Cross selling, economies of scale, synergy, vertical integration, increased market share, geographical diversification and reducing taxes are all desired outcomes powering the surge in M&A activity throughout the world's economy. In fact, M&A has become a standard business strategy throughout many sectors including airlines, banking, financial services, high-tech and telecommunications among others.
Consolidating the underlying systems and the data therein is often the major success factor in reaping the desired and, in some cases, pre-promised results of a merger. In fact, Accenture estimates that IT is a key enabler to realising expected merger synergies in over 60 percent of post-merger transactions.
Let's take a closer look at the key roles that data integration and data quality play both prior to any acquisition activity, through the due diligence lifecycle, and into the years as a merged company afterwards.
Pre-acquisition due diligence and planning
Prior to the acquisition, executives, bankers and lawyers need to be able to quickly access key operational and financial data. The data is key to evaluating whether or not to go through with the transaction, and to set the deal terms. Often, basic reports and spreadsheets are insufficient. The deal team needs access to detailed data in systems for their valuation.
Companies can spend tens or hundreds of millions of dollars in the due diligence process alone. The ability to accelerate this process by providing faster access to more accurate data can lead to huge cost savings - even if the deal doesn't go through.
Managers also need early visibility into the systems and data to begin scoping the overall integration effort. The ability to access the data helps them to prepare an IT assessment, to set a high-level integration plan even before the deal is closed, and to ensure they have a say in the promises made to shareholders.
The ability to natively access a wide variety of data sources is critical to accelerating these processes. Days or weeks can be lost hand-coding access to legacy data sources, whereas data integration tools with pre-packaged connectivity can extract the data in hours. Also, teams that use data profiling tools can quickly gain a sense for the data structure and data quality issues that will need to be addressed once the integration project gets underway, helping executives to appropriately scope timelines and resources.
Financial reporting consolidation
Post-merger, the first project on most agendas is to provide consolidated financial reporting across the newly merged entities. Often, they only have a quarter or less to make this happen. In most cases, to meet the severe time constraints, they build a consolidated financial data warehouse that pulls together data from the various financial systems, rather than attempting to consolidate the underlying financial systems themselves. Reports for the newly joined entity can then be produced from the new financial data warehouse.
Again, data integration and data quality play a critical role in implementing the financial data warehouse quickly. Data must be accessed from the various source systems, and transformed in order to roll up correctly. The data must be cleansed so that it is accurate, and so that it can be reconciled and validated. With stringent audit requirements and a huge amount of scrutiny to be expected, there can be no room for error with the financial statements.
Using the right data integration and data quality tools can not only drastically accelerate the cleansing and consolidation of the financial reporting data, it can significantly reduce risk. Modern tools automatically document on where the data came from and how it has changed, enabling teams to provide an audit trail to satisfy the most rigorous compliance review.
Back office efficiencies
As far as business synergies go, increasing back office efficiency is often considered low-hanging fruit, and is typically tackled in the first year following a merger or acquisition. One common project is consolidating master, or reference data, for suppliers and vendors. With visibility across all vendor relationships, procurement officers and finance groups are able to negotiate better prices and terms, ensure corporate discounts are applied, take advantage of volume purchasing discounts, and better control accounts payable.
Similarly, customer master data is also often tackled early on, with the initial tactical goal of improving accounts receivables processing. The organisation consolidates a reference or "master" list of all customers and customer IDs across all business units, enabling finance to ensure that invoices and payments are being processed correctly.
Regardless of whether the focus is supplier, customer, product, or employee data, master data management is a significant undertaking for any company. Data must be extracted from a variety of source systems, cleansed, consolidated into a master record, and then synchronised back to operational and reporting systems. Again, data integration and data quality tools play a critical role in accelerating master data management (MDM) projects, and ensuring repeatable processes on-going.
Customer loyalty and cross-sell
Streamlining accounts receivable is just one business benefit of customer master data management. More important is the impact on revenue. The opportunity to cross-sell products and services across the customer bases of the combined entities is often the number one motivation for M&A. The single view of the customer provided by MDM enables sales and marketing to understand a customer's profile and history, and make the appropriate cross-sell offers to maximise incremental revenue.
While the focus is often on such cross-sell opportunities, customer MDM is also critical to preventing customer attrition, which can spike during the chaos of M&A. With better customer visibility, companies can run retention programs to ensure that customers don't "slip through the cracks".
As customer MDM expands in scope to achieve these various business objectives, additional data must be added and new systems synchronised. With data integration and data quality tools, it is easy to reuse and extend the logic to support these new requirements.
Enterprise resource planning (ERP) consolidation
One of the most complex projects companies may take on post-M&A is consolidation of ERP systems. This is typically a multi-year process, and may not even start until a few years after the initial merger. To streamline business operations, improve consistency and reduce IT costs, companies must consolidate multiple systems, often numbering in the dozens, onto one or a handful of systems.
Migrating and consolidating data from the legacy systems onto the new system(s) is a huge undertaking, and the work involved is often underestimated - leading to inappropriate execution plans and promises. Multi-year, US$100 million ERP consolidation projects have been delayed or even derailed altogether by data migration issues.
By planning data migrations appropriately upfront, and investing in vendor-neutral data integration and data quality tools to manage those activities, companies can drastically reduce the risk to their overall project and ensure that the benefits are delivered as promised to the business.
Conclusion
M&A is a complex undertaking for any company, fraught with risk. Remember that critical statistic, 58.3 percent of deals destroyed value for the acquirer's shareholders, producing a net loss of 1.2 percent for all transactions.
By tackling data issues upfront, and effectively leveraging data integration and data quality tools to support the process, rather than impede it, companies undertaking merger and acquisition activities can help lower their risk, deliver real business impact, and avoid becoming stockmarket roadkill.
Read the article "How To Keep IT On Track When Integrating Companies - Expectations And Assumptions"