5 Essential Data Governance Considerations for Successful Mergers & Acquisitions

September 5, 2019

6 minute read

Data governance needs to be a top priority when entering into a merger or acquisition. Here are five key considerations to make.

Data governance needs to be a top priority when entering into a merger or acquisition. Here are five key considerations to make.

As many couples can attest, merging two households isn’t easy. Whether it’s agreeing on the correct way to stack the dishwasher—or where to “store” your partner’s coffee mug collection—a successful union involves compromise and communication. Business marriages—i.e. mergers and acquisitions—are no exception. With so much money, time, and trust on the table, it’s a race to start generating value as soon as the ink dries on the agreement. Just as combining households presents challenges, so can marrying two businesses—especially in the realm of data governance.

Marrying data governance strategies might be one of the biggest challenges standing in the way of producing meaningful results after a merger or acquisition.

Successfully consolidating data, just like merging companies, can resolve risks and reveal opportunities. As enterprises approach this challenge, here are four factors to consider:

5 data governance steps to take to ensure a successful merger or acquisition.

5 data governance steps to take to ensure a successful merger or acquisition.

1. Unify Disparate Data & Processes

Ask any pair to agree on the correct way to hang a toilet paper roll, and you’ll soon realize that everyone has their own way of doing things. Bringing together two different data governance strategies is no exception.

When two companies come together, they will each have their own systems and guidelines for data governance. But merging two businesses requires transforming two disparate ways of handling data into a unified framework, including taxonomy, processes, and systems. The two businesses may also possess redundant, obsolete and trivial (ROT) data—which must be identified, cleansed, and consolidated into a single consistent record. But all of this is unlikely to be easy or quick.

In addition to challenges around determining the prevailing strategy, businesses may also face cultural resistance to new ways of doing things. The sooner data from both companies can be identified, enriched and classified, the sooner they can extract business intelligence and deliver value as a unified team.

2. Settle on a New Data Governance Strategy

Considering the vast amounts of data most businesses generate, creating a cohesive data governance strategy and standardizing legacy data could take years. After an investment as steep as a merger or acquisition, most can’t afford to wait that long to start banking returns.

The time and effort required to consolidate data must be a major consideration in approaching the merger. It’s crucial to quickly settle on a single new strategy to speed the availability of data and start migrating to the new order.

3. Identify Hidden Risks

As privacy legislation continues to strengthen with GDPR and the upcoming CCPA, overlooked and unaddressed personally identifiable information (PII) is among the biggest threats that can result in serious financial penalties and reputational damage.

With GDPR and CCPA in full force, data governance becomes a high priority as organizations face hefty fines and consequences for non-compliance.

With GDPR and CCPA in full force, data governance becomes a high priority as organizations face hefty fines and consequences for non-compliance.

From a compliance standpoint, PII is a common (and risky) operational blindspot. However, unprotected and overlooked PII is just one of the problems that may be lurking unaddressed.

When it comes to data governance, it’s tough to see the forest for the trees, and many enterprises overlook the actual documents themselves. Additionally, most organizations don’t consider the colossal effort needed to ensure that their data assets are usable—and that they’re identifying the potential value (and risk) that they contain.

Take a financial services organization that possesses thousands of unstructured contract documents strewn across repositories and fileshares, for example. Without a unified data governance strategy in place—or an effective means of finding, enriching, classifying, analyzing, and migrating the relevant data—both parties face unprecedented compliance and legal risk.

Beyond compliance sanctions, the risks contained within contract documents could cause significant financial harm to the buyer in the form of lost revenue and unprofitable business decisions. If these compliance and business risks were known and not disclosed prior to the sale, the seller could also face legal and financial blowback.

Undoubtedly, being unaware of the risks within inherited data doesn’t offer exemption from the potential fallout. As such, it’s critical to take quick action to surface, extract, enrich, and analyze the data in question—and swiftly contain the threats within it.

4. Reveal New Opportunities

The goal of a marriage is that two entities will come together as something greater than the sum of their individual parts. But two people can’t become the best versions of themselves if their value remains unrecognized. The same rings true in a business marriage where untapped insights is hidden within reams and reams of organizational data.

For example, two corporations coming together may each possess disparate customer data that, once successfully merged, could yield more robust and sophisticated insights than either data set could produce on its own. A pharmaceutical company, for example, could pool data from legacy trials, reducing the cost and effort of redundant work and identifying new possibilities for existing drugs.

Across industries, the potential is endless. For organizations to reap this vast value—enhancing R&D, developing new products and services faster, and accessing new IP—data consolidation under a single data governance strategy is essential.

5. Put Enterprise-Grade Software to Work

There are tools that can help enterprises identify (and act on) the risk and value buried within their unstructured data—a chore that would take years to complete manually. Once the two organizations have settled on a unified data governance strategy, they should adopt a software solution that will:

  • Eliminate redundant, obsolete, and trivial (ROT) content clogging up fileshares and repositories.
  • Automate Document Conversion to transform all relevant documents into a collection of searchable, digital assets.
  • Utilize Progressive Classification to organize, prioritize, and leverage the data that exists across the entire organization.
  • Automate Data Extraction to extract insights from unstructured data.

Wrap Up

While there are many important considerations in the race to drive value after a merger or acquisition, data governance must be a top priority. Whether they wish to uncover hidden risks or reveal new opportunities, the sooner businesses can get their data in order, the sooner they will realize meaningful results from a new corporate marriage. That sounds like a match made in Data Governance Heaven.

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