So legacy data does not become a burden

Many companies face a major challenge when it comes to data management. Customer and business partner data must be maintained in such a way that it is consolidated, harmonized and of high quality. Up to 50 percent duplicates can be found in companies' customer databases.

Merging records carries the risk of creating duplicate or erroneous records, for example due to different entities or even just different spellings, such as Müller and Mueller or Str. and Straße.

Added to this are the data changes on the part of the addressees themselves: According to a study, more than nine million people in Germany move every year, in some cases several times. Marriages and divorces are associated with name changes, street names and even place names change, as do bank details or even contract agreements.

This quickly results in multiple digital identities for a single person, with differences in the respective data records. These not only burden the data pools and complicate data management, but also simply cause unnecessary costs.

Incorrect, erroneous and outdated customer data can mean that companies can no longer clearly identify their customers and business partners. This causes not only monetary but also non-monetary deficits.

Possible consequences can include loss of trust and loyalty due to an incorrect form of address: spelling mistakes in the name, the wrong form of address or, in the worst case, addressing someone who has already died - rightly so, customers not only feel bad about this, but also get the feeling that they (and their data) are not being treated in the best possible way, but negligently.

Consequences of incorrect data

Mailings that go nowhere: If offers and other mailings are sent out undeliverable because the address or name is out of date, this drives up marketing and shipping costs just as much as multiple mailings to one and the same person whose duplicates in the database are not corrected.

Mailing multiple catalogs to different people who all live in the same household is also unnecessary - a circumstance that can be circumvented via household mapping with the help of proper data usage.

Faulty customer service due to unambiguously assigned contract data: If different contracts are stored for a person in different departments of a company and the related data is not continuously reconciled, it is difficult to generate individually tailored offers here, for example, or to cross-sell for new contracts.

Overall, duplicate and erroneous data can create breaks in otherwise well-established processes, leading to poor customer experiences and putting customer loyalty at risk.

Spring cleaning

When it is necessary to transfer data from an existing system to a new one, as is the case when converting existing SAP systems to S/4, companies have no choice but to revise their customer and business partner data due to the changed data model.

This is because the new in-memory database technology means that a one-to-one transfer is not possible. If the legacy data is eliminated right away in this preparatory step, the maximum added value can be developed from the data and also from the SAP solution. The following tips can help to make the data migration as efficient and profitable as possible.

Correctly assign entities in the data model: It is not only important to correctly assign the entities in the customer data record during a migration. If entries are already assigned differently in different departments within a company, this leads to incorrect data records and duplicates.

During a migration to S/4, the systematics can even change: Whereas SAP's legacy ERP still distinguished between customer, vendor and business partner, each of which had properties such as personal and address data, posting accounts, bank details or role descriptions, these are now located on a new abstraction level as meta information on a uniform business partner data master. Companies therefore have no choice but to revise their data prior to migration.

The migration strategy influences the data revision: Depending on the existing software and IT architecture as well as the initial configuration, different technical and conceptual migration strategies are available to companies that affect the processing of the data. Users of classic R/3 and SAP Business Suite 7 (ERP/ECC 6.0) can migrate the complete system landscape step by step using the brownfield approach.

If no third-party systems or legacy databases are involved, this approach can be recommendable because previously made, individual adjustments can be retained. According to a survey of SAP managers in 122 German companies commissioned by Uniserv, 33 percent of the respondents pursue the brownfield approach. Here, those responsible should check in advance for legacy data records, because with this strategy there is a risk that unnecessary, error-ridden data will remain in the system.

Another third of the respondents are pursuing the greenfield approach, in which S/4 is implemented on a greenfield site and the previous system is completely replaced. Non-SAP systems can also be transferred to the new ERP suite, but must be adapted and converted accordingly.

This new start offers an enormous opportunity to standardize old, overgrown structures. In the course of this, those responsible can check exactly which data is really still needed and eliminate the old burdens accordingly.

More complex legacy systems from multiple software platforms with separate data silos are well advised to use the Bluefield approach. The complex migration is facilitated by the Selective Data Transition, because here system by system is technically transferred and it is decided which data is to be transferred to the new target system. Since the systems are considered separately from the data, a technical transformation, such as the move to the cloud, can be implemented in parallel.

Data quality assurance

It becomes clear that regardless of the migration strategy, the quality of the source data must be right. If this is not the case, neither the full potential of a new system nor that of the data can be exploited to the maximum. True to the motto "Crap in, Crap out".

In the Uniserv survey, however, only 53 percent of SAP managers rated the data quality available in the company as "very or rather high". 75 percent stated that they were struggling with outdated data. In turn, 83 percent complained about problems due to multiple existing data after the migration had already taken place - without having optimized the data quality.

However, if companies put the topic of data quality on the agenda at an early stage, processes can even be automated and simplified to a large extent. At the latest, the time before the migration should be used to perform an error-tolerant, automated mass check for correctness, cleansing and completion of the data with the help of professional tools.

Added value of the data

Correct, complete and up-to-date data records offer business-critical advantages regardless of a system changeover. Data of this quality can be consolidated from different data sources and merged into a golden record. This creates a unified 360-degree view of the business partner, which in turn is the basis for a smooth customer journey and customer experience.

Based on analyses from correct data, data-driven business decisions can be made, new business models can be developed, and targeted marketing measures can be designed. Ensuring and maintaining data quality must be understood as a firmly established and continuous process within the company. This is not only essential for business operations, which are supported with reliable analyses, but also enables complex projects, such as a system change or a merger, to be managed more quickly.