DATAPULSEBY AXOQUANT
Method

How the score is produced.

Three decisions separate DataPulse from a questionnaire. Who is asked, how much each answer counts, and what the answer is measured against. All three are explicit, editable, and visible in the report.

Nothing here is inferred after the fact. The weights are set before the assessment opens, the participants answer against published maturity-level descriptions, and the arithmetic is reproducible from the raw inputs.

FIG. 2 — QUESTIONS APPLICABLE PER ROLE
Data Steward 24 Chief Data Officer 25 Data Engineer 50 Data Consumer 60 0 60 questions
The four role banks are disjoint: 24 + 25 + 50 + 60 = 159 questions, each carrying one dimension, one focus statement, and per-question maturity descriptions for levels 1 to 5. A participant sees only their own role's questions. The counts differ because the roles differ in surface area: a Data Consumer touches data decisions across the whole organisation, a Data Steward's remit is narrower and deeper. Values measured from the seeded banks.

Who is asked

Roles are first-class in the data model, not labels on an answer. Every question carries an applicability flag per role: a question about pipeline architecture is not put to a business analyst, and a question about decision rights is not put to a platform engineer. Participants see a survey filtered to their role — and, where a question is genuinely shared, several roles answer it, each on their own score.

Custom roles are supported, which matters for governance structures that do not look like a corporate hierarchy — public sector, joint ventures, federated groups.

How much each answer counts

Two weights multiply every answer.

Impact factor — set per role and per question on a 0–2 scale (default 1). This is the instrument's statement of how much that role's view should count on that subject. It is edited in the weight matrix before the assessment opens, and every cell of that matrix is part of the report's record.

Experience factor — derived from the participant's SFIA level, on the ladder below. Seniority is not treated as authority over the answer, but as a stated multiplier on it.

SFIALevelExperience factor
1Follow0.70
2Assist0.85
3Apply1.00
4Enable1.15
5Ensure / Advise1.30
6Initiate / Influence1.45
7Set Strategy / Inspire1.60

The weighted score

score(dimension) = Σ (answer × impact × experience)
                  ÷ Σ (impact × experience)

Summed over every answered, applicable question and participant in that dimension. The denominator is why coverage is reported alongside every score: a dimension answered by three of its four roles is a weaker reading than one answered by all four, and the report says so rather than hiding it in a single number.

What the answer is measured against

Each question is scored twice on a five-level maturity scale: as-is, where the organisation is today, and to-be, where it intends to be. Every level carries a written description — what level 1 looks like, what level 5 requires — so two participants scoring the same question are at least arguing about the same thing.

The difference is the gap. Gaps are what the engagement is scoped against: a dimension at 3.05 today and 4.51 as a target is a different piece of work from one already at 4.2, even when both have the same score today.

The eight dimensions

STRData Strategy
How the organisation defines its data strategy and translates strategic enterprise data goals into measurable domain value, priorities, and investment decisions.
GOVData Governance
How data ownership, policies, access controls, and compliance are defined, enforced, and monitored across the organisation's data assets.
ARCArchitecture
How the organisation's data models, schemas, integration standards, and storage placements conform to defined architecture standards and support interoperability.
DQTData Quality
How data quality rules, issue detection, remediation, and measurement ensure the organisation's data is accurate, complete, consistent, and timely.
MDTMetadata
How business and technical metadata, definitions, classifications, and lineage are documented, standardised, and kept discoverable and accurate.
DSTData Analytics
How dashboards, reports, and analytical outputs accurately and consistently represent the organisation's data through agreed definitions and calculations.
INTIntelligence
How the organisation's data supports predictive analytics, AI, and machine learning, including transparency, bias mitigation, and model governance.
CULData Culture
How data literacy, evidence-backed decision-making, collaboration, and continuous learning are embedded in the organisation's culture.

Repeating it

The instrument is designed to be run on the same scope at a cadence — half-yearly, annually — with the same weights unless the organisation deliberately changes them. Because the question bank, the impact matrix and the scoring arithmetic are versioned with the assessment, a second run is comparable to the first. That is the point: the first assessment establishes a position, the second measures movement.

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