Reliable decisions start with reliable data. We support clients across all industries and company sizes in the targeted implementation of their data quality frameworks.
Why DQ?
Reliable Data for Better Processes, Decisions, and AI Applications
Whether in commerce, manufacturing, finance, or regulated industries, data drives processes, customer interactions, reporting, and, increasingly, automated decisions.
Data quality does not mean that all data must be perfect at all times. What matters is that it meets the requirements of the respective business process and can be reliably used for its intended purpose.
We help companies make data quality measurable, improve it in a targeted manner, and embed it permanently in processes and systems—from the analysis and definition of business-specific quality rules to technical implementation and continuous monitoring.
Let’s talk about your data quality requirements:
experts@striped-giraffe.com
Challenge
Poor data quality rarely goes without consequences
Duplicate records, missing required fields, or conflicting information may initially seem like minor technical issues. In reality, however, they impact operational processes, customer experiences, and business decisions.
Typical causes include:
- inconsistent data entry across different systems,
- missing or inadequate validation rules,
- unclear responsibilities,
- manual data transfers and media breaks,
- differing definitions across departments,
- outdated or incomplete data records,
- lack of transparency regarding origin and processing,
- legacy system landscapes.
Possible consequences:
- incorrect reports and metrics,
- inefficient or interrupted processes,
- incorrect orders, deliveries, or invoices,
- unreliable customer and product information,
- additional verification and correction efforts,
- compliance and reputational risks,
- data unsuitable for analytics and AI.
what DQ
encompasses
Data quality is determined by the specific intended use
Data is not inherently good or bad regardless of its intended use. For example, a shipping address may be sufficient for a marketing analysis but unusable for delivery.
That is why we define data quality based on specific business requirements and measurable quality dimensions.
Completeness
Is all information required for a process available?
Accuracy
Does the data correspond to actual circumstances and business rules?
Consistency
Is information consistent within a system and across system boundaries?
Timeliness
Is the data still valid and relevant at the time of use?
Uniqueness
Are customers, products, suppliers, or other objects uniquely identified?
Validity
Do values conform to the specified formats, value ranges, and business rules?
Integrity
Are relationships between data objects complete and logically correct?
Availability
Is the required data available to authorized users and applications in a timely manner?
Not every dimension is equally important for every process. We therefore work together to determine which criteria are relevant for the specific use case and which thresholds must be met.
Shared
Responsibility
Business Units and IT Must Work Together
IT can technically implement quality rules, create data profiles, and operate monitoring platforms. However, only the responsible business units can assess whether a dataset is technically correct and suitable for a process.
We are therefore creating a shared operating model with clear roles:
Data Owner
Is responsible for business-specific quality goals and prioritizes improvement measures.
Data Steward
Monitors data quality in day-to-day operations, analyzes problems, and coordinates their resolution.
Business Units
Define business rules, quality requirements, and relevant thresholds.
IT and Data Teams
Implement rules, integrations, monitoring, and technical controls.
Governance and Compliance Functions
Ensure that policies, security requirements, and regulatory mandates are adhered to.
Data quality thus becomes an integral part of operational responsibility – not merely a downstream control process.
Data Quality
by Design
Qualität dort sichern, wo Daten entstehen
Fehler lassen sich am effizientesten vermeiden, wenn Datenqualität bereits bei der Erfassung, Änderung und Verarbeitung berücksichtigt wird.
Dazu gehören beispielsweise:
- verständliche Eingabemasken und Pflichtfelder,
- fachliche und technische Validierungen,
- kontrollierte Wertelisten,
- automatische Plausibilitätsprüfungen,
- Dublettenprüfung bei der Datenerfassung,
- Freigabe- und Korrekturworkflows,
- Qualitätsprüfungen an System- und Prozessgrenzen,
- klare Verantwortlichkeiten für Ausnahmen.
So wird Datenqualität nicht erst nachträglich gemessen, sondern aktiv in Prozesse und Anwendungen eingebaut.
DQ for
Analytics
and AI
Reliable results start with the data
Analytics and AI applications cannot automatically compensate for existing quality issues. Incomplete, distorted, or misclassified data leads to unreliable analyses and results.
We help companies systematically evaluate and validate their data sets for analytics and AI. This includes, among other things: This creates a reliable foundation for reports, forecasts, automation, and productive AI applications.
- Analysis of relevant data sources,
- Verification of completeness, consistency, and representativeness,
- Identification of outliers and anomalies,
- Documentation of origin and processing,
- Quality rules for training, reference, and context data,
- Continuous monitoring of production data flows,
- Definition of approval and escalation criteria.
Technology &
Automazation
Continuously Monitor and Improve Data Quality
Modern data quality platforms help companies automatically profile data, enforce rules, detect anomalies, and continuously monitor quality metrics.
Depending on your requirements, we implement solutions such as: Our approach remains technology-agnostic. We evaluate which tools are best suited to your existing environment and specific use case—ranging from established platforms to custom-developed components.
- Data profiling and automated inventory analyses,
- rule-based validation,
- matching and duplicate detection,
- anomaly detection,
- automatic correction suggestions,
- data quality dashboards and alerts,
- workflows for analyzing and resolving errors,
- quality checks in batch, streaming, and API processes,
- integration with MDM, PIM, ERP, and analytics platforms.
Whether it’s SAP, Stibo Systems, Informatica/Salesforce, Akeneo, or custom in-house developments—we have the necessary interface expertise to implement your data quality processes efficiently and in a future-proof manner.
Our
Approach
From Analysis to Continuous Improvement
Phase 1: Analyzing Data and Processes
We examine relevant data sets, systems, and business processes and identify the quality issues with the greatest business impact.
Possible outcomes:
- automated and domain-specific data profiling,
- analysis of critical data objects,
- identification of error sources,
- assessment of existing controls,
- prioritization based on business impact,
- baseline for key quality metrics.
Phase 2: Defining Quality Requirements
Together with the business units, we translate business requirements into measurable rules and target values.
Possible outcomes:
- relevant quality dimensions,
- domain-specific and technical data quality rules,
- KPIs and thresholds,
- roles and responsibilities,
- escalation and correction processes,
- prioritized roadmap.
Phase 3: Implement Rules and Processes
We embed data quality into the relevant systems, data pipelines, and operational processes.
Possible outcomes:
- automated validations,
- matching and cleansing logic,
- quality checks at interfaces,
- data stewardship workflows,
- integration into MDM, PIM, ERP, or data platforms,
- proof of concept or pilot.
Phase 4: Continuously Monitor and Improve
Data quality evolves alongside systems, processes, and business requirements. That’s why we establish ongoing monitoring.
Possible outcomes:
- KPI dashboards,
- alerts and escalation procedures,
- root-cause analyses,
- measurement of trends and improvements,
- regular review of rules,
- continuous optimization.
Business
Value
More Than Just Better Data
More Efficient Processes
Fewer errors, fewer follow-up inquiries, and fewer manual corrections speed up operational workflows.
Lower Costs
Automated checks and early error detection reduce the effort required for corrections, error-related costs, and redundant data maintenance.
More Reliable Decisions
Consistent metrics and reports provide a solid foundation for operational and strategic decisions.
Better Customer and Product Experiences
Up-to-date and complete information improves communication, service, commerce, and cross-channel processes.
Greater Automations
Reliable data enables processes to be automated more securely and reduces the need for manual checks.
Better Foundations for Analytics and AI
High-quality data increases the reliability of analyses, forecasts, and AI applications.
Fewer Compliance Risks
Clear rules, documented controls, and traceable correction processes support regulatory and internal requirements.
Let’s talk
Whether you need to gain insight into the state of your data, automate quality rules, or establish a company-wide framework, we’ll support you every step of the way—from analysis through ongoing operations.
Just contact us:
- experts@striped-giraffe.com
- +49 (0)89-416 126-667
We will be happy to support you.
FULL-SERVICE FOR YOUR DIGITAL CHALLENGE
No matter what digital challenge you are facing, we will support you. With various specialists in our team and our network of experts, we find the right solution for every problem.
