Reporting & Data Quality Sprint
Turn fragmented spreadsheet and ERP extracts into traceable, maintainable reporting workflows.
Sample reporting check
ERP export + spreadsheet
Item ID and reporting month must identify a record.
Duplicate key → manual review
Synthetic illustration; no measured client productivity result.
When the same report becomes manual work every month
A recurring reporting bottleneck may involve fragmented spreadsheets, changing ERP extracts, copy-and-paste steps or inconsistent KPI definitions. The sprint tackles an agreed bottleneck rather than promising to redesign all reporting.
Conflicting numbers
The same KPI has different values in different reports. Source grain, filters and reference dates need to be reconciled first.
Fragile refresh
Exports have to be imported, cleaned and combined manually, with undocumented intermediate steps.
Quality issues surface late
Missing identifiers, duplicate records and invalid dates are discovered only after a report has circulated.
What a scoped sprint needs
Source files and access conditions are agreed separately. An initial conversation needs only the reporting objective, a description of available exports and an outline of the current workflow; please do not upload confidential data to this website.
Approved source extracts
For example agreed Excel/CSV exports from ERP or CRM systems, with field information and a business owner for questions.
KPI and business rules
Which calculations, units, cut-off dates, filters and mappings apply? Who signs off on the business definition?
Checks and accountable reviewers
What sample values should reconcile? Who reviews exceptions? What format will make the outcome usable?
From raw extracts to a repeatable routine
The tool choice follows the actual scope and maintenance needs: Power Query, Excel, SQL or Power BI where appropriate and agreed.
Define the objective and boundary
Record the reporting cycle, users, source data, KPIs and acceptance criteria.
Profile source quality
Assess types, missing keys, duplicates, date validity and available joins.
Build transformation and checks
Implement a traceable data preparation path, validation rules and KPI logic.
Reconcile with business owners
Compare agreed samples against the source inputs and resolve or document exceptions.
Document the handover
Transfer the workflow, source assumptions, KPI definitions, known limitations and runbook.
What a project may produce
Final deliverables are selected in the written scope. These examples describe possible outputs, not an unlimited package.
Repeatable transformation
Defined import and preparation steps with assumptions about file formats and refresh cycles.
KPI dictionary
Metric definition, formula, filters, reference period and named business owner.
Data quality log
Tests, exceptions, incomplete fields and an agreed prioritisation of follow-up work.
Review evidence
Reconciled samples, tolerances and explicitly documented ambiguous cases.
Handover / runbook
How to refresh, check and maintain the workflow as sources change.
Known limitations
Uncovered sources, unresolved edge cases and remaining manual review tasks.
No unlimited integration or guaranteed time savings
The work applies only to an agreed process and data scope. New connectors, write access to production systems, continuous monitoring and promised savings are not automatically included.
A practical first conversation
Describe the recurring report, the source formats available and the steps that repeatedly require manual checks.
Do we need to replace our entire reporting setup?
No. A bounded workflow is often a more useful starting point. Any redesign is agreed after reviewing the current setup.
Who approves KPI definitions?
An authorised business owner confirms the formula, filters and samples. KERNROW documents the implementation and unresolved assumptions.
Related information
Define a practical scope before committing to a project.
We review source data, checks and acceptance criteria before a proposal. No blanket delivery or savings guarantee.