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 Defining what constitutes “good” data initially seems relatively simple. High-quality data is current, relevant, and complete information 鈥� precisely the information a company needs to ensure its processes run efficiently and it can make decisions based on solid data.

 

Data quality often suffers significantly because companies accumulate enormous and sometimes superfluous amounts of data over the years from everyday business activities, personnel and organizational changes, as well as mergers and acquisitions. In addition, digitalization is causing a massive increase in data volume across all business areas. Such a data accumulation quickly results in a steep rise in administrative and operational costs. Maintaining and servicing IT systems costs time and money. Employees lose track of which data they can use, when, and for what purpose 鈥� and where to even find that data. This negatively impacts all ongoing business processes.

 

Poor data quality arises not only from incomplete, erroneous, outdated, or incorrectly formatted data records, but also from the duplication of master data鈥攁 relatively common practice that can lead to confusion, data misuse, and business disruptions. One example is excessive credit limits for a customer for whom numerous different data records exist, which are then processed by different employees. Another example is duplicate addresses, which cause unnecessary postage costs or even lead to goods being misdirected.

 

Companies, especially those in highly regulated industries, need a strict and structured approach to data quality. But awareness of the potential problems posed by poor data is also growing in other data-sensitive areas, and companies are addressing their challenges more proactively.

 

Even in retail and other lower-margin sectors, awareness of the issue is growing. These companies increasingly view data 鈥� including data from social media, email, and other unstructured sources 鈥� as a key driver of business growth and are therefore placing great emphasis on data quality.

 

Generally, smaller companies find it easier to identify and resolve data errors directly and address problems individually due to the smaller overall volume of data they store. Large companies, on the other hand, need a holistic and cost-effective approach to improving their data quality because the risks of business disruption and the associated costs and liabilities are proportionally much higher for them. As a result, resolving data issues often takes longer. Comprehensive data cleansing followed by ongoing data quality assurance is the best way to manage growing data volumes.

 

To develop a scalable strategy for improved data quality, companies must first understand in detail exactly how their data supports their business objectives. A comprehensive data audit starts with the following questions: Which data is central to the company’s processes? What requirements do we have for the data we collect and process, as well as for data from third parties? What standards must the data meet so that we can use it optimally?

Based on this, the audit includes a detailed analysis of existing weaknesses and information gaps, uncovering potential data problems. This provides the fundamental guidelines for future data management, upon which short- and long-term tactics for improving business-critical data and cleansing the entire data set can be built.

Establishing an organizational structure:

The precise approach to defining the new data strategy depends on the size of the company, its strategic business priorities, and the value it assigns to different aspects of data. Furthermore, “data quality” can be defined differently depending on what a process or business unit requires from the data in question. Even from team to team, expectations regarding quality can vary considerably.

When defining data quality goals, it is often helpful to align oneself with established practices of comparable companies and industry standards. Compliance requirements usually need to be considered. Data quality is also a matter of interfaces, as data should be able to be exchanged seamlessly between systems and business units, as well as with suppliers and customers.

Once the foundation is laid and the quality objectives are defined, the company should create a suitable organizational structure to effectively drive the new data strategy. In this context, companies often appoint a Chief Data Officer or designate internal “data champions” to help establish best practices for handling data within the company.

These best practices are fundamentally based on three pillars: people, processes, and technology. Only a solution that encompasses all three areas can meaningfully lead to continuous improvement in data management. All decisions regarding technology, processes, and personnel should be well-considered and made not only within the context of the current situation but also with a view to supporting the long-term business vision. Acting proactively pays off because data volumes will undoubtedly continue to grow in the digital age.

Targeted investment
When selecting a suitable technology for data transformation, data cleansing, and data management, it’s important to remember that cheaper isn’t necessarily better. Depending on the challenges to be addressed, the required investments range from a few thousand to several million euros 鈥� depending on the current data situation, data volume, complexity, and desired retention period.

High-end tools today offer numerous flexible options for defining data quality guidelines, validating data, correcting data errors, and handling exceptions. Detailed reporting functions and automatic alerts when minimum requirements are not met provide a transparent overview of the current data status at all times. Such a platform enables continuous quality assurance, even with growing data volumes.

Savings and Competitive Advantages:
The first investment should definitely be in a qualified and thorough data analysis, as this reveals the potential return on investment that can be achieved by improving data quality. In modern data environments, such assessments, combined with strategic data provisioning, are now indispensable for most companies.

High-quality data not only immediately reduces the time and costs associated with manual error correction or data preparation. Companies that significantly improve their data quality also benefit from smoother processes and valuable insights thanks to reliable data analytics. A comprehensive data strategy also offers another, longer-term advantage: clean data can positively impact a company’s reputation. Customers are satisfied, the brand image is preserved, and hefty fines for potential GDPR violations are avoided.