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IT due diligence in a merger

Mergers and Acquisitions (M&A) involve a tremendous amount of effort to properly evaluate the situation before decisions are made. Leaders must put together a teams to perform a due diligence effort of the organization. This relies on data derived in large part from the IT organization. If that data is inaccurate or incomplete, the decisions on those efforts could be catastrophic. Regardless of which direction leadership decides to go in regarding the M&A, they need that due diligence to be accurate and complete which can only happen with high quality IT generated data. This means the data IT provides for the M&A assessment is at the core of make or break multi-million dollar decisions.

In the normal course of operation, IT generates foundation data used daily but could also one day be needed to execute an M&A. Once the decision however to go forward with an M&A, IT then needs to step up its efforts to ensure that the data is as accurate and complete as possible before sharing it with leadership. This needs to encompass three core data quality activities:

Core Data Quality Activities

  1. segregation of the necessary data
  2. validation of accuracy & completeness
  3. normalization of what remains

Segregation

Segregation means that only data from those areas of the organization impacted by an M&A need to be made available to leadership and it’s ITs responsibility to provide it. An example, is if the organization being merged with overlaps in only one area of the business. In this case, it’s important to make sure that only those devices of the IT infrastructure which support that line of business are accounted for in the assessment. Data that potentially inflates the amount of duplicate and/or complementary devices could mislead leadership. They could possibly believe that there is a greater cost saving potential than there really is.

Accuracy & Completeness

Accuracy and completeness of data must be ensured after completion of the segregation. Inaccurate or incomplete data could cause leadership to estimate and communicate the wrong details. Information regarding long-term cost savings of a merged company need to be clearly understood. They could also underestimate the actual cost and duration to complete the effort. If either of these occur, the financial well being of the organization could be at risk. This could also cause management to wrongly evaluate the efficiency of the IT organization.  As a result, outsourcing of IT or leveraging a Managed Service Provider could become an option.

Normalization

The third aspect with regard to IT’s due diligence role in an M&A is to normalize the data both before and after the event.  After the event, there are are equally important tasks to those stated above that occur beforehand. To avoid confusion, data from both entities must be viewed and interpreted similarly. IT must make every effort to normalize the data. People in the new combined organization can’t communicate with each other clearly and succinctly otherwise. If individuals are interpreting data differently, it will have a negative impact on business operations.

Conclusion

Mergers and Acquisitions happen all the time and they all rely on high quality in order to succeed. Failed ventures are generally a result in some way of bad data being used to make important M&A decisions. Positive business outcomes will not happen when the business decisions are based on wrong or incomplete data. IT organizations need to be constantly improving the quality of data for daily operations but it must also be ready in the event they are called upon to support a multi-million dollar M&A.

There many tools used by and that can help the due diligence role IT has with master data management (MDM) in an M&A setting. In the IT service management space which I am more familiar with, there are some interesting ones like Blazent and BDNA that are worth a look.  More broadly, I suggest you look into tools in the discovery, analytics and intelligence market segments. If you are looking for MDM tool evaluation help, check out one of these articles:

Six criteria for master data management tool evaluation

Tips for the MDM evaluation process and vendor selection

MDM Best Practices

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