Data strategy
Data as a lever.
Yoline’s core principle: data is our lever.
Data without architecture is a burden
Most companies have data. Few have a data strategy. Excel lists, tools and reports side by side, without any link to a system.
Five fields of work
Data model
Which entities, which fields, which relationships. Built architecturally.
Source systems
Which systems supply which data. Pipelines, interfaces, data cleansing.
Reporting hierarchy
Which KPI for which decision level. Data is released selectively. Hierarchy according to responsibility.
KPI definition
Consistent definition, calculation, aggregation. What counts as ‘revenue’, what counts as ‘margin’. Documented unambiguously.
Visualisation
Dashboards for decisions. Readable, focused, guiding action.
Data as the basis for AI and CRM
A data strategy is the basis for AI use cases (see the AI strategy service) and CRM architecture.
Architecture sprint: on request, 4 to 8 weeks. Engagement: on request. Initial conversation of 30 minutes, free of charge.
What comes first, data or CRM?
Both in parallel. The data strategy supplies the model, the CRM supplies the relationship data.
Do I need a BI tool?
Rarely essential. Yoline checks whether reporting needs justify a BI tool or whether existing tools (Excel, native CRM reports, Sheets) are enough. The tool comes after the strategy.
How does data strategy differ from data analysis?
Strategy is architecture: what is captured, aggregated and evaluated, how and where. Analysis is application: what the data actually says. Operational analysis follows in the engagement.