Solution
Turn Operational Data into Decisions People Actually Use.
Consolidate data sources into governed models and dashboards designed around the questions leaders ask.
Who this is for
Leadership and operations teams that spend days assembling reports, then argue about which number is right.
The problem
Each team calculates revenue, margin or utilisation differently. Reports arrive after the decision is made, and nobody knows how fresh the data is.
Example workflow: weekly pipeline and capacity review
Step 1
Decision
We start from one decision, such as "do we hire or slow sales next month?", and list the questions it needs answered.
Step 2
Definitions
Each metric gets one written definition and owner. For example: "weighted pipeline = open deal value × stage probability".
Step 3
Pipeline
CRM, timesheet and finance data load into a warehouse on a stated schedule.
Step 4
Dashboard
One page shows the answer, the trend and the data's last refresh time, with drill-down to records.
Step 5
Alerts
Thresholds such as capacity below 70% send a short alert to the owner.
Inputs we need
- The decisions the dashboard should support
- Metric definitions and owners
- Access to source systems
- Who may see what
Systems involved
- Source systems (CRM, finance, operations)
- Warehouse such as BigQuery or PostgreSQL
- BI tool such as Power BI or Looker Studio
What you get
- Metric glossary
- Dashboards with visible data freshness
- Data-quality checks and alerts
Exception handling
- Source load fails: dashboard shows a stale-data banner instead of old numbers
- Value outside expected range: flagged for the metric owner
- Definition change: versioned, with the date shown on charts
- Missing records: counted and reported, not silently dropped
Permissions
- Row-level access, e.g. regional managers see their region
- Salary or customer-identifying fields restricted to named roles
- Editing definitions limited to metric owners
Included
- Metric definitions workshop
- Data pipelines and models
- Dashboards and alerts
- Training for report users
Not included
- BI tool licences
- Fixing the underlying processes that create bad data
- Forecasting models unless scoped
What affects cost
- Number of sources and their access method
- Refresh frequency required
- Number of metrics and dashboards
- Row-level security complexity
Illustrative example
A services firm decides hiring from a monthly spreadsheet. A dashboard joining weighted pipeline with booked capacity, refreshed nightly, shows the gap eight weeks out. The hiring discussion moves from opinion to one agreed chart.
FAQs
Data Analytics and Dashboards: common questions
Where do we start?
With one recurring decision and the five to ten metrics behind it, not with connecting every system.
How fresh will the data be?
As fresh as the decision needs and the sources allow — often nightly, sometimes hourly. Every dashboard shows its last refresh.
Which BI tool should we use?
Usually the one your team already licenses. We recommend a change only if it cannot meet a specific requirement.
Can different people see different data?
Yes. Row- and column-level rules are set in the data model, so they apply wherever the data is used.
What if our data is messy?
We measure how messy it is first, then fix what matters for the agreed metrics and report the rest openly.
Discuss data analytics and dashboards for your team
A short call is enough to check fit, the systems involved and a sensible first release.
No obligation. Clear recommendations. Confidential discussion.