How dashboards, reports, and analytical outputs accurately and consistently represent the organisation's data through agreed definitions and calculations.
DST-CON-01
Availability of self-service analytics
How well are you equipped with self-service business intelligence and visual analysis tools that let you answer your own business questions, rather than having to request every answer from a central team?
Maturity level descriptions
No self-service capability is available; every analytical question must be requested from a central team or an individual with technical skills. The Data Consumer submits a request and waits for someone else to produce every figure, chart or breakdown they need.
Some business users have obtained tools independently, with no organisational provision, support or standard. The Data Consumer works in spreadsheets or a tool they sourced themselves, unsupported and inconsistent with what others use.
A defined self-service analytics capability is provided, supported, and made available to business users who need it. The Data Consumer has access to a supported organisational tool and can build or modify their own views of approved data.
Self-service provision, adoption and usage are measured, with access aligned to business need and support arrangements maintained. The Data Consumer’s use of the tool is measured, provisioning follows a defined process, and support is available when they need it.
Self-service is the default operating model, continuously improved on the basis of usage analytics, with central teams focused on enabling rather than servicing requests. The Data Consumer answers the substantial majority of their own questions, and central analytical effort is directed to capability rather than to routine requests.
DST-CON-02
Capability to use analytics tools
How well prepared are you, through training and support, to use the analytical tools available to you correctly and confidently?
Maturity level descriptions
No training or support is provided; whatever capability the Data Consumer has is self-taught or absent. The Data Consumer works out the tool alone, or avoids it, with no training offered and no one to ask.
Training is occasional and generic, offered inconsistently, with support dependent on finding a helpful colleague. The Data Consumer may have attended a one-off session with no follow-up, and relies on a colleague for help.
A defined training offering and support route exist for business users of analytics tools, communicated and accessible. The Data Consumer has completed role-appropriate training and knows where to get help when they get stuck.
Capability is assessed and tracked, training is targeted to identified gaps, and support performance is measured. The Data Consumer’s capability level is known, training is matched to their actual gaps, and support response is measured.
Capability development is continuous and embedded, with in-tool guidance, communities of practice and capability treated as a measured business outcome. The Data Consumer receives contextual help within the tool, participates in a user community, and capability growth is tracked as a business measure.
DST-CON-03
Certified and authoritative assets
When several reports or dashboards appear to answer the same question, how clearly can you tell which one is the authoritative, approved version?
Maturity level descriptions
Nothing distinguishes authoritative from unofficial assets; the Data Consumer cannot tell which of several similar reports to believe. Multiple versions of the same report circulate with no indication of which is approved, and choices are made on familiarity.
Some assets are informally regarded as “the official one”, known through convention rather than any marking or record. The Data Consumer knows by habit which report is usually used, but nothing on the asset itself confirms this.
A defined certification process exists and certified assets are visibly marked, with business users shown how to identify them. The Data Consumer can see a certification indicator distinguishing approved assets from personal or draft ones.
Certification coverage is measured and maintained, uncertified proliferation is actively managed, and consumption of certified assets is tracked. The Data Consumer finds a certified asset for essentially every significant business question, and duplication is actively reduced.
Certified assets are promoted by default in discovery and the surrounding controls make uncertified duplicates rare, with certification maintained continuously. The Data Consumer is presented with the certified asset first as a matter of course, and certification status is maintained automatically as assets change.
DST-CON-04
Consistency of measures across reports
When the same measure appears in more than one report or dashboard, how consistently does it show the same value, calculated the same way?
Maturity level descriptions
The same measure routinely differs between reports, with no explanation available; the Data Consumer cannot reconcile the difference. Two reports give different figures for the same measure and nobody can explain which is correct.
Differences are known anecdotally and explained case by case when challenged, without lasting resolution. The Data Consumer learns from an analyst why two figures differ, and the same question arises again next month.
Standard calculation definitions exist for priority measures and are applied to certified reporting, with differences documented where they persist. The Data Consumer finds consistent values for priority measures across certified reports, with any remaining variance explained in documentation.
Consistency is measured across business-facing reporting, discrepancies are tracked as defects and remediated on a defined cycle. The Data Consumer sees consistent figures across reports, and any discrepancy they raise is logged as a defect and fixed.
Measures are calculated once in a governed semantic layer and consumed everywhere, making inconsistency structurally improbable. The Data Consumer relies on figures matching across reports because every report draws the measure from the same governed definition.
DST-CON-05
Standardised performance reporting
How well does the standard performance and KPI reporting you receive give you a consistent, comparable view of business performance over time and across business areas?
Maturity level descriptions
No standardised performance reporting exists; each area produces its own, in its own way, with no comparability. The Data Consumer receives performance information in an inconsistent format that cannot be compared with other areas or prior periods.
Some standard reporting exists for a few areas, produced manually and inconsistently, with definitions varying between producers. The Data Consumer receives a regular report assembled by hand, whose content and definitions change depending on who prepared it.
A defined set of standard KPIs with agreed definitions is reported on a regular cycle across business areas. The Data Consumer receives KPI reporting using agreed, documented definitions on a predictable schedule.
KPI reporting is measured for timeliness and accuracy, includes trend and comparison, and its use in business decision-making is monitored. The Data Consumer receives KPI reporting on time with trends and comparisons, and its use in decision forums is tracked.
Performance reporting is dynamic and self-directed — the Data Consumer explores KPIs to the level of detail they need, with definitions and hierarchies governed centrally and the KPI set reviewed continuously against strategy. The Data Consumer drills into a KPI to the underlying detail themselves, and the KPI set is reviewed and adjusted as business strategy changes.
DST-CON-06
Discovery of existing reporting
Before commissioning or building something new, how easily can you find out whether a report or analysis that answers your question already exists?
Maturity level descriptions
No means exists to find existing reporting; the Data Consumer builds or commissions new work without knowing what already exists. Analysis is duplicated repeatedly across the organisation because nobody can see what has already been produced.
Discovery depends on asking colleagues; some duplication is caught by chance, most is not. The Data Consumer sometimes discovers, after the fact, that an equivalent report already existed elsewhere.
Reports and analytical assets are catalogued and searchable by business users, with descriptions that make their purpose clear. The Data Consumer can search for existing reporting by business topic and understand from the description whether it answers their question.
Catalogue coverage and search effectiveness are measured, duplication is monitored, and new requests are checked against existing assets before work begins. The Data Consumer’s new report request triggers a check for existing equivalents, and duplication rates are monitored.
Relevant existing assets are proactively surfaced to the Data Consumer at the point of need, and redundant assets are retired continuously. The Data Consumer is shown existing certified assets relevant to their question before they ask for anything new, and the estate is actively pruned.
DST-CON-07
Accuracy of data representation
How confident can you be that a dashboard or report faithfully represents the agreed business rules and definitions of the underlying data, rather than the report builder’s own interpretation?
Maturity level descriptions
Reports are built to whatever interpretation the builder applied, with no reference to agreed definitions or business rules. The Data Consumer has no basis for knowing whether a report’s filters, exclusions and calculations reflect agreed business rules.
Interpretation is discussed informally with the builder during development, with nothing documented or verified. The Data Consumer relies on the report builder having asked the right questions, with no documented confirmation.
Reports document the definitions, filters and business rules applied, and business users can inspect them. The Data Consumer can see, alongside a report, which definitions, filters and exclusions were applied to produce it.
Conformance of reports to agreed definitions is verified as part of a defined review, tracked, and non-conformance is remediated. The Data Consumer uses reports that have been formally reviewed for definitional conformance, with exceptions tracked to closure.
Reports derive business rules automatically from a governed semantic layer, making divergent interpretation structurally difficult and verification continuous. The Data Consumer’s reports inherit agreed rules automatically, with any deviation detected without manual review.
DST-CON-08
Transparency from report to source
When you are looking at a chart or figure, how easily can you establish what data it is built from, how it is defined, and how current it is — without leaving the report?
Maturity level descriptions
No supporting information is available from within a report; the Data Consumer sees only the visual output. A chart presents a figure with no source, definition or refresh information anywhere in view.
Some information can be obtained by contacting the report’s author, if the author can be identified. The Data Consumer must find and ask the person who built a report to understand what it draws on.
Reports display defined supporting information — source, key definitions, refresh time and a contact — as a documented standard. The Data Consumer sees source, definition and last-refresh information on the report itself, together with who to contact.
Compliance with the supporting-information standard is measured across the reporting estate, with gaps remediated on a defined cycle. The Data Consumer finds consistent supporting information on essentially all certified reports, with coverage measured.
Full lineage and definition detail are available interactively from any figure, generated automatically and always current. The Data Consumer can click a figure and trace it through its definition and transformations to its source system.
DST-CON-09
Approval before publication
Before a new report or dashboard is published for business use, is there a defined review confirming its accuracy and appropriate interpretation, and how consistently is it applied?
Maturity level descriptions
Anything can be published to business users with no review; the Data Consumer may receive material that has never been checked. Reports appear in shared workspaces with no accuracy or interpretation review having taken place.
Review depends on the individual builder’s diligence; some assets are checked informally and others are not. The Data Consumer receives some reports that a colleague reviewed and others that nobody checked, without knowing which is which.
A defined review and approval step exists before business publication, documented and expected to be followed. The Data Consumer’s new reports pass through a named review step confirming accuracy and definitional correctness before release.
Review compliance is tracked, review findings are recorded, and publication without approval is detectable and addressed. The Data Consumer can rely on published reporting having been approved, because unapproved publication is detected and corrected.
Approval is embedded in the publication process, with automated checks preceding human review and approval status visible to users. The Data Consumer sees approval status on published reporting, and technical controls prevent unapproved assets reaching business audiences.
DST-10
Feedback when reporting looks wrong
When a figure in a report does not look right to you, how effectively does that observation reach the people who can investigate it, and how well are you told the outcome?
Maturity level descriptions
No route exists for challenging a figure; the Data Consumer either ignores the report or quietly stops using it. A report loses credibility and falls out of use without anyone responsible ever learning why.
Challenges are raised informally to whoever built the report, with no record and no consistent response. The Data Consumer contacts the report author directly, and whether it is investigated depends on that individual.
A defined route exists for business users to query a figure, with a named responder and a documented process. The Data Consumer raises a query through a named channel and it reaches a defined responder for investigation.
Queries are tracked to resolution with stated response expectations, outcomes are communicated back, and query patterns are analysed. The Data Consumer receives an explanation of what was found within a stated timeframe, and recurring queries are analysed.
Discrepancies are largely detected by automated reconciliation before business users see them, and user challenges feed systematic improvement of reporting and source data. The Data Consumer rarely encounters a wrong figure because reconciliation catches it first, and the queries they do raise demonstrably improve the estate.