Data quality

Data quality has several dimensions, which vary in importance depending on the scenario. Ideally, good data is clear, unambiguous, accessible, relevant, traceable, valid, correct, up-to-date, consistent, complete, and compliant.

Poor data leads to incorrect decisions and lost productivity. It not only causes follow-up costs but also wastes time, resulting in stress for employees and dissatisfaction for customers. Poor data can hinder or prevent the automation and thus scalability of business processes. It can complicate the onboarding process for new employees. Poor data can significantly delay the adoption of AI within a company. It can block the transition to modern business software, leading to stagnation. Finally, poor data can pose significant compliance risks.

In addition to soft factors such as employee satisfaction, criteria for data completeness can be defined and measured. How often are data records processed? What is the error rate of automated workflows? How often do data need to be searched for and supplemented in other systems? Or, on a more abstract level: How many processes can one person handle? Does this number increase with an improved data foundation?

Masterdata Managment These are fundamental, usually long-term valid data about people or products. Examples include customer and supplier data, article numbers, addresses, or material master records. They change relatively infrequently and form the stable basis for business processes. Movement data In contrast, transactional data is constantly generated and regularly changes as part of daily business operations. Typical examples of transactional data include orders, invoices, payments, and inventory movements. It is usually time-limited, exists in large quantities, and is closely linked to processes. Error in master data have a lasting effect (e.g. B. wrong address for all orders). Errors in movement data distort operational processes and key performance indicators (e.g. B. incorrect quantities or prices).

AI can automatically check data, for example, by detecting duplicates, performing plausibility checks, and supplementing data. Furthermore, AI can simplify working with business software, thereby improving process and data quality. For example, employees can ask the AI ​​what function a field has or how a specific process should be carried out. Another important application of AI is eliminating media breaks. AI can take (unstructured) information from technical drawings, photos, emails, chat messages, or phone calls and transfer it as structured and validated data into the ERP system or CRM solution. The use of AI relieves the burden on employees, increases data quality, and ensures process quality. As a positive side effect, efficiency also increases.

Artificial intelligence in a business environment should not rely solely on general AI models, but should also work specifically with company data. An AI can only perform as well as its data foundation. Missing or outdated information leads to flawed results. Data accuracy and traceability must be ensured, as this is the only way to use AI results with confidence. Crucially, data protection and compliance must be guaranteed at all times, ensuring that only authorized personnel have access to sensitive company data. Classification and access rights are also integral components of data quality.

Improving data quality is not a one-time event, but an ongoing process. Should companies permanently allocate personnel and financial resources to optimizing data quality?

Data quality often gets lost in the daily grind, and no one feels responsible. Therefore, the first step for you as a decision-maker is to make it clear within the company that data quality is not just an IT issue, but a business-critical process.

Underline through clear messaging and actions that data quality is a top priority.

Designate responsible individuals for data quality, ideally people who are thoroughly familiar with both the process landscape and the data model of the business software.

Create organizational structures for creating and updating data.

Train your employees and raise awareness of the issue throughout the company.

Review how you measure improvements in data quality to make progress and obstacles transparent.

Provide sufficient human and financial resources for data maintenance and data quality.

Make sure that data quality is already embedded in daily workflows and not just during the final check.

Organizational aspects

• Clear responsibilities: Define Data Owners and Data Stewards who are responsible for the maintenance and quality of the master data.
• Governance rules: Define binding standards for the collection, use, and maintenance of master data.
• Processes and training: Ensure that employees know how to correctly record and maintain master data – and why this is important.
• Company-wide visibility: Master data is not a departmental project, but affects sales, purchasing, production, service and controlling equally.

Technical aspects

• Centralized data storage: Instead of many isolated solutions, a common database, for example via an ERP system such as Microsoft Dynamics 365.
• Duplicate and plausibility checks: Automatic detection of multiple entries and incorrect information.
• Integrations and interfaces: Master data must be consistently available across all systems – without media breaks.
• Automation and AI: Support through intelligent tools that clean, supplement and keep data up to date.
• Versioning and historization: Traceability of changes for compliance and auditing.

Our solutions and services empower you to access better data, leading to improved processes and better decisions. Our customers benefit, for example, from industry-focused best practices.-Practice processes, smart and secure Cloud-Migration methods, AI-gestureüdata maintenance/data collection and our integrated Microsoft platformöWe avoid problems with data silos and interfaces, laying the foundation for automation, scalability, and growth. This is complemented by the extensive experience and expertise of our consultants, who will work with you to create the optimal roadmap for improved data quality.