Tips on Migrating Data Successfully During a Merger or Acquisition

While mergers and acquisitions originally tapered down at the start of the pandemic, the recent funding rush and current geopolitical situation is seeing M&A activity pick up again. India, in particular, has seen an increase in the number of mergers and acquisitions that have taken place in 2021. There were 598 deals worth US $112.8 billion, with technology, media, telecom and financial services sectors leading the fray in terms of activity and value. All of these sectors are data-rich and since their businesses are driven by data, the success of a merger or acquisition literally rides on how well the migration process is handled. 

For one, getting people to collaborate and unite as one is probably the hardest part in making this a success. That said, there are many aspects to consider while migrating data during a merger or acquisition. 

First, one needs to know what is absolutely imperative for any data migration project to be a success. 

Work with trustworthy data: It’s important to ensure that your data is always up to date so it can set the foundation on which to start building your combined data governance framework. There is a lot of knowledge and data exchange that goes on between the companies in question, related to things like revenue, overhead, employees, etc. But you need to ensure that these numbers are correct and are being sourced from the correct place.

Implement strong data governance: When it comes to more long-term wins, ensure you have a strong data governance framework. With the knowledge that your data is always accurate, along with the ability to track your data throughout your landscape, you are able to build a solid foundation for high-quality governance.

Set clear leadership roles: The CEO or CFO are typically involved in the early stages of a merger or acquisition, focusing on investigating company figures and the true value of the business opportunity. After the merger has been finalized, you would typically need two CIOs whose primary focus is collaboration. It’s also important that the project is supported by top management because you need to have full transparency into a system when merging and migrating.

Avoid common pitfalls in M&A migration projects

You also need to be prepared to tackle possible pitfalls during the course of the migration. From a data migration perspective, it’s crucial to ensure that the data landscapes are united in such a way that all the data and KPIs follow the same logic. For that, one would need to keep a few things in mind so as to avoid some of the common pitfalls in M&A migration projects: 

• Evaluate & Validate: From an early stage, before the merger has even taken place, you need to be able to evaluate and validate the figures that are presented to you – so you can properly determine where the KPIs actually come from, how they are connected, how they are calculated and how they are interpreted.

• Pre-empt the impact on the data landscape: When you merge data landscapes, it’s often extremely complicated. It’s crucial that you, early on in the process, understand what impact the merger is going to have throughout your entire data landscape. This is why it’s entirely necessary to establish a thorough overview of your landscape, including all its integrations, before tackling this type of migration.

• Adhere to one type of logic: It’s also quite common that the companies in question have different ways of measuring, using and calculating data. This presents a further challenge as it is entirely essential that the final landscape adheres to only one type of logic.

• Be prepared for the unknown: An organization may even have built in-house add-on systems, e.g., time reporting, budget forecasting system, etc. These types of systems can be difficult to identify in advance and often come as a surprise when you are already midway through the project.

• Factor in cultural and language differences: Often, documentation and software will be presented in a local language – creating challenges when it comes to understanding KPIs and the overall data landscape. Cultural differences can be an obstacle when it comes to decision-making styles, leadership, ability to change, how people work together and beliefs regarding personal success and teamwork. In cases like this, data lineage can reveal the truth about the company´s real status.

• Discover ‘bad’ data: Unfortunately, surprises like these happen a lot – especially when you are dealing with two or more separate landscapes. Usually, when you choose to embark on any type of migration project, you will use that opportunity to clean your data.

• Automate the landscape-mapping process: There is a lot of impact analysis that needs to take place before anything can be migrated. As you begin to merge your landscapes, you will need to first map the individual data environments in order to fully understand what elements will be affected by the migration and what will need to properly integrate with the new combined landscape. But manually mapping these landscapes and their dependencies is very time-consuming, and you also don’t get a comprehensive overview of the entire solution. So it’s important to have an automated solution that can help you instantly see where all the dependencies exist. 

Estimating the cost and time needed to migrate data

This entirely depends on the size and complexity of the data landscapes in question. Another important thing to consider is the innate compatibility of the landscapes. Are the companies using similar systems from the get-go or will further migrations have to take place to achieve the final result (e.g., BI or ERP migrations)? A good place to start is by mapping the landscapes in order to fully understand what it is you are dealing with.

It is also important to look at the long-term effect of a project. Data quality, data cleaning, mapping and documentation are key factors for an efficient project. If that is not done properly from the beginning, further problems and costs will occur later.

Improving the project’s ROI

IT projects generally tend to become larger than initially predicted and this applies here as well. Subsequent needs for further investment often stem from bad project management and incorrect prioritization.

But instead of trying to calculate a specific number, you can look at it in terms of the number of resources you were allocating to different BI tools and teams, as well as different data landscapes prior to the migration. This acts as one parameter. But another important thing to consider is the quality of your solution(s). By uniting all the data in one landscape, you can ensure that you are basing your business decisions on the correct KPI (rather than having two or three separate versions to choose from).

The key to maximizing ROI and success lies in automating the mapping of your current data landscapes, as well as employing a documentation process that is automated and up to date. Ultimately, I would say the ROI is split between directs savings, less software and manpower costs, and (perhaps most importantly) indirect savings, as well as the quality of your data solution. 

- The author, Oskar Grondhal, is a Senior Director of Product Management at Qlik, specializing in Data Analytics, Governance and Enterprise Data Strategies.