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"Data Migration in Mergers and Acquisitions"

Mergers and Acquisitions: Best Practices in Orchestrating and Accelerating Data Migration Processes in a Standardized Way

White Paper: Analytix Data Services

The Mergers and Acquisitions (M&A) process brings with it a broad range of complexity, from contracts and accounting, to organizational structure and employee protocols. The primary reason many mergers and acquisitions do not deliver longer-term value is because they lack a strong cultural-integration plan.

This whitepaper seeks to provide how the data related issues that come with combining disparate source data as is often the case in mergers and acquisitions can be addressed. 

Key takeaways from this whitepaper on Mergers and Acquisitions:

  • How to avoid Common Pitfalls in Mergers and Acquisitions?

  • More than 80 percent of all mergers and acquisitions fail. Why?

  • Best Practices in Orchestrating and Accelerating Data Migration Processes in a Standardized Way

  • Features of a typical merger or acquisition data project using a resource-driven approach

  • Pain points in mergers and acquisitions that begin with basic decisions about how the organizations involved, align with one another

  • Best tool to standardize the data migration process that can eliminate the error prone and costly manual process in managing complex data migration projects? 

  • Best features and functionality offered by AnalytiX Data Services’ flagship product AnalytiX Mapping Manager

In this whitepaper, there are few out-of-the-box answers for streamlining this process, and though there are many system integration companies available to help, this can add significant cost and time to the Merger & Acquisition cycle.

Mergers and Acquisitions: Best Practices in Orchestrating and Accelerating Data Migration Processes in a Standardized Way
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Data management is the development and execution of policies and procedures in order to manage the information lifecycle needs of an enterprise ensuring the accessibility, reliability, and timeliness of the data for its users. Data Management enables organizations and enterprises to use data in: Organizing the enterprise data, Storing and preserving data for future re-use, Making data ready to use anytime, Share data with colleagues

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