Data platforms and analytics
Turn distributed data into a reliable basis for decision-making.
Inconsistent metrics, manual exports and unclear data flows cost time and trust. SYNEDAT connects data architecture, integration, quality and reporting into a useful solution. A focused data domain demonstrates how information can be delivered reliably and understood by business users.
SYNEDAT PLATFORM
Platform experience for your project
We use these selected tools in SYNEDAT PLATFORM or its delivery processes. We adapt suitable practices to your project and align their integration with your existing systems.
Deployment and platform automation
Kubernetes · Azure Kubernetes Service · Helm · Argo CD · Terraform
Versioned configuration and declarative deployment connect infrastructure and applications. GitOps makes proposed changes reviewable and the desired state explicit. Operational transitions and recovery procedures are still planned for the specific application.
Repeatable changes and clearer responsibility boundaries.
Observability and operations
Prometheus · Grafana · Alloy · Loki · Tempo
Metrics, logs and traces provide different views of applications and platforms. We organize data sources, dashboards and alert paths around specific operating questions. Retention, sensitive data and costs are considered when planning data collection.
Better incident diagnosis and informed operating decisions.
Data, search and messaging
MySQL · Redis · RabbitMQ · Solr · OpenSearch
Data storage, caching, messaging and search have different consistency, access and recovery requirements. We select and connect these components around your business processes. Ownership and maintenance are part of the integration.
Technical components that fit your data and application processes.
From your requirements to practical results
We start with a decision or workflow that needs better data. Business and IT teams clarify sources, definitions and quality requirements together. This produces an initial data flow with understandable metrics and controlled access. Architecture and operations are designed to support further domains as needed.
Agree on business questions and metrics
A report becomes useful when its definitions are clear. We agree the calculation, ownership and business acceptance criteria for key metrics. Differences between sources and existing reports are made visible.
Connect data sources with clear data flows
Interfaces, files, databases and event streams are integrated according to need. Refresh, error handling and responsibilities form part of each data flow. We assess where manual intermediate steps can be replaced.
Build an appropriate data architecture
Warehouse, lakehouse and other structures are assessed against usage, data types and operational needs. Storage, processing and permissions are planned together. Existing tools and skills are considered.
Make quality and lineage testable
Quality rules define whether data is suitable for its intended use. Sources and processing steps are documented clearly. Detected issues follow a defined process to the people responsible.
Make reports and analytics actionable
Dashboards and data models support specific decisions. Metrics, filters and access are tested with business users. Documentation and training help users interpret and reuse the results correctly.
Define ownership and operations
Data products need business and technical owners. We plan monitoring, updates and change handling. Access and approval rules are agreed with your organization.
A clear path to deployment
Select a focused data domain
We define the business question, available sources and required freshness. Metrics and quality criteria are agreed together.
Build the path from source to use
A bounded data flow connects integration, preparation and analysis. Business users validate results using real cases.
Operate and expand reliably
Monitoring, responsibilities and change procedures are documented. Further data domains are prioritized according to value and available foundations.
What you receive
- An agreed data model and clearly defined metrics.
- A testable data flow with quality rules and traceable lineage.
- Useful reports and analyses, with agreed responsibilities and operating procedures.
Three ways to get started
Data and analytics assessment
For a focused start: business questions, sources, metrics and prioritized data requirements.
Discuss your project: Data and analytics assessmentFirst usable data product
For visible results: bounded integration, quality rules, a data model and useful business analysis.
Discuss your project: First usable data productBuild and operate the platform
For further domains: an agreed architecture, additional sources, responsibilities and operational handover.
Discuss your project: Build and operate the platformQuestions before you decide
Where should we start building a data platform?
Start with a specific business question and a focused data domain. This lets you test sources, quality and usage together. Architecture decisions reflect the initial need and a realistic plan for expansion.
Do we need a data warehouse or a lakehouse?
This depends on data types, analysis needs, existing tools and operational requirements. We compare suitable structures based on your specific use. The selection should enable a sustainable data flow and be comprehensibly justified.
Can existing BI tools continue to be used?
Yes. Existing data models, reports and licenses are included in the planning. We examine where data quality, modeling or integration can be improved. Replacement is a justified option, not a requirement.
How can users trust the metrics?
Definitions, sources and calculations need to be understandable to business users. Shared test cases and quality rules help identify discrepancies. Designated owners approve the results and decide how open issues should be handled.
Does our data need real-time processing?
Only if the workflow benefits from lower latency. More frequent updates can increase processing and operational demands. We align required data freshness with the actual decision-making need.
What happens if data is incorrect or missing?
Quality rules and technical monitoring make defined deviations visible. A coordinated process clarifies evaluation, correction and reprocessing. Depending on use, data can be withheld, labeled or made available with documented restrictions.
How are sensitive data and access handled?
Data ownership, usage and permissions are considered together. We plan suitable access rules and account for processing and retention requirements. Legal interpretation is handled with your responsible specialists.
How can the benefits of an initial project be assessed?
Suitable criteria may include less manual preparation, clearer metrics or more reliable updates. The starting point and desired improvement are agreed in advance. Business users assess the solution using representative tasks.
Which decision needs a better data foundation?
Describe the analysis you need, existing sources and current difficulties. We help define an initial data domain with testable results.
Discuss your project