Introduction

The transformation of a company’s IT landscape and its connected business and operations offers great opportunities with SAP S/4HANA. It provides, among other things, an excellent opportunity to modernize business processes, reduce costs, and increase competitiveness by enabling real-time analytics, automation, and an optimized customer experience. This helps companies adapt to new technology trends and future-proofs them in a rapidly changing business world. However, there are certain prerequisites involved in a successful transition, and one of these is data quality. With this in mind, the purpose of this blog post is to illustrate the relevance of data quality and to outline some important steps involved in assessing and improving data quality in your company.

Why data quality is the lifeblood of business transformation

Data quality is critical in the context of a business transformation. The following section discusses five of the many reasons why this is so.

Data as the core of process efficiency and business process automation
Data is at the heart of operations for modern businesses. In the vast majority of the business transformations that we have observed, the general approach is to bring almost all business processes together on a common platform. Having data distributed over multiple platforms makes it impossible to maintain good data quality, and this is where SAP S/4HANA offers an answer – as an ideal common platform.

Furthermore, SAP S/4HANA is characterized by efficient and automated processes, which means that the quality of the data that flows into the processes is crucial. If data is inaccurate, incomplete, or outdated, the smooth running of business processes is hindered, as processes are only as effective as the data on which they are based. Inaccurate data also leads to rework by staff, who are forced to manually cleanse data or find workarounds. This negates the benefits of a transformation.

Relevance to machine learning and artificial intelligence
It’s widely accepted that machine learning (ML) and artificial intelligence (AI) will continue to play an important role for successful companies in the future. Good data quality is also the heart of this, as it forms the basis for accurate models and predictions. Inferior data quality leads to inaccurate results, distortions, and unfair decisions that affect the efficiency and trustworthiness of the systems. Furthermore, high data quality increases the robustness, scalability, and acceptance of ML and AI applications.

Data-driven decision-making
In today’s business world, data-driven decision-making is critical. Companies collect large amounts of data to gain insights into their business. If data quality is poor, the decisions based on that data are inevitably going to be incorrect. This leads to costly wrong decisions and has a negative effect on competitiveness.

Customer satisfaction and stakeholder relations
The quality of data, and customer data in particular, has a direct impact on customer satisfaction. If customer data is inaccurate, you’ll experience faulty deliveries, invoices, or communications. This affects customers’ trust in your business and endangers long-term relationships. In addition, it results in high follow-up costs due to faulty deliveries or expensive follow-up processes of rework.

Compliance and reporting
Some industries have strict regulations and compliance requirements that demand accurate and verifiable data. Poor data quality leads to violations of these regulations and reporting errors that have legal and financial consequences.

How data quality should be prepared for your transformation with SAP S/4HANA

We’ve examined a few important aspects relating to the crucial role played by good data quality, but many companies still face challenges in this area. These companies want to know how to identify and implement the right approach and the right steps to gain knowledge about their current data quality. They also need to know how to perform adequate cleansing of their data and how to maintain good data quality in the future. Therefore, in conjunction with your transformation with SAP S/4HANA, it’s important to take the following steps:

1. Perform a data analysis
   Start with a comprehensive analysis of your current data landscape. Identify all data sources, and determine which data is needed and which is redundant. Then carry out data profiling for your relevant data. It’s important to know how well your data is standardized and harmonized across the entire company and system landscape.
2. Define metrics and standards for your data quality
   Establish clear data-quality metrics that are as consistent as possible across your organization and that align with its business objectives and its data strategy. These metrics should be specific, measurable, and achievable and should serve as benchmarks for assessing data quality. You should also have clear standards written down and communicated to help ensure, for example, that dimensions and units are consistent across the company and that data is harmonized across the company-wide system landscape.
3. Run a data cleansing
   Data cleansing involves identifying and removing duplicates and irrelevant data, as well as cleansing erroneous data. In addition, data retention policies can be implemented to control the removal of obsolete data. These steps can help create a consistent and correct data landscape. Another reason that data cleansing is important with regard to the transformation with SAP S/4HANA is that it prevents incorrect or duplicate data from being migrated into the new system landscape.
4. Test data before migration
   Perform thorough testing of the data before the migration. This step helps ensure that data quality is maintained throughout the migration process and allows you to identify and resolve any issues or discrepancies that may occur prior to data migration.
5. Use data quality monitoring tools
   Consider using automated tools such as the data quality management component in the SAP Master Data Governance application or SAP Information Steward software to identify and correct inaccurate records. This is a proactive way of preventing problems caused by the detection of inaccurate records at a later stage, such as when the consequences of faulty data are experienced or when customer complaints are received by your company. In most cases, a proactive correction is more cost effective than the detection of the errors by employees or, for example, by a customer.
6. Assign data ownership
   Establish a clear data governance model with rules and responsibilities for individuals or teams within your organization. Data owners are responsible for the accuracy, completeness, and timeliness of data. Among other things, clear data ownership helps maintain data quality after the transformation.

How SAP supports you in your transformation journey

With the help of the Business Transformation Services group of SAP Services and Support, you can assess the current situation in your company by undertaking our “Business Assessment for Master Data Management.” For this purpose, the six important building blocks – data strategy, governance, organization, processes, data quality, and technology – are examined. After an initial maturity assessment, strengths and weaknesses in the individual areas are identified. These are then further investigated to adequately address weaknesses or to harness strengths and apply them to other areas. The initiatives and work packages derived from this are then prioritized and summarized on a road map to form a transformation plan. This gives you a clear approach toward determining which points you should address first so that you can be prepared for your transformation, eliminate risks to success in your already-running transformation, or further improve your existing processes.