Decision and metric map
A list of the decisions you make, the reports and measures behind them, and which of those need more reliable data.
Data and analytics strategy
We trace reporting problems to their sources and plan the repairs, from conflicting calculations to missing records and the manual work behind each report.
ScopeA focused review or a staged program
Delivery timingUsually 1 to 3 weeks for a focused review; larger programs run in stages
How we check itEach proposed change names the report or application it supports and the data problem it fixes.
Know which data problems to fix first
Data problems show up as reports people don’t trust and decisions they can’t make. We start with those reports and work back to the fixes that matter most. A new data platform is one option, not the default.
Good fit
- Leaders weighing competing dashboard, data, and AI requests
- Teams whose reports are pieced together from scattered spreadsheets and systems
- Organizations planning to replace or upgrade their data systems
Not the right fit
- Data warehouse projects with no report or application waiting for the data
- Collecting data in case it proves useful later
- Governance work that isn’t tied to any report or system being built
What you get
Source assessment
For each data source, its known quality problems, conflicting definitions, access limits, and the person responsible for it.
Delivery sequence
A practical order for data repairs, data models, reports, and any later AI work, so each step builds on the last.
How it works
Review today’s reports
We go through current reports with the people who use them and note where numbers are reconciled by hand, exports rebuilt, or definitions disputed.
Follow each figure to its source
We trace each important figure back to its source records and to the person responsible for them.
Prioritize the repairs
We rank the repairs by what the first report or application needs, and you approve the order.
Scope, cost, and ownership
What we need from you
- Examples of reports and the decisions they support
- People who can explain source data and approve definitions
What affects cost
- Number of sources and conflicting measures
- Quality of existing documentation
- Scope of the reporting or modernization work
Technical scope
- Source assessment
- Business definitions for key measures
- Data ownership
- Order of integration work
- Semantic model planning (shared calculations)
- Data quality priorities
Support and maintenance
Your team identifies who can approve a calculation or correct a source record. The plan names who will maintain the resulting reports and pipelines.
Common questions
Does this replace an AI strategy?
No. It spells out the data work that a planned AI or analytics system depends on.
Can we start with dashboards?
Yes. Show us a report you use and the figures you have to check or correct, and we will trace those calculations back to their sources.