Data Integration & Data Quality

End Data Silos

Start with clear, usable data. We bring your scattered enterprise data into a central, error-free structure so teams can work faster, reports are accurate, and automation finally becomes possible.

Schedule Your Free Consultation Now

AI-ready, scalable
data structures

100% data sovereignty –
GDPR-compliant

Automated
quality assurance

Challenges

Fragmented data costs time, money, and control

In many companies, data is scattered across systems, departments, and formats. This slows down the organization, increases costs, and makes reliable management difficult.

Time and cost loss: High manual effort for coordination, data maintenance, and reporting instead of value creation.
Uncertain decision-making: Different data states lead to contradictory metrics and poor decisions.
AI remains patchwork: Without accessible, consistent data, AI applications cannot be operated stably in production.
Compliance and audit risks: Permissions, data flows, and changes are difficult to trace.

A consolidated, high-quality data foundation is the prerequisite for implementing automation and AI economically and at scale.

Solutions

From data silos to a future-proof data platform

We combine proven data science methods with modern AI to bring order to your data landscape and make it permanently usable. Depending on your security and architecture requirements, we implement the solution cloud-based, hybrid, or completely on-premise.

Data Cleansing & Data Integration: We harmonize data from your relevant sources, remove duplicates and contradictions, validate content, and bring everything into a consistent structure. This creates a reliable foundation for reporting, automation, and AI.
Quality Analysis & Monitoring: Automated checks ensure data quality permanently. A dashboard makes trends, deviations, and optimization potential visible so data quality can be continuously improved.
Data Modeling & Governance: We establish a standardized, documented data model with clear responsibilities and rules. This creates transparency, reduces coordination effort, and accelerates new use cases.

Where could your data organization run more smoothly? Schedule a consultation

Customer Success

More transparency for tickets and permissions

A client was using Serviceware Processes but struggled with highly variable ticket quality. Relevant information was often buried in emails or activity notes, and a consolidated evaluation of granted permissions was not possible.

Our Approach

Building a data pipeline for ticket and processing data with automated import

Processing unstructured content (e.g., PDFs, screenshots) for automated analysis

AI-powered analysis of free texts/emails as well as structured capture and evaluation of access rights

The goal of our collaboration was to create transparency. We generated a consolidated data foundation with all relevant information – ready for analysis and visualization.

MacBook dashboard screenshot - AI analytics dashboard with data visualization

Benefits

What you gain with an integrated data foundation

With a consolidated data foundation, you solve the typical problems of distributed systems. IT teams gain freedom for strategic tasks and companies can more easily benefit from innovative technologies.

Faster Management

Unified metrics instead of contradictory reports.

Higher Efficiency

Less manual work through standardized data flows and automation.

Less Risk

Better traceability for audits, permissions, and compliance.

Better AI Readiness

Data that is accessible, consistent, and reliable enough for productive AI.

Scalability

Data models and governance that grow with new requirements and systems.

Approach

Our proven project approach gets you to your goal faster

1. Kickoff Workshop

You explain your goals to us and we determine together which data you need or need to consolidate for this purpose.

2. Data Analysis

We comprehensively analyze which data exists where and in what quality from data sources: If new data sources are to be integrated into your IT infrastructure, we handle the interface configuration.

3. Data Cleansing and Consolidation:

Subsequently, the actual data integration of all internal and external data sources takes place.

Free Initial Consultation

Let's talk about your data

Learn how we optimize your data integration, increase data quality, and provide you with a reliable foundation for automation & AI

Schedule consultation

FAQ

: By consolidating data from ticketing systems, monitoring tools, CMDBs, or Excel spreadsheets, a unified view of your IT landscape emerges. For service managers, this means less duplicate work, for IT leadership reliable metrics, and for auditors consistent evidence. Decisions are based on solid facts and processes can be more easily automated.
: The process has three stages: kick-off for goal clarification, analysis and connection of data sources (e.g., tickets, monitoring, inventory), then cleansing and consolidation. The internal effort is usually limited to providing access and contacts. For business units and IT leadership, this means: little operational burden, but ultimately a central, reliable data foundation.
: We integrate classic IT systems (ticketing, CMDB, monitoring), office data (Excel, CSV), unstructured content like emails or PDFs, as well as logs. AI-powered language models even structure free text, so comments in tickets or email attachments can be included in analyses. This way, IT leadership, controlling, and service teams all benefit equally from a complete view of the data.
: Through interfaces and parsers, we consolidate data sources like ticketing systems, CMDBs, or Excel files. AI-powered models detect duplicates, harmonize contradictory information, and prepare free texts so they are usable for analyses. The result is a consistent data foundation that IT leadership, service managers, and reporting teams can reliably use.
: Together with you, we define which criteria are important – such as completeness of ticket fields, consistency of CMDB entries, or currency of monitoring data. These KPIs are continuously checked and displayed in monitoring, e.g., in a dashboard or as a regular report. IT teams can thus make targeted adjustments, while management keeps trends and developments in view.
: We create a standardized data model that maps and documents all relevant entities and relations (e.g., tickets, systems, services). Versioning ensures that changes remain traceable. Additionally, responsibilities can be defined – such as which business unit is responsible for certain datasets. This keeps the data foundation consistently usable for IT leadership, auditors, and business units.
: The result is a cleansed and consolidated data foundation with a documented data model and monitoring of data quality. For IT leadership, this means: reliable metrics. For service teams: less friction in daily work. For auditors: traceable, consistent data. Based on this foundation, you can implement reporting, visualizations, or AI analytics in the next step – for this, we refer to our service Data Analytics.
: Our data models are modularly constructed and versioned. New systems (e.g., a different ticketing tool) or additional fields can be integrated with manageable effort. Thus the data platform grows flexibly – and IT leadership retains a consistent, usable data foundation for reporting and compliance even during changes.