AurvikAI

Data Strategy

Data strategy designed by people who implement it.

Most data strategies fail because they're designed by consultants who won't execute them.

Data governance, catalogue design, quality frameworks, and organisational data strategy. For enterprises that have data problems they can't solve with more tools. We design strategy we can execute — frameworks that survive contact with real engineering teams and real data.

18yrdata systems experience
5regulated industries served
100%strategies we can execute

A typical data strategy engagement

From honest assessment to executable roadmap.

Phase 1

Current state assessment

Audit data infrastructure, quality, team capability, and data culture — without the optimism that typically inflates these assessments. A strategy built on accurate current state is executable. One built on aspirations isn't.

Infrastructure auditQuality assessmentCapability mapCulture survey

2–3 weeks

01
Phase 2

Priority & governance design

Prioritise data initiatives by business impact, define data owners for every critical dataset, and design the governance framework — catalogue, dictionary, access policies, retention schedules, and quality SLAs.

Prioritised initiative mapOwnership modelGovernance framework

2–3 weeks

02
Phase 3

Executable roadmap delivery

A prioritised roadmap with specific initiatives, owners, success metrics, and timelines. Not a framework diagram — an actionable plan your team can execute without us.

Roadmap documentInitiative specificationsSuccess metrics

1–2 weeks

03
Phase 4

Execution support

Optional hands-on support for the highest-priority initiatives. We stay available to guide implementation, troubleshoot challenges, and adjust the roadmap as conditions change.

Implementation guidanceQuarterly reviewsRoadmap adjustments

Ongoing

04

Strategy + culture

A data strategy without a data culture is a document.

We work with your leadership and engineering teams to build the habits, processes, and accountability structures that make strategy stick. Data ownership, quality accountability, and cross-team data sharing aren't technical problems — they're organisational ones.

73%

of data strategy failures trace to organisational issues, not technical ones

Industry research

01

Data ownership model

Defining who owns each critical dataset and what ownership means — quality accountability, access management, and documentation.

02

Quality accountability

SLAs for data quality with escalation paths, monitoring, and regular review cadences.

03

Cross-team data sharing

Data contracts between producing and consuming teams — schema, freshness, and completeness agreements.

04

Leadership alignment

Executive sponsorship and governance board design to keep data strategy on track through organisational change.

Data strategy components

The building blocks of a strategy that survives contact with reality.

Frameworks that are as lightweight as possible while meeting compliance requirements.

Data catalogueDiscovery

Searchable metadata catalogue with ownership, lineage, quality scores, and usage statistics.

Access policiesSecurity

Role-based access control, data classification, and audit trails for regulated environments.

Retention managementCompliance

Automated retention schedules aligned to regulatory requirements and business needs.

Data strategy workshop with leadership team mapping data ownership and governance priorities

Honest assessment

We assess current state without optimism.

Most organisations overestimate their data maturity. We audit infrastructure, quality, team capability, and data culture — and deliver an honest assessment that forms the foundation of an executable strategy. A data strategy built on aspirations produces initiatives that stall. One built on reality produces initiatives that ship.

Most data strategies fail because they're designed by consultants who won't implement them. We design strategy we can execute.

100%

strategies designed to be executable

5

regulated industries with governance experience

From strategy to execution

We don't just hand you a document. We help you execute it.

01
4–6 weeks

Quick wins

Highest-impact, lowest-effort initiatives that demonstrate value within the first 4–6 weeks. Building momentum and stakeholder confidence before tackling larger initiatives.

Quick win implementationsImpact measurementStakeholder updates
02
8–12 weeks

Foundation building

Data catalogue deployment, ownership model activation, quality monitoring setup, and governance process launch. The infrastructure that makes everything else possible.

Data catalogueOwnership assignmentsQuality dashboards
03
12–20 weeks

Capability expansion

Self-serve analytics enablement, advanced data quality automation, cross-team data sharing activation, and ML readiness assessment.

Self-serve platformAutomated quality checksML readiness report

Governance that works

Governance that's too heavy doesn't get followed.

We've built data governance programmes for regulated industries — healthcare, finance, legal — where data lineage and quality aren't best practices, they're regulatory requirements. We know what rigour looks like. We also know that governance designed for compliance and not for usability produces documentation nobody reads and processes nobody follows.

5

regulated industries served

100%

compliance audit pass rate

Data governance dashboard showing catalogue completeness, quality scores, and ownership coverage

Ready to build a data strategy that actually gets executed?

Let's start with an honest assessment of where you are — and a plan for where you need to be.