreieck, Robert Finke, Manuel Wiesche, Helmut Krcmar

3. Manage multiple platform-ecosystems -- Christopher Jud, Georg Herzwurm

4. Survival of the smartest: Digitalization of mechanical engineering companies by creating a software ecosystem -- Industry Keynote by Benjamin Müller, ADAMOS GmbH

5. Building your IoT ecosystem: Proposing the Hybrid Intelligence Accelerator -- Dominik Dellermann, Nikolaus Lipusch, Philipp Ebel, Jan Marco Leimeister

6. The European Standard on eInvoicing EN16931 - Applications and Services to implement Directive 2014/55/EU on electronic invoicing -- Industry Keynote by Seeburger AG

7. Platform Business in Application Markets: Data Analytics of Mobile App Usage and Descriptions -- Lauri Frank

8. Fake it till you make it: how to bootstrap an ecosystem before your company is ready -- Alexander Eck, Benjamin Spottke

9. Strategy definition in large enterprise software ecosystems -- Ralf Meyer

BUY IT NOW

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.

An end-to-end, unified platform builds the foundation of M&A success. End-to-end means that the platform covers all phases of the M&A process from early strategizing to deal sourcing to due diligence, signing, closing and integration. All data are combined to one single source of truth, no data are lost between phases, better and well documented handovers are possible between phases.

Unified platform means no more jumping between different solutions and tools. t eases the pain of processing massive amounts of data, be it the data room or planning data for integration planning. For due diligence, this includes combining collaborative due diligence management with virtual data room capabilities.

While there are many advantages of such a platform, let´s just look at three key advantages.

Advantage 1: A unified data lake for all deals

The data lake covers all process phases and all deals allowing e.g. cross-deal analytics, large training sets for machine learning, proposals of next steps based on best practices from all deals. The data lake contains massive amounts of information, but all information used in the process, information about the process steps and decisions taken is stored in one place.

The load of information in M&A processes is already overwhelming? So how can i leverage this large amount of data? Modern information system technologies like predictive analytics, finding outliers within data, semantic analytics and forensic tool to analyse and navigate large data sets as well as providing the right information for your current work context will enable you to leverage the data collected.

Advantage 2: Better decision are being taken and documented

There are two aspects of this advantage: decision journey and augmentation. For each decision taken, you can always recall the decision journey. How was the decision prepared, who took it, what were the consequences, were the goals of the decision reached?

The second aspect is augmentation of decision tasks: if you are the decision maker, augmentation provides you with similar decision taken in other deals including their impact on results in the integration phase, so you can make the best decision. The augmentation in the deal sourcing phase e.g. includes market data, financial data about all targets and predictive analytics about the future success of the target companies.

Advantage 3: Less documentation and reporting: More productivity

Massively increased productivity and less errors due to robotic process automation. No more learning of process models, they will naturally be followed. No more thinking about what the next step is or what your project status is, all information is augmented in your usual workplace. Reporting annoys you? The platform will autmatically propose the content for the next status update, so you spend less hours on reporting, more on quality work and problem solving.

Outlook: where´s the platform?

So, now we know some of the advantages of the platform: one question remains: is the platform your work environment and do you have to learn a completely new work environment that does not naturally integrate with all the other productivity tools that you are using: email, teleconferencing etc.? The platform i envision will be invisible, you will work in your usual work environment, e.g. using a Windows tablet with Outlook and other tools you know. the platform will track your work and augment inforrmation as you work, no separate login, no missing integrations that get on your nerves.

If you liked this article, you will like my book about due diligence.

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 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.

Our home cleaning person retired. This was the final motivation to start additional robotics in our home.

The Roomba 960 was easy to install. remove two tabs, plug in the charging station. done.

The Roomba worked well on all types of floors: wooden and tiles and all of our carpets: thin, thick, wool, plastic, everywhere.

We have 100 square meters to clean and the roomba needs one charging cycle in between to finish, which is not a problem since the roomba finds its charging station and restarts automatically as soon as it has finished recharging. We set a schedule to clean every second day automatically.

 

The roomba recognized the stairs everywhere and avoided them.

Overall a very good performance

things to be aware of  are:

• cables: cables on the floor will easily be eaten. just remove them
• thin curtains that reach the floor: we have one thin curtain that reaches the floor. well, the roomba started eating the curtain. now we just pull it up when the roomba starts.
• some voices say, black carpets will be avoided by the Roomba. We don´t have any so we cannot comment on this.
• that´s it.