ViBe://AUTOMATE
Turn scattered business data into the dashboards your leadership actually uses.
Most leadership teams make decisions with incomplete data — not because the data doesn't exist, but because aggregating it across systems takes manual effort that no one has time for. We build the pipeline so the dashboards update themselves.
Current manual process
How reporting gets done today.
- Someone exports CSVs from each system at reporting time
- Data is pasted into a spreadsheet and manually combined
- Formulas applied, errors corrected, formatting cleaned up
- Report shared via email — snapshot in time, already outdated
- Next week, the entire process repeats from scratch
- Leadership makes decisions based on data that may be days old
Cost of manual reporting
5–8 hours per week plus decision lag.
- Reporting cycles that consume analyst and ops time every week
- Data that's stale by the time it reaches decision-makers
- Inconsistent metrics — different people pulling numbers differently
- No single source of truth across departments
- Leadership flying blind between reporting cycles
Automated flow
Extract → Transform → Load → Visualize.
Human checkpoints
Where your team validates and interprets.
- Data anomaly review — the pipeline flags unusual values, your team investigates
- Business logic changes — metric definitions updated by human decision
- Dashboard design and KPI selection — built with your leadership team, not for them
- Strategic conclusions drawn from the data — always a human interpretation
Systems involved
Source systems and reporting destinations.
CRM
Sales pipeline, deal velocity, and revenue data.
Accounting software
Revenue, expenses, AR/AP, and cash flow data.
Helpdesk
Ticket volume, resolution time, and customer satisfaction data.
Project management
Utilization, milestone data, and delivery metrics.
Data warehouse
Snowflake, BigQuery, or Redshift — clean consolidated data layer.
BI tool / dashboard
Looker, Tableau, Power BI, or custom — where leadership views data.
Expected capacity recovery
Illustrative weekly time savings.
Per week recovered
5–8 hrs
Illustrative estimate
Implementation time
3–6 wks
Depends on source complexity
Dashboard refresh cadence
Daily
vs. weekly manual reports
Capacity estimates are illustrative ranges based on typical reporting complexity. Actual hours recovered depend on number of source systems, data quality, and reporting cadence requirements.
Implementation
Live in 3–6 weeks.
01
Discovery
Map your data sources, reporting needs, and the decisions leadership needs to make.
02
Build pipeline
Configure extraction, transformation, and loading for each source system.
03
Build dashboards
Design and build reporting views with your leadership team.
04
Handoff
Dashboard goes live. Team trained on reading data. Anomaly alerts activated.
FAQ
Common questions.
Do you build custom dashboards or use existing BI tools?+
Both. We can build data pipelines that feed your existing BI tool (Looker, Tableau, Power BI) with clean, structured data — or build a custom dashboard when the standard tools aren't the right fit.
What if our data lives in many different systems?+
That's the common case. We build the extraction and normalization layer that pulls from each source system, cleans and joins the data, and produces a unified reporting layer. The complexity is in the setup, not in ongoing maintenance.
How often does the data refresh?+
Refresh cadence is configured based on your data source capabilities and reporting needs — from near-real-time to daily or weekly aggregations. Not every data point needs to be live.
Get started
Build your data pipeline.
Book a demo to walk through your current reporting workflow and data sources. We'll scope a build plan with source integrations, transformation logic, and a dashboard design session.