How does it fit? M&A data management

A current definition for merger and acquisition is the consolidation of two organizations. The rational that supports these operations is that the two organizations together will generate more value than each of them.

The underlying concept is that the whole is greater than the sum of the parts.

This consolidation movement is a normal part of any industry. It establishes economies of scale driving down the cost of goods and services and turn the market more competitive.

The additional value targeted by these operations is generated achieving synergy between the assets of both organizations, that may result in cost efficiencies, larger market shares, stronger competitive position, enhanced revenues.

Accomplishing these synergies is an objective in merger and acquisition deals, but the desired synergistic benefits are not always achieved.

The success of these operations is strongly dependent on identifying potential problems in the process and prevent their effects after the merger is completed. Managing these risks can determine the success of the operation.

Due to the complex and mission critical nature of business processes within financial services and telecommunications companies, glitches and disruptions in the course of integration can be disastrous, resulting in the loss of customers and compromising the company reputation.

Generating synergies with data

The merger and acquisition process focuses on evaluating the prospective institutions, to drive value from the synergies between the organizations.

An often-overlooked perspective is if they can drive synergies from their data.

Q: How important is it to have good data management program in place before mergers and acquisitions?

A: It’s not important, it’s critical.

A critical step prior to an M&A operation, to determine the cost of operation, is the due diligence process. Determining the IT-related costs, specifically data related costs is a daunting task.

If no data management program, even minimum, is in place, it is almost impossible to determine where the company’s data is, what is used for or if it has the necessary quality.

When the M&A happens, it will be necessary to start, for instance, to report combined financial results, which means to integrate the information from both organizations, and this is where the issues start to arise.

Some of the recurrent situations at the start of this process are for instance the difficulty of identifying all the relevant data sources (most likely some of the relevant data is managed in spreadsheets), identifying data that does not meet the necessary quality requirements (forcing ad-hoc corrective measures), etc.

On the other hand, if all the data is already governed, all the previous situations will be previously known, making the integration process much cleaner and at a lesser cost.

The lost customer

Customer data is often an obvious starting point as the organization will surely exploit a larger pool of Customer data for up-sell, cross-sell and retention purposes.

This approach is an opportunity and a challenge, as most of the organizations don’t have an integrated view of their customers spanning across all lines of business. This is challenging when considering a single organization, when considering two organizations the challenge is even larger and so are the potential gains of building this pool of data origination from both organizations.

These gains can be easily perceived in the savings on time and cost of the systems integration process, provide the basis to reduce the risk of data migration or application retirement process, once supported by an integrated view of the customer base from both organizations, as well the potential created as a source for high-quality customer data, for marketing or other business activities and with the possibility of scaling-up into a Master Data Management solution.

The same approach can be applied for additional domains, especially critical for the banking/telco business, as products, contracts or reference data.

The recommended approach for this situation will be founded on the following stages:

• Identify within the two organizations ecosystem the sources for customer data.

• Identity the Critical Data Elements (CDE) to be integrated.

• Assess the data sources to identify the possible data merge scenarios and data quality issues.

• Clean and standardize the data.

• Match and merge of the entities.

• Build a centralized high-quality data repository, to be the hub for subsequent initiatives.

From this point is possible to scale-up to a Master Data Management solution and it is possible to get real benefits from such an approach within a few weeks or months at most.

Implementing an MDM solution, without first addressing data quality issues or without the necessary conditions to load it with consistent data and keep it synchronized, will, instead of solving the integration problems, create additional difficulties.

When talking of large enterprises, the integration of all their major systems often take years to accomplish at huge costs.

A large portion of these costs is related with data migration, frequently identified as the major causes to prevent closing these projects on time and on budget. Being that the major reasons behind it is the incapability to know and understand the data to be migrated, to identify all relevant data sources.

When embarking in such an endeavour it is critical that these organizations are supported by a partner capable of addressing all these challenges, generating value, supporting the change process, managing the risks, and maintaining the alignment with a consistent data strategy.