Step 1
Assess the Current State
We review your current setup, constraints, and priorities so business data analytics is scoped against the real operating context.
Step 2
Deliver the Agreed Scope
We execute the agreed work with clear milestones, practical communication, and visible progress against defined outcomes.
Step 3
Review Outcomes and Next Actions
We close with recommendations, implementation notes, and clear next steps to sustain the gains from the engagement.
Step 4
Step 1
Assess the Current State
We review your current setup, constraints, and priorities so business data analytics is scoped against the real operating context.
Step 2
Deliver the Agreed Scope
We execute the agreed work with clear milestones, practical communication, and visible progress against defined outcomes.
Step 3
Review Outcomes and Next Actions
We close with recommendations, implementation notes, and clear next steps to sustain the gains from the engagement.
Step 4
Why not just buy Power BI or Tableau and be done?
Power BI and Tableau are tools. We build the analytics function. the data model, the metric definitions, the dashboards that match how your business actually decides things, and the governance so the numbers don't drift. The tool comes second, after the function is designed.
How long until we have usable dashboards?
Standard SME engagement (10-15 source systems, 4 dashboards, 8 KPI definitions): kick-off to first usable dashboard in 6 weeks, full production by week 12. We never deliver dashboards faster than week 6 because the underlying data model has to settle first.
Do you replace our analyst?
No. Most of our SME clients have either no analyst, one overworked analyst, or an analyst who is technically strong but lacks senior support. We sit alongside, do the architectural work, and either upskill your person to take it on or run it for you on retainer. We have never replaced an in-house analyst.
What about data quality. our data is a mess?
Standard scope. The first phase of every engagement is a 4-week data-quality and lineage audit: where data lives, who owns it, what it is supposed to mean, and what cleanup the source systems need. We will not build dashboards on dirty data because the resulting decisions are worse than no dashboards.