Post-merger Data Consolidation

As company mergers (including acquisitions) as well as splits (e.g., selling company divisions) are a common and ongoing practice these days, it is absolutely vital to have tools to support such processes on a business critical data level, in order to obtain a single view of the customer or integrated product catalogue as soon as possible. One of the first priorities after a merger is data integration, which can follow several typical scenarios:

1. Zero data integration – only an EAI based approach
2. Data migration – data from the primary systems of the acquired company is migrated into the primary systems of the acquiring company (and sometimes even the other way around)
3. Operational data integration – an Operational Data Store (ODS) or some other specific operational system (e.g., CRM) based approach
4. Analytical data integration – typically a Data Warehouse based solution
5. Any combination of the approaches mentioned above

Ataccama tools can support any of these scenarios:

1. Ataccama Data Quality Center (DQC) can be used as a data quality firewall to prevent both bad data entry and exchange among integrated systems. DQC can provide even match & merge and golden record creation functionality if some of the connected systems can deal with, e.g., golden customer records. In case there is a need to persist consolidated data domains (customer, employee, product, etc.) while no primary system can store them (typically due to technical, performance or structural inflexibility reasons) Ataccama Master Data Center (MDC), with its almost endless system integration possibilities, may become a part of an architecture as a business critical master data integration element.
2. As a data migration is one of the biggest opportunities to assess, cleanse and consolidate business critical data, Ataccama Data Quality Analyzer (DQA) and/or DQC can be used as a key element of this process. Once data is migrated into its target system, DQC can provide its online bad data prevention services, known as "Data Quality Firewall".
3. In case of an existing ODS or other operational data integration system, DQC can be used as an addition to the data integration component to deliver ongoing data validation, cleansing, consolidation (match & merge), data quality monitoring and reporting capabilities. In case of the need for data mastering of business critical data domains (including shared lists of values), MDC will easily fit in for that purpose.
4. Similar situations to the previous case, with the purpose of data integration only being consistent reporting for the new extended enterprise. Both DQC and MDC can play their roles in this as well, as there won’t be any reliable results without a single version of the truth based on trustworthy cleansed, consolidated and reconciled data, permanently surveilled by DQC for its required data quality level.

In addition to the integration scenarios, the "disintegration" ones when a company splits are equally important. As it is typically necessary to remove the piece of data belonging to the sold part of the company, there needs to be an instant process, like in a merger situation, to consolidate the results and get consistent answers to urgent questions about updated customer profiles, their products, a new view on their current and future value, etc.