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Successful Mergers and Acquisitions Don’t Forget About the Data

For years study after study have confirmed that mergers and acquisitions fail to achieve desired results. Part of this is strategy, but an equally big part is execution and, within execution, there’s one reason for these failures that I have seen overlooked time and time again: the business-critical data.

Through my experience with multiple mergers, acquisitions and divestitures, addressing data needs is universally more complex than anticipated. In fact, data has stopped more than one M&A effort in its tracks, and ultimately impacted both results and costs.

Consider these real-life examples:

System-dependent processes: In a quest for synergies, an acquiring company planned to combine service groups. However, these synergies disappeared because the data wouldn’t support it. Even though the majority of their customer base was the same, they were unable to automate identifying over a million duplicate accounts, and therefore couldn’t merge the two groups:

  • One group relied on a home-grown system with a data structure designed to support extending credit to customers.
  • The second company didn’t extend such credits and their data was designed to track service resources and payments.
  • Combining the groups would require a complete redesign of how both service functions and systems utilized data, which would cost more than the synergies this effort was expected to deliver.

Incompatible reporting information: In another example, two merged companies reported financials with fixed cost allocations for different regional structures. When it came time to combine the data across regions, they couldn’t for some key reasons:

  • There was no way to maintain a sales person’s sales history, and thus maintain commission payments, without creating a dummy account in both legacy systems.
  • Order histories were based on regional criteria and separating them into a new regional structure would require resolving allocations that, for one organization, were applied inconsistently.
  • Employees with the subject matter expertise to unwind the pre-defined allocations were eliminated just a few months after the acquisition.

Too much change: In this last case, multiple changes were made to a newly acquired company’s structure and go to market approach which included bringing on third parties as value-added resellers. Unfortunately, the contractual terms were based on incorrect data that resulted in missed orders and penalty payments.

  • Performance and order history were used to predict future sales volumes but the business model had changed so much that the historical data no longer applied.
  • The volume predictions, which were based on untested assumptions and invalid in the new model, were used to set contractual terms.
  • When results didn’t match the model, the company experienced an empty six-week supply chain, missed orders, penalty payments to customers, increased costs for expedited shipping, and a damaged reputation.

Who gets it right? Here’s one success story:

A well-oiled machine: In this instance, Company X spent 6 months with a team of consultants that were hired to test and run the company on a parallel cloned technology platform. They had one team dedicated to data execution within IT and another dedicated to the integrity of business processes with data dependencies. There was also a senior resource with a wealth of knowledge within the business assigned specifically to data migration and validation. When it came time to go live the implementation team was so bored due to a lack of issues that they reduced a 30-day hyper-care period to two weeks! Was it expensive?? Yes!? But there were no unforeseen surprises, delays, or costs.

So what can you do to ensure data integrity supports all aspects of a merger, acquisition, or divestiture?

Consider the following:

  1. During due diligence, be realistic about the data and factor it in accordingly.
  2. Don’t assume you know enough about an organization’s data based on past experiences. Find a way to validate whether customer, cost, or volume data is accurate.
  3. Assign resources specifically to data execution and treat it as a distinct activity that spans the gap between IT and other business functions.
  4. Understand that a merger or acquisition changes the nature of a company, and analytics on historical data are less likely to predict the future.
  5. When it comes to data, success is never an accident. It only happens when you plan ahead and utilize resources familiar with your needs.

Do you have plans to grow your organization in the coming year? Here at Mar Dat, we specialize in the business of data so you can do exactly that.

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