In our previous post, we discussed effective M&A cloud integration and how cloud can help in two specific scenarios: acquisition of a small target and a merger of equals. Continuing this train of thought, we’re looking here at where cloud can help with data integration in M&A, to go a step beyond and solve consolidation challenges.  

We know cloud can be used to host and accelerate a ‘clean room’ environment, which is part of neither of the merging organizations but is often required in banking. These locations quickly offer the ability to join data from both organizations for purposes such as evaluating financials, merging customer data for overlaps, and ensuring data quality during the various merger events, in line with regulatory demands. 

In particular, three categories of data—financial, customer and reporting—are strong candidates to leverage cloud within M&A integrations, as they can require more complex data transformation and modelling than, for example, product and transaction data. 

It’s worth noting that the maturity of your data governance and operating model is a key consideration before moving forward. The benefits that cloud provides are heavily reliant on the governance process and the operating foundation, and it will either introduce or reduce risk in the categories listed above. If data governance is still in development, cloud providers and partners can provide tooling (MDM, data lineage, DevOps, etc.) as well as process guidance for enhancements. 

Now, let’s dive deeper into these three data categories to highlight how cloud can help solve their key challenges within the M&A journey.  

Financial data consolidation 

The key challenge: Post Legal Day 1, the combined organizations need to report a consolidated set of financial statements.  

This challenge is a legal requirement, calling for extreme care and precision. Most often, there’s also a very short timeline if merger milestones are to be met.  

The solutions can be temporary until the charts of accounts are migrated into a single instance as part of the post-merger integration activities. Here are two to consider:    

1. Transformation / mapping of the data from one company using the cloud, and feeding it into the reporting systems of the other company.
2. Feeding information from both companies into the cloud and then running the reporting. 

Both approaches allow for a temporary environment that leverages the “pay as you go” flexibility of cloud, as well as running necessary cloud solutions to manipulate data and meet reporting requirements. 

This also enables organizations to spin up the necessary environment(s) quickly, as dictated by a Legal Day 1 milestone, without incurring sunk costs. 

Customer data quality  

The key challenge: Customer data in multiple systems.  

This challenge is magnified during M&A. For example, what one company refers to as a banking business customer, another bank may call a commercial banking customer. The result could be that the data from two essentially identical groups is reported into different profit centers, while the overlaps that are common in banking relationships may become more numerous.  

It also matters who claims ownership of and has responsibility for the clean-up of this data, as this can complicate things further. It can increase the risk of confusing communications and even processing errors during the transition. An early focus on customer data quality can minimize these risks.   

Cloud tools can easily create environments where sophisticated analytics is applied to improve the quality of the customer data that guides the integration strategy and work. With a focus on short- and longer-term outcomes using cloud, we suggest three solutions: 

1. Immediate-term: Identify and classify potential customer impacts as soon as possible to ensure the sales and service teams can plan proactively. Additionally, simplify the process of securely managing customer data by having a ‘clean room’ environment in a secure cloud location.
    
2. Short-term: Use cloud data tools to clean up customer data and review its quality. Establish valuable analytics and reports that identify customers shared by different systems and organizations. This could require personalized change management activities targeting specific customers. Remember, data reviews and fixes will accelerate the post-merger integration of customer master data while ensuring the data is as clean as possible.
    
3. Longer-term: Re-architect customer master data record using a temporary environment and new quality and integration tools. One or more cloud environments could be used to clean customer data and productionalize it beyond post-merger integration. These environments could also be used as a platform to enhance customer data by combining it with data from partners and other systems.  

Reporting data architecture  

The key challenge: Existing data lakes / stores / marts are difficult to simply combine.   

Mergers often double the number and complexity of data stores and integrations. M&A is an opportunity to address the challenges relating to your data stores.   

We found that the solution to this reporting challenge exists in the re-evaluation of needs in terms of the current data lake / mart architecture and the context of the cloud investment. 

This is an opportunity to consider your candidacy for a move to cloud, taking out the fixed cost of infrastructure while maturing your data and cloud operating models. This brings the added benefit of migrating key reporting applications and further enhancing the cloud economics through simplification. 

It’s no secret that moving AI data lake environments into cloud creates major opportunities. Cloud providers are driving innovation in tools, processes and methods—and that increases the possible return on investment.  

We understand that it can be difficult to balance M&A cloud solutions in a way that supports short-term, tactical requirements as well as long-term ambitions. But remember, cloud has the power to help solve these data challenges and the rewards are worth exploring.  

Thank you to Richard Givens, Accenture’s Data Transformation Lead, for contributing to this blog.  

Want to stay on this train of thought? Connect via LinkedIn or reach out to us. We look forward to discussing these cloud data solutions with you. 

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Disclaimer: This content is provided for general information purposes and is not intended to be used in place of consultation with our professional advisors. Copyright© 2022 Accenture. All rights reserved. Accenture and its logo are registered trademarks of Accenture.