Business Consolidation

Business Consolidation


David Hane has recently published an article on the challenges of post-merger integration programs.

M&A is big business. According to Refinitiv, worldwide M&A activity totaled US$3.9 trillion in 2019. Of that, there were 43 deals alone worth over US$10 billion during the year (totaling US$1.2 trillion). Yet for all of this activity, how much value is actually created? Countless studies have documented the success rate for such transactions as ranging between 10% to 30%. With so much at stake, how can this be?

Integrations Gone Awry

No one factor is driving these outcomes. In some cases, the acquirer simply buys the wrong company. For example, the buyer’s strategic intent may be to acquire a business that will help them reinvent their business model. Unfortunately, identifying such candidates is extraordinarily difficult and most buyers aren’t able to pull this off. When you’ve acquired the wrong company, no amount of integration expertise will rectify the situation.

A more common factor driving M&A failure, and one that I’ve seen firsthand, is a botched integration. One of the easiest ways to derail your acquisition integration is to ignore the people dimension. Having an explicit plan to account for the people impact in an integration is also known as change management. You could fill a crater the size of the Grand Canyon with all of the information about change management. Don’t read all of it. Just know that you need to be deliberate and thoughtful in how you plan to integrate organizations.


Points covered in this article include:

  • The consolidation of three businesses
  • Why progress grinds to a halt
  • Encouraging buy-in


Read the full article, A Requiem for M&A: The Best Laid Schemes o’ Mice an’ Men, on LinkedIn. 


Data scientist and psychologist Tobias Baer (who recently published a book on algorithmic bias) is giving a talk on how to prevent algorithmic bias in the U.K. on Tuesday, 11 February 2020. 

Algorithmic bias can affect us everywhere, from minor trivia such as our social media feeds to critical decisions where bias can wreak havoc with a person’s life dream or a company’s survival. Sources of algrorithmic bias are manifold – some, such as biased data and overfitting, sit squarely in the domain of data scientists themselves, while others only can be tackled by the business users and government agencies who use algorithms, be it through carefully crafted experiments that generate truly unbiased data or through deliberate tweaks of the decision-making process.

 The discussion will include:

  1. The psychological and statistical sources of bias
  2. What business users and data scientists can do respectively to manage and prevent algorithmic bias.
  3. How regulators should think about algorithmic bias

To learn more about the event, visit: