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MDM, Data Governance • 10 min read

The hidden Data Trap behind M&A

Timothy Peeters | 17-06-2022

When done right, M&A boosts growth and dominance, breaks into new markets and increases innovation and relevance. Small wonder it takes a big chunk out of the management’s time and focus. A key factor often overlooked is the quality of data, which can break or make your M&A.

Over the past year, mergers and acquisitions (M&A) have picked up a considerable pace. The number of announced deals in 2021 exceeded 62,000 globally, a sharp 24% rise from 2020. According to PwC's Global M&A Industry Trends: 2022 Outlook, 2021’s M&A activity was fueled by demand for technology, and digital and data-driven assets. Companies sought to acquire technology capabilities to transform their business models and increase value generation. 

Digital due diligence

One of the biggest shifts in mergers and acquisitions (M&A) has been the digitalization of due diligence. Meeting with the management team online and touring plants and warehouses with drones and robots, kept M&A during the COVID-19 crisis not only possible but made it faster and more cost-efficient. Digital data rooms made the company data readily available to assess the commercial and legal risks and opportunities. Moreover, data rooms also give a key insight into the state of data management at the company you are considering acquiring or merging with.

One of the key indicators of a successful merger or acquisition is the degree by which the companies will manage to integrate. Typically, the first focus lies on merging the front offices to quickly be able to sell new products. In the back end, however, the IT systems and their ways of working often stay apart for too long. Failed IT integration is regularly cited as one of the big reasons why M&A fail because it leaves out one of the biggest assets a company has: data.

BRACE YOURSELF, DATA IS COMING

When two companies become one, this transformation happens on many levels. Customers, employees, offices and locations, suppliers and, of course, products are put together. These different domains each have their own processes, systems, structures and policies. Before you can process an order through the new company, the underlying data that enables the transaction needs to align in all the processes and systems. The quicker you have these data domains integrated, the faster you can create value from the merger. However, when there has been little attention to data management during due diligence, this will become a hard pill to swallow afterwards. Especially when one of the companies brings a lot of data silos or even paper to the table.

Realizing data is the crux of a successful merger or acquisition means including data governance in your due diligence. To do this effectively, it’s essential to understand the enabling role data has for the strategic goals your company may have. Utilizing the best supplier contracts for the whole company, improving product information to increase e-commerce revenue and entering new markets by cross-selling are typical goals that are quickly achievable once the underlying data domains are integrated. So, how do you do that during M&A?

THE SIX STEPS FOR MERGING DATA

Getting the most out of your M&A means supporting your buy-and-build strategy with data governance. There is a huge potential in merging data, but there are also pitfalls that can swallow both money and resources. Follow these six steps to ensure a thorough approach to your data management during a merger or acquisition:

1. Set up a data management team
Start by making data management a C-level responsibility. Set up a data team headed by someone from the business and that consists of people from both organizations and from various disciplines covering IT, data and business.

2. Perform a data management audit
The next step is for the data team to audit all the business areas and departments involved, including its data stakeholders. This means indexing and validating all the data sources and silos. The goal is to describe the current data landscape and the future data model. Ideally, this is done during due diligence and not after the deal is done.

3. Choose a data classification system
Next comes setting the data classification system which will be used by the new organization. For product data, for example, this system will form the basis for matching and linking product data.

4. Select a master data management system
A master data management (MDM) system identifies, links, acquires and synchronizes data from a variety of internal and external sources. If there is no MDM system being used by either previous organization, selecting an MDM system should be on top of the agenda.

5. Choose data governance procedures
Now there is a strong need for governing the way data is collected and processed. This calls for data governance policies and procedures to secure accountability, instill accurate reporting, manage compliance and ensure transparency.

6. Migrate the data
Finally comes the migration of all the data. This should include a methodology check, capability checks for hardware, software and resources, a data security plan to avoid data breaches, a recovery plan and a go-live plan.

These steps need to happen in one way or another, whether you choose to merge or connect the IT systems. It’s essential, however, to see data management separate from the IT infrastructure to ensure it gets the full attention it deserves.

SPEAK THE SAME LANGUAGE

As companies differ in their customer strategies, work processes and cultures, they also differ in their terminologies. Consider, for example, the definition of a customer. Is it someone you are selling to as a sales representative? Or someone you target with your marketing campaigns? Or is it the company that you invoice? This difference in understanding happens even across departments within organizations. Realizing the importance of working with a single definition is the first step toward creating golden records: single versions of truth of all the data entities in one organization. This is how you prevent Babylonian confusion.

This is the second blog of our article series on Data Governance. Click here for the next article.

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Timothy Peeters

Data Governance Expert

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