Turning your data into support for everyday work.

Yes. The 6 services can be planned as clearly scoped work packages. Together we clarify the situation, the prerequisites and the result you need first. The subpages show the respective scope and the technical decisions.

Before implementation, we agree on goals, affected systems, involvement, results and acceptance criteria. Open technical questions can first be clarified in a current-state review or a pilot. Additional tasks are assessed for their impact on effort and schedule.

Documentation, briefing and responsibilities are covered in the engagement. If OTOKO® takes on ongoing tasks, we agree on an operating scope for them, including service hours and escalation paths. Your team can also take the results forward itself or with existing partners.

AI alone does not answer questions of data quality, responsibility or cost-effectiveness. That is why we separate the tasks: making data available, understanding key figures, testing predictions and integrating generative models in a meaningful way. You can order each area on its own. For a complete project, we combine the results into a solution your employees can use within their existing workflows.

Reports, forecasts and assistants need reliable data. OTOKO® plans and implements a data platform that brings your sources together, controls access and provides data for specific tasks. The starting point can be a single domain, such as sales, production or service.

Target architecture with storage layers, zones for raw and prepared data and an access concept Build with Terraform and Kubernetes, reproducible for test, acceptance and production Data catalog with lineage, owners, quality rules and retention periods Encryption at rest and in transit, key management in an HSM on request Monitoring, backup and recovery with documented recovery times

Operational data platform with infrastructure as code Data catalog and data model Operations manual with access and emergency concept

The decisions that data should support come first: which datasets must be available daily, which events immediately and which historical values are needed. From this, we derive the data model, storage needs, update frequency and owners. A data lake takes in different types of raw data; a warehouse provides curated data for analysis. A lakehouse combines these properties but does not replace data ownership. An existing platform is first checked for extensibility before a new one is introduced.

We design separate processing layers with traceable transitions. Incoming data is tagged with its origin, load time and a schema; quality rules decide which data is processed further and which is routed for clarification. Repeatable load processes prevent a restart from duplicating figures. Roles are tied to data domains, development access is restricted and retention rules are built into the technical setup. Architecture decisions on file formats, partitioning and compute capacity are tested against your queries and data volumes, so inexpensive storage does not lead to disproportionately expensive analyses.

For acceptance, we look not only at a successful analysis but also at missing sources, damaged inputs and restarting after a failure. Your team receives the data model, an operations manual and named responsibilities. For planning, sample reports, a list of data sources, known data volumes and the intended user groups are helpful. A data catalog shows what has been captured and where the picture is still incomplete.

When figures from ERP, CRM and production do not match up, a nicer dashboard will not help. We develop data pipelines that connect sources, standardize formats and handle faulty inputs visibly. Your business unit can trace where a value comes from and when it was last updated.

Connection of SAP, Microsoft Dynamics, Salesforce, databases, files and APIs Batch and streaming pipelines with validation rules for format, duplicates, completeness and plausibility Versioned pipelines with automated tests and a release process Traceable data lineage from the source to the report Operations with alerting on failed runs and quality violations

Pipelines as code with tests Validation rule set and quality reports Lineage record per data set

ETL means transforming data before loading it into the target; with ELT, processing takes place mainly in the target platform. We choose based on data volume, protection needs and available compute resources. More important than the acronym are rules for mandatory fields, units, time values and business keys. A missing price, for example, must not silently become zero. Faulty records get a traceable clarification path with an assigned owner and options for corrected processing.

For incremental processing, we store progress and lineage. Delayed events, later corrections and deleted source data need their own rules. Change data capture can record database changes; we check whether that fits based on the database, permissions and the impact on source operations. Versioned transformations and test datasets safeguard changes to columns and formats. For repeated deliveries, we use stable identifiers or matching rules, so an invoice delivered again does not appear twice in the analysis.