Many analytics and AI projects fail not because of the idea itself, but because of the data. The data is available, but it is scattered across source systems, inconsistent or poorly documented. No one can say with any certainty which figure is correct.
We have a dedicated format for this: the Data Sprint. Over a period of four to eight weeks, this results in a robust, accessible and well-documented database that can serve as the foundation for analytics applications and AI projects.
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If the general direction has not yet been determined, a data strategy is the first step. If, on the other hand, the aim is to report on existing, clean data, the most direct route is via a BI project.
Let’s work together to clarify which data sources we’ll connect to and what can realistically be achieved within four to eight weeks.
automated pipelines from the source systems to the reporting layer.
Data dictionary, lineage, quality rules and governance guidelines for your in-house team.
The final point is the most important one for the future. Not only will you have a fully functional database, but you will also know how it can be usefully expanded.
Weeks 1 to 2
Weeks 2 to 3
Weeks 3 to 5
Weeks 5 to 6
Weeks 6 to 8
Let’s work together to clarify which data sources we’ll connect to and what can realistically be achieved within four to eight weeks.
The Data Sprint does not employ ML engineers or AI architects, but rather data specialists with a focus on architecture and engineering
Data Architect
A Data Architect is responsible for the technical target architecture, data model design and platform selection.
Data Engineers
Analytics Engineers
Sprint Lead
A database is only as good as our understanding of the systems that feed into it. collana implements these systems itself, ranging from Microsoft Dynamics 365 Business Central through to specialist applications and e-commerce platforms. We don’t need anyone to explain the source structure to us.
Once the sprint is over, the work remains integrated. The same teams that implement your BI projects will take over responsibility for reporting and dashboards. For AI projects, the path to an AI sprint is open.
A time-sensitive project aimed at establishing a robust, accessible and well-documented database for analytics and AI initiatives. The focus is on data architecture, data quality and integration.
The data strategy sets out the direction, responsibilities and target architecture, and concludes with a decision paper. The Data Sprint implements this direction and concludes with a usable data platform, complete with governance. Anyone who does not yet have a target vision should start with the strategy.
Not necessarily. In the first phase, we’ll determine which architecture is suitable based on your system landscape and your requirements. This approach is designed specifically for companies that do not wish to embark on a data warehouse project lasting several months.
Your team will take over a documented database, complete with a data dictionary, lineage and quality rules. Reporting applications, additional data sources or an AI project can be built on top of this. Recommendations for the next steps form part of the deliverable.
Yes, the handover is designed with this in mind. Documentation and training are integral parts of the final phase. If requested, we will continue to support the operation.
ERP and CRM systems, specialist applications, databases, e-commerce platforms, production data and external sources via interfaces. The focus is on the Microsoft platform, but integration is not limited to this.
During the free initial consultation, we’ll review your data situation and work with you to define the scope of the project. This will help us determine whether a Data Sprint is the right approach or whether you should start elsewhere first.