Data consolidation - biggest success factor for your migration project
Data consolidation gets more and more important. Because within companies, the number of data sources and (communication) channels is constantly increasing. Social media, CRM systems and email marketing tools, plus various company departments such as sales, accounting, customer service, merchandise management or different company divisions. All these data sources usually generate different data structures of varying quality. Since the generated data is usually stored separately touchpoint by touchpoint, this fosters a heterogeneous landscape of applications and databases that - to make matters worse - cannot communicate with each other.
As a result, more and more data silos are being formed, i.e., self-contained data islands that do not allow any data exchange among themselves, alone because of the different structure of the data sets. Each island thus contains fragmented data records, but they do not "inform" each other about changes or updates. A holistic view of customers, prospects and other business partners is thus not possible. This has a negative impact, especially during data migrations.
An example of the consequences of missing data consolidation:
Mr. Müller is calling his customer advisor concerning his mobile contract. He explains that he has just gotten married and now has the double name Müller-Schulze. The customer advisor updates the name in the data stored for the mobile contract. Mr. Müller also receives Internet from the same company. However, the cell phone and Internet divisions do not synchronize their data, and his name in the Internet contract remains the same. This quickly leads to inconsistent, duplicate and, in the worst case, contradictory data records. Mr. Müller now exists as two data records, under an outdated name and under a new name. Employees can no longer determine which is the correct one, at best by the update date of the one data record. This is not only annoying, but also wastes a lot of potential: If all information and data were collected and automatically maintained centrally for up-to-dateness and quality, a holistic picture of the customer or prospect could be created, which in turn could be used for a wide range of activities.
Successful data migration only with consolidated data
There are many reasons for data migration projects. These include the introduction of new systems, the consolidation of IT systems or major version updates of existing systems. However, many of these migration projects fail or take longer and thus become more expensive because there was no data consolidation in progress before the migration.
This generally increases the complexity of the entire migration project. The risk of transferring incorrect data that is incomplete, outdated or even duplicate and multiple increases extremely. The result is time-consuming rework that becomes very expensive.
If you were to take a concerted approach to the topic of data consolidation in the run-up to data migration, and in the course of this also increase the quality of the data to be migrated, one would save oneself a lot of work, especially in the aftermath.
What does data consolidation mean?
A uniform, comprehensive and, above all, reliable database from all data in all data silos with customer data that can be clearly assigned to a person.
Does that sound like an unattainable goal? Not at all!
Data records can be brought together from all existing sources in a targeted manner, so that they are no longer fragmented but consolidated and centrally available. This enables uniform master data management, starting from a 'master dataset', on the basis of which those responsible for the data define the data quality criteria. For all subsequent processes in data processing and use, consolidation thus represents quality assurance, for example in the case of data migration.
Tools and methods for data consolidation
A desired data model must be defined for the central master data basis, to which all data to be merged must be adapted, so to speak the reference. This is known as the ETL process, where ETL stands for Extract, Transform and Load. The relevant data is extracted from the various sources, transformed into the new data model and then loaded into a new system with its target database, for instance, as part of a data migration. The data model can, for example, contain a uniform customer ID. The data managers set up matching and merging rules for this purpose. These define how sensitive the detection of redundancies and duplicates in data records is during consolidation. If Mr. Müller in our example has the same date of birth and the same first name and street name in all data records, but the double name appears in only one data record, then data quality tools can use the matching and merging rules to identify these data records as duplicates in an error-tolerant manner and merge them if necessary.
Consolidation produces the so-called Golden Record, the best possible data record with the highest achievable information content. This serves as a guideline for future data maintenance.
But how do the current, high-quality and complete data sets get back into the systems where they are used? This is done with the help of a customer data hub. As a central instance, the CDH not only takes over the quality assurance and consolidation of the data, but also controls the synchronization of the data across all systems.
Advantages of data consolidation
By standardizing the data structure, consolidating and aligning the information content of each data record, a uniform and comprehensive view, a 360-degree view, of the customer is possible. This benefits marketing and sales, for example, as the data quality of the customer data has a direct impact on the customer experience and the customer journey. Cross-selling and upselling potential can be exploited. With this high-quality database, companies also have a reliable basis for analyses that can be used to further develop business strategies. The completeness and accuracy of the data are also indispensable in compliance departments, the legal department or in controlling and generally for data protection.
Advantage through data consolidation: Merger & Acquisition
Data consolidation is particularly important in the case of mergers and acquisitions. This is because completely different system worlds meet here, with their own data silos, their data architecture and their databases, with their own data management. Often, they are based on different data quality standards, possibly even with different regulatory requirements. If, for example, corporate groups merge, many millions of customer data may come together, possibly with numerous overlaps. But if merger partners want to create a group-wide, consolidated 360-degree customer view, they cannot avoid data consolidation. Merging and integration requires great care, sufficient resources and some lead time. As part of the M&A, data stewards must establish consistent data governance with high data quality standards to ensure the data quality achieved is sustainable. In the end, the result is a consistent, reliable database that reveals new potential, for example for cross-selling and upselling. And, in general, it makes quality-oriented data management much easier.
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