The Key to a Successful M&A = Data

Successful M&A = Data

Data is often the single point of failure for many organizations. Divestitures, privatization, leveraged buyouts, and management buyouts are all on the rise, but data too often remains an afterthought, rather than an asset to be governed.

Many M&A events come from the need to improve agility and innovation. Effective data governance will become both a competitive advantage and the prime opportunity for mergers and acquisitions in the modern age. Why?

Data governance makes the M&A process much more efficient. If you are the acquirer, data governance provides assurance that data assets can be valued and repurposed for your business needs. If you are being acquired, data governance is a prime method for articulating goodwill and demonstrating business and performance value.

Keys to Success for the New M&A

To succeed in the new era of M&A, businesses must promote and encourage data-savvy cultures, commit to defining central policy for data, and generate accountability for data at each level.

The data-savvy culture isn’t just about hiring a team of data scientists or refreshing the employee population with a younger mindset. To ensure an effective M&A event, business owners should tap the most experienced operators to define the processes, rules, and standards that define what data is valuable and how it runs the business. This shouldn’t be considered busy work. This is the opportunity to define the next iteration of the business as a digital enterprise.

It’s vital to centrally define strategy and policy for data. In order for data policy to work, it needs to be dynamic, meaningful to the business, and accessible across the enterprise. M&As are about generating scale, but businesses can’t scale across the new hybrid landscape without a common rhythm to march to. A central data policy creates this rhythm.

Generating data accountability is a pervasive challenge among enterprises with increasingly matrixed organizations. Simple habits within line-of-business practices create process interruptions and then disrupt analytics.

Take a wholesale distributor, for example, that works hard to expedite the introduction of a new customer into its system. The first screen of the system asks for addresses that represent both “ship to” and “bill to” criteria. But there is no way to advance to the second screen until both addresses are filled in. Employees commonly duplicate the addresses when the customer knows one but not both, under the assumption he or she can come back and update it later.

Often before that can happen, an order is returned based on the wrong shipping address, and the business is unsure who was accountable for the error. Was it the author of the data who entered it, trying to improve customer service time and get the order placed quickly? Was it the IT team that runs data quality reports and profiles for duplicate addresses? Or was it the order fulfillment team that should have performed quality assurance and double-checked the most important data attributes of the order before shipping?

Mergers, acquisitions and divestitures all require many things to be successful. They are no doubt highly complex and complexity is often the breeding ground of failure. However, with the proper focus on the value of data from initial due diligence all the way through integration, companies can avoid missteps and account for complexity early on.

By Will Krump

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