8 Data Best Practices for a Successful M&A

Posted by Katie Mowery on February 27, 2019

Accurate, complete master data is essential for successful integration of acquired assets into an organization’s new or existing operations and maintenance environments. Whether you're leveraging machine learning, data quality tools/software or a team of data experts, there are some best practices that can help guide data integrity and system consolidation strategy for mergers and acquisitions?

  1. Ensure data is part of the timeline. As you consider and define your M&A timeline, realize that data tends to be one of the sticking points. Having a well-defined process and dedicated organizational team to manage process and data components drives the speed and success with which the systems integration can happen.
  2. Get IT leaders involved early. Gartner found that “without early and active participation in the strategy phase of a merger and acquisition, technology business unit leaders may get involved too late, putting the company at a competitive disadvantage and deal synergy at risk.”
  3. Ensure there is strong business sponsorship for getting the data right. This is a critical first step from an asset perspective. Get everyone from engineering, operations and manufacturing to understand that if the organization treats its assets in a governed, managed state, there are specific benefits to the business that make their jobs easier.
  4. Agree on one set of business rules, processes and goals, aligned to master data. For example, ensuring a uniform way to procure and maintain equipment – and having a designated set of data rules and taxonomies to reference the process – will create only one cost structure going forward as well as consistent work orders and maintenance schedules.
  5. Have a “champion” in each department or unit who will enforce decisions. Find a subject-matter expert in each unit or department who is respected. Once a decision is made around a new process or business rule, they can communicate it teams within the unit or department and explain the method behind the madness. This will increase speed of adoption and boost the likelihood rules are followed.
  6. Leverage the tools available. If the organization has a governance program or technology platform already in place, use it as a bridge in the M&A process to acceleration the movement of master data from the old system into the new operating environment.
  7. Seek out low-hanging fruit. Go through the inventory of data assets, where they exist, what systems they’re in, and how they’re relevant to the new operating model. Profile that data to identify any early opportunities or benefits relevant to business drivers. For example, if one of the drivers is to increase customers by 50 percent, use any clean data to shore up existing relationships to ensure the customer experience doesn’t suffer during the M&A.
  8. Have a plan for post-integration. While it’s important to define what “good” looks like now, if an organization doesn’t govern good data, it will be right back where it started in 18 or 24 months. The ideal scenario is a combination of data transformation and data governance, so have a plan in place for how to keep the data sparkling clean once the golden record is created.  

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Topics: Data Quality

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