Informatica Enterprise Data Management

Every experienced CIO has been through an acquisition, merger or divestiture in his/her career. The pressure cooker starts when the CEO makes promises to the street and starts the results clock ticking. Expectations set during the pre-acquisition due diligence and planning process—expectations about operational metrics, timelines, and financial results— often bear little resemblance to post-acquisition reality. In my experience with our customers using our technology during M&A, data and how it’s managed plays a central role in meeting the street-committed results.

Putting aside the due diligence process, a challenge unto itself, typical post-merger projects that put data in the center of success are usually big ticket, highly visible IT efforts. Data Fragmentation and the challenges therein are really magnified when two IT organizations are forced to come together or in some cases, wrestle together.
• Consolidated Financial Reporting – This is usually the first major project with highest visibility. Completion is mandatory for the next quarterly reporting period and for adherence to compliance regulation. In most cases, to meet the severe time constraints, companies 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. Data integration and data quality play a critical role in implementing the financial data warehouse quickly.

• Customer Upsell/Cross-sell – Not a merger announcement goes by without estimates on the amount of revenue that can be derived from a newly minted joint customer base. Realizing that value depends on the speed at which customer data can be consolidated, analyzed, and integrated and the efficiency of combining sales processes and teams. Of all the data-related merger projects, this is the most difficult to do well. Quite often customer data is stored in a variety of systems with varying degrees of quality. Performing this process by hand is a risky endeavor and is a natural fit for data integration and data quality technology.

• “Back Office Efficiencies” - These are the internal efforts to combine suppliers, reduce redundancy and drive alignment within the newly formed organization. Master data projects crop up in support of reducing costs, better negotiation leverage and consolidation of contracts. And of course, master data projects need data analysis, data quality, and data integration!

• ERP Consolidation – ERP consolidation is a major multi-year undertaking, especially when different platforms are used by the separate organizations. Often, the data assessment and migration process is under analyzed and under planned. In fact, 5 in 6 data migration projects *fail* according to industry studies. By planning data migrations appropriately upfront, and investing in data integration and data quality tools, companies can drastically reduce the risk to their overall project and ensure that the benefits are delivered as promised to the business.

An ICC Can Accelerate Results
If a business is actively engaged in M&A as a growth strategy or in the extreme a complete business model, an ICC is a critical success enabler. ICC’s by their very nature implement best practices for data-related projects like consolidation and master data management including definition alignment and the measurement and improvement of quality. One great example is First American, a strategic Informatica customer, engaged in serial M&A to grow their business. Their strategy and results are documented in a joint Informatica, First American and Gartner online video/presentation on Enterprise Data Management

Proactive Value Estimation
One model that I’ve seen offered by a few of our strategic consulting partners is the concept of a “data clean room”. Acting as a neutral third party in the due diligence process, master data from both companies is gathered for processing and analysis. Wouldn’t you like to know the percentage of product portfolio overlap, the cross-sell opportunities within a future joint customer base or even the initial level of data quality of a possible acquisition target? Data Quality principles all apply and can provide rapid measurement and alignment of key data types.

Dare I mention the impact of divestiture? This strategy is starting to gain momentum as the economy switches gears. As this Booz Allen Divestiture Analysis outlines, the data-related challenges never go away!

I welcome your comments on the challenges you’ve faced in a merger or divestiture.