Data-driven decisions with dashboards and analytics

Depends on: number of data sources to consolidate, complexity of required transformations, number of dashboards/reports, update frequency (real-time vs daily), simultaneous users, need for predictive analysis. Small project (1-2 sources, 3-5 dashboards). Medium project (3-5 sources, 8-12 dashboards). Large project (5+ sources, 15+ dashboards, predictive). We evaluate in initial diagnosis and present options by phases.

Direct connection: Works for simple analysis, few sources, clean data. Advantage: faster to implement. Data Warehouse: Recommended when: multiple sources, dirty data needing cleaning, historical analysis (operational systems don't keep history), need for complex calculations, high query volume. Most medium-large projects benefit from light DW (can be Azure SQL, Snowflake, or even simple relational database). We evaluate and recommend according to case.

Both models: (1) BI capability implementation: We develop dashboards, train your team, they execute ongoing analysis. (2) Analysis as service: Alternative executes periodic analysis and delivers insights (e.g.: monthly profitability analysis with recommendations). Model 1 is more common (sustainable internal capability). Model 2 for companies without internal analyst. We also offer hybrid: we implement + analytical accompaniment first 6 months.

Depends on need and technical viability: Real-time: Dashboards updated every few seconds (e.g.: production monitoring, call center operations). Requires direct connections or streaming. Near real-time: Every 15-30 minutes (e.g.: retail sales). Daily: Nightly update (majority of cases). Weekly/Monthly: For historical analysis or sources that only update periodically. We balance business need vs technical complexity and cost. Majority of clients operate happily with nightly daily update.

Yes, absolutely. We recommend iterative approach: Phase 1 (initial): Most critical core dashboards (typically 5-8). Phase 2 (3-6 months later): New dashboards according to needs that arose. Phase 3 (ongoing): Continuous evolution. Reasons: (1) Usage learning generates new needs, (2) Budget distributed in phases, (3) Incremental change is more manageable. Data architecture designed flexible from start to facilitate expansion at low additional cost.

Yes, we offer training at two levels: (1) Power User: For analysts or technical users who will create new reports and dashboards. 16-24 hour course (theory + practice with real company data). Includes: data modeling concepts, DAX/calculations, visualization best practices. (2) End user: For executives and management who USE dashboards but don't create them. 4-8 hour course focused on interpretation and use. Objective: autonomy so your team can create new reports without Alternative (sustainability).