In this blog, we discuss the challenges that can arise after a merger or acquisition and why you should prioritize data and analytics to ensure a successful deal, as well as how to develop a plan to integrate and centralize data after the M&A activity finalizes.

Integrating data after an M&A transaction will not only allow you to address some of the critical data challenges that arise after a transaction, but it will also allow you to gain more value from the deal.

White and blue graphic outlining six steps to consider when making a plan to integrate and centralize data after a merger or acquisition, including evaluating data maturity, starting with data integration right away, starting with sales use case, centralizing data assets in one repository, data governance, and user adoption.

After a merger or acquisition, it is critical to have a plan in place to integrate and centralize data ASAP.

Mergers & Acquisitions: The Data Challenges that Emerge

Integrating two or more companies together is no small feat, and data-related activities often take a back seat to other crucial operational integration activities in the early stages post-merger or acquisition.

In the absence of reliable integrated systems, processes, and data, emailed spreadsheets and PowerPoint decks become status quo, which leads to numerous challenges:

  • Inability to Be Proactive: A lack of timely and actionable information restricts the ability to respond to issues and opportunities. The amount of change combined all at once with a lack of data can cause paralysis in decision-making.
  • Bad Customer Experience: Brand reputation can take a hit when data quality issues affect your ability to serve customers after a merger or acquisition. This creates doubt in your customers’ mind—leading them to question if you can still deliver on promises and meet their needs.
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  • Wasting Valuable Time and Resources: Change management is hard enough without the lingering effects of bad information, duplicate efforts, and delays created by manual processes. Poor data management results in wasted time and resources.

It is critical to see the forest for the trees during a merger or acquisition. Having quality data is key to maintaining unbiased visibility into organizational performance; analytics is the best way to keep a strategic eye on success criteria and capitalize on opportunities quickly.

The complexity around managing disparate data—specifically harmonizing existing data ecosystems across companies—can be an incredible challenge due to the varying degrees of data maturity that exist. You need a plan for how to bring the right data into a central place and then transform that data into valuable insights.

Talk to an expert about your data integration needs. 

A Plan to Integrate Data and Analytics After Merger and Acquisition Deals


6 Steps to Integrate Data During Mergers and Acquisitions

Consider the following steps when developing a plan to integrate and centralize data after a merger or acquisition:

Evaluate the data maturity of each organization.

No two organizations are alike. Quantify the data maturity of all entities in the M&A activity to set expectations and start planning your data activities. Ask questions like:

  • What skills do the teams possess in each organization?
  • What systems, tools, and technologies are already in place?
  • What data is available today at the organization, and how is it collected—as well as what is the level of detail, quality, and timeliness?
  • How does the organization approach decision-making today? Is it by gutfeel or is it data-driven?
  • What people, processes, technologies, or data overlap across organizations? Where are there gaps?

With this information, best practices from across the blended organization can be used to balance and elevate data maturity.

Learn more about “How to Develop a Data Strategy Roadmap to Modernize Your Data and Analytics

Don’t wait for systems integration to start data integration.

At initial phases of M&A activity, it is important to not conflate enterprise technology migrations or consolidations with the ability to independently improve analytics capabilities. They can be addressed at different times.

Analytics capabilities can often be matured more quickly than enterprise technology integrations can take place. To start getting your analytics in order, ask questions like:

  • What information is critical to understand the health of each business area?
  • Is the data of sufficient quality today to make decisions and act when opportunities arise?
  • What would the value of visibility create in business integration activities and change management?
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Start with sales data to kick off your data integration initiatives.

Customer and prospect data is often more readily available than data from other business activities. Use this data to unlock quick insights and gain momentum on your data integration initiatives post-M&A.

Ask questions such as:

  • Are there customers that should be targeted for cross-selling only where one organization is active?
  • Are there customers that are candidates for collaboration where multiple organizations are already active?
Learn more about “How to Use Your Sales Data to Develop a Customer-Centric Sales Approach

Centralize data assets into one main repository.

You need a data catalog to do preliminary analysis of your next use cases.

Determine a plan for housing all data assets under one central repository in an unmodeled state and make it a one-stop-shop for your data team. No one should have to figure out how to connect to a mainframe or an emailed spreadsheet to do analytics—bring those data assets into a modern cloud data platform instead.

Ask questions like:

  • What subject areas are captured in equal detail and quality across organizations?
  • Are there opportunities for improvement in data collection?
  • Who are subject matter experts that can provide access to data resources quickly and reliably?
  • What frequency and by what processes is the data updated?

Begin to harmonize and govern data assets.

Establish a data warehouse where you can model disparate data together across business lines. At first, this will likely be very high-level information while you improve data maturity. With proper data modeling, you will be able to gracefully extend the model as new data assets become available in your central data repository.

Ask questions like:

  • What are common business dimensions across our organizations (customers, products, employees, and vendors)?
  • What are common business processes across our organizations?
  • What is similar about those business dimensions and processes?
  • What is different about those business dimensions and processes?
  • Are there any opportunities for standardization?
Learn more about “8 Steps to Start Your Data Governance Program

Consider user adoption before building.

Consider who the end users of your data and analytics should be by prioritizing business use cases with a data strategy roadmap. Executives require different information than practitioners, so ensure your plan meets the needs of your target audience.

Use the pre-built “usage dashboards” that come with analytics tools to measure adoption and identify potential analytics champions across the businesses.

Ask questions like:

  • Who are the people that need data for visibility?
  • Who are the people that need data for action?

Mergers and acquisitions present many challenges, but even more opportunities. A data strategy helps mitigate the risks that come with significant change and unlocks opportunities for significant growth.

Do not wait for full businesses integration to start the process of data integration. Start asking the right questions and moving incrementally, and you’ll generate valuable insights quickly.

Tony Dahlager Tony is Analytics8’s Managing Director of Data Management, leading sales, marketing, partnerships, and consulting enablement for our data management service line.
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