How to analyze the quality of your data before a data migration

When introducing new software such as SAP S/4HANA, data needs to be migrated from the old system to the new one. However, there are a number of pitfalls with data migrations that often stand in the way of implementing the project on time and on budget. In addition, companies sometimes treat the topic of master data (quality) somewhat neglectfully – with serious consequences. After all, if difficulties with the data suddenly arise during an ongoing migration project, this can result in considerable delays.

Start your data migration project with high-quality and verified data

To ensure that a data migration runs smoothly, it is essential to gain detailed knowledge of the relevant data and processes before the project begins. If possible, low-quality data should not be transferred to the new system in the first place, but should be cleansed and optimized beforehand. A careful analysis of your own data and processes aims to create maximum transparency and ensure a smooth data migration.

The four phases of a data migration

As a competent expert for data migrations, IBsolution follows a comprehensive best-practice approach consisting of the phases of data analysis (“Know your data”), data cleansing (“Cleanse your data”), data transport (“Migrate your data”) and data quality monitoring (“Trust your data”). This approach ensures that the data is complete, correct, redundancy-free and quality-assured in the new system after migration and that the high data quality is maintained in the long term.

“Know your data” – Data analysis

The “Know your data” phase serves to draw a coherent picture of the existing data as well as its quality and depth of detail as a starting point for the upcoming data migration. The aim of creating transparency about the existing data landscape is to achieve maximum clarity, draw the right conclusions for the course of the project from the analysis results and define the next steps.

“Cleanse your data” – Data cleansing

Data cleansing includes defining the scope and deciding which data should be cleansed in the first place. Address cleansing and a duplicate check are proven measures for effectively cleansing data. These methods make a valuable contribution to increasing data quality and preparing the data for migration to the new system in the best possible way.

“Migrate your data” – Data transport

When it comes to transferring the cleansed data to the new system, the main focus is on technical aspects. The first step is to define the criteria for selecting the migration tool. It is equally important to decide exactly how the data transfer to the new system is to take place.

“Trust your data” – Monitoring data quality

After the migration, the long-term goal should be to keep the data in the new system permanently clean and to monitor the data quality effectively. Achieving this goal requires a strategy that defines suitable measures to avoid duplicates in future, keep addresses up to date, validate correct field formats or enrich and supplement the existing data material using external sources if necessary.

Best practice approach: Data analysis in six steps

A six-step approach has proven to be effective for evaluating data quality prior to data migration. This gives companies a reliable picture of the state of their data and identifies the most important starting points for making their data stock fit for migration.

Step #1: Workshop

The starting point for the data analysis is a workshop in which basic information is collected and known data quality problems are discussed. It is also important to formulate the precise objectives of the data audit. The workshop should focus on the business and its processes rather than the technology. Following the workshop, the project participants define the next steps for the data analysis.

Step #2: Kick-off

The kick-off serves to bring together the data stakeholders from the business departments and IT so that they can define the scope of the project and clarify the IT infrastructure. The definition of master data and the answer to the question of its provision also take place as part of the kick-off. In addition, the participants jointly draw up a project guideline containing detailed information on the scope, content and objectives of the data migration. The kick-off then serves as a roadmap for the evaluation of master data quality.

Step #3: Quantitative data analysis

Quantitative data analysis involves creating a master data catalog, defining the frequency of the data and validating the data types and characteristic variants in the database. The data is examined with regard to attribution, value ranges and fill level. A detailed report summarizes the results of the quantitative analysis and describes any anomalies.

Step #4: Qualitative data analysis

The qualitative analysis is the most important step of the master data audit. The aim here is to analyze the already known errors and problems and to identify duplicates. A close exchange with the business departments, who generally know the data and the associated challenges best, is crucial for success. The findings from the quantitative data analysis are used to assess the quality of the master data. Furthermore, the master data catalog is finalized, which provides a precise overview of the data content.

Step #5: Evaluation

The evaluation combines the results from the quantitative and qualitative data analysis and compares them with each other. The roadmap for data migration is also derived from the results.

Step #6: Summary

The final step in the master data audit is the summary. The results of the data analysis are presented to the project members and stakeholders. In addition, the creation of a catalog of measures and the drafting of a roadmap are also important elements of the summary. Based on the results presented, the participants decide on the next steps in the migration project.

Conclusion: Starting the new system with cleansed data

Ensuring high data quality is one of the main challenges of a data migration project. Ideally, incorrect data should not be transferred to the new system in the first place. It is therefore important to identify and rectify existing problems in terms of data maintenance and data quality. If only the data that is actually required and validated is migrated, companies contribute directly to their competitiveness and avoid dissatisfaction among users. To achieve this goal, a master data audit proves to be an effective procedure. After all, a clean data pool creates the ideal conditions for making a good start in the new SAP system. After all, the establishment of AI-supported processes, for example, is inconceivable without high data quality.