Market disruptions over the past two years have driven a global surge of mergers and acquisitions. According to Ernst & Young, the first six months of 2021 saw a record $2.6 trillion in M&A activity, obliterating normal levels that hovered around $1.5 trillion per six months prior to the pandemic.
M&A allows organizations to consolidate capabilities, diversify resources and ultimately increase business agility and adaptability — all critical in a dynamic business environment. Salesforce’s recent acquisition of Slack, the merger of Eat and Grubhub and Intuit’s takeover of Credit Karma are just a few recent corporate transactions that have strengthened the combined company and readied it for fluctuating market conditions.
The data challenges involved in a merger or acquisition, however, are significant — and a failure to combine business systems, integrate data sets and reduce redundancies can jeopardize the entire transaction. Here’s a look at how modern, cloud-based technologies can help organizations overcome these challenges and facilitate M&A success.
Data challenges for merging organizations
When two organizations merge, terabytes of data from dozens or hundreds of sources — including SaaS applications and CRM, ERP and finance systems — need to be centralized in a single destination that all stakeholders can access. Insights from that data can help the new organization streamline operations and eliminate redundancies. The faster the integration occurs, the better, as running parallel data systems can be incredibly expensive.
It may take data engineers and scientists months just to understand where all the data is, however, as well as how it is structured and how it will be used by the new business. The data may have competing taxonomies and will need to be cleaned up and structured consistently.
Streamlining and accelerating this process is especially important today, because mergers and acquisitions are becoming more elaborate and taking longer than ever to complete. Digitally driven deals and transnational mergers, for example, introduce regulatory compliance roadblocks, while activist investors may gum up the works by placing demands on deals. The average time to close an M&A deal has increased by 31 percent over the past decade, according to Gartner, reaching an average of 106 to 179 days to complete deals larger than $25 billion.
All of this makes efficient data integration a business necessity for merging organizations.
Options for solving M&A data integration issues
Many organizations create and manage their own data pipelines using manual tools such as SQL Server Integration Services (SSIS), Azure Data Factory and Informatica. This DIY data integration approach is largely manual, slow and requires in-house expertise that isn’t easily replaced when engineers leave the company. It can take a data engineer weeks or even months to build a single data connector. Multiply that by the number of connectors needed — often dozens or even hundreds within a single company, depending on the data environment of the merging companies — and you’re talking about a multi-year project.
Outsourcing to consultants
To lessen the burden on existing resources, some organizations choose to outsource the development of data connectors to consultants — but this, too, costs money and is susceptible to expertise leaving through turnover. Pipelines built by consultants tend to be fragile, with data reliability issues and high maintenance costs. This approach also adds a bureaucratic layer, putting up an artificial barrier to gaining business insights quickly.
Automated, fully managed data integration
For merging or acquiring organizations, full-service managed data integration solutions are the simplest, most effective way to connect disparate data sources quickly and efficiently — without diverting engineering resources, adding operational complexity to the IT stack, or introducing new data security and privacy risks.
Managed data integration allows business users to put in a request to pull data from SaaS-based sources such as Google Analytics 360, Marketo and Salesforce, as well as transactional databases such as MySQL, Postgres, SAP and Oracle. Managed services typically support hundreds of data sources that touch nearly every aspect of the business, making it easy to expand access to new data sources or commonly used internal sources.
In most cases, data teams can make the connection to a data source within minutes and initiate the flow of data immediately. Within hours, stakeholders across the merged companies can start manipulating and analyzing data.
For more on how common data integration approaches compare, take a look at our post on DIY data integration vs. automated solutions.
How modern data architecture facilitates M&A
Automated, managed data integration is an integral part of a modern data stack, which often includes a cloud-based data destination, an integrated data transformation tool and a business intelligence tool. Modern organizations of all sizes are transitioning to a modern data stack to simplify the data engineering underlying data analytics and data management — including businesses struggling to unify disparate data infrastructures after mergers or acquisitions.
Autodesk Construction Services, which recently decided to unify its data architecture after multiple acquisitions, is a great example. With a data stack of Fivetran, Snowflake and dbt, Autodesk was able to standardize and improve its combined data infrastructure after acquiring BIM 360, BuildingConnected and other businesses. The new infrastructure allowed it to easily merge data from the acquired companies and create a trusted, organization-wide single source of truth. Autodesk Construction Services also eliminated data pipeline maintenance, saving hundreds of hours in data management and engineering.
Tune into "Using a Modern Data Stack to Power Your M&A Success" and hear from experts at Slalom, Coupa and Okta about standardizing data practices in M&A efforts.