Data Strategy

Turn Data Into an Enterprise Capability—Not Just a Technology Program

Concinnity helps organizations define a practical data strategy that connects business priorities, trusted data, governance, architecture, analytics, planning, AI, and measurable enterprise value.

Start a Data Strategy Conversation Explore the Framework

Business Strategy Growth • Margin • Efficiency • Risk • Customer • Innovation

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Data

Governance

Architecture

Analytics

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Enterprise Data Strategy Priorities • Operating Model • Roadmap • Value

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Business Outcomes Better Decisions • Faster Planning • Trusted Insights • Scalable AI

Executive Insight

A strong data strategy does not begin with platforms. It begins with the decisions, outcomes, capabilities, and operating model the business needs—then aligns data, governance, architecture, analytics, and AI around those priorities.

Why data strategies fail

The problem is rarely a lack of technology

Organizations often invest in data platforms, analytics tools, cloud services, and AI before they have aligned priorities, ownership, governance, and value measures.

Technology-Led Roadmaps

Platform decisions are made before business priorities and target outcomes are clear.

Fragmented Ownership

Business, IT, analytics, and data teams operate with different priorities and unclear accountability.

Low Data Trust

Inconsistent definitions, lineage, quality, and governance reduce confidence in reporting and AI.

Too Many Initiatives

Organizations accumulate disconnected projects without a common architecture or value sequence.

Weak Adoption

Data products and analytics are delivered without enough attention to decisions, workflows, and users.

Unclear Business Value

Success is measured by technical delivery rather than business outcomes, adoption, and performance improvement.

Concinnity Data Strategy Framework

Connect business direction to data capability and execution

1. Business Priorities & Decisions Strategic outcomes • Critical decisions • KPIs • Value pools • Pain points

2. Data & Intelligence Use Cases Analytics • Planning • AI • Automation • Data products • Operational intelligence

3. Data Domains & Governance Ownership • Definitions • Quality • Metadata • Lineage • Security • Policies

4. Architecture & Platforms Source systems • Integration • Cloud • SAP • Microsoft • Semantic layer • Consumption

5. Operating Model & Delivery Roles • Product teams • Ways of working • Prioritization • Funding • Adoption

6. Roadmap & Value Realization Sequencing • Milestones • Investment • KPIs • Change management • Continuous improvement

What we help define

A complete strategy across business, governance, architecture, and execution

Business-Aligned Data Vision

Define how data, analytics, planning, and AI will support enterprise strategy and operating priorities.

Decision & Use-Case Portfolio

Identify and prioritize high-value decisions, data products, analytics, planning, automation, and AI use cases.

Data Domain Strategy

Define critical enterprise data domains, ownership, shared definitions, stewardship, and reuse.

Governance Operating Model

Establish practical governance across quality, metadata, lineage, security, access, and accountability.

Target Data Architecture

Align SAP, Microsoft, cloud, integration, semantic, analytics, and AI architectures to a common direction.

Data Product Model

Design reusable, business-owned data products that serve analytics, planning, applications, and AI.

Delivery & Organization Model

Clarify roles, team structure, product ownership, funding, prioritization, and enterprise delivery practices.

Transformation Roadmap

Create a sequenced roadmap that balances foundational work, near-term wins, investment, risk, and value.

Strategy journey

From current-state reality to an executable enterprise roadmap

01DiscoverBusiness priorities, pain points, current landscape, stakeholders, and constraints

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02AssessData maturity, governance, architecture, analytics, planning, AI, and delivery capability

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03PrioritizeCritical decisions, use cases, data domains, capabilities, risks, and value

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04DesignTarget operating model, governance, architecture, data products, and delivery model

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05MobilizeRoadmap, investment, ownership, milestones, adoption, KPIs, and execution governance

Data maturity assessment

Understand where you are before deciding where to invest

Concinnity assesses maturity across the capabilities required to turn data into a sustainable enterprise asset.

Business Alignment

Governance

Data Quality

Architecture

Analytics & Planning

AI Readiness

Operating Model

Illustrative maturity profile. Actual assessments are based on interviews, evidence, architecture, process, and business priorities.

Technology alignment

Make platform choices part of the strategy—not the strategy itself

We help align technology decisions to business and data requirements across heterogeneous enterprise landscapes.

SAP

SAP Business Data CloudSAP DatasphereSAP Analytics CloudS/4HANABW Modernization

Microsoft

Microsoft FabricPower BIAzureData & AI ServicesCopilot

Enterprise Data

Cloud Data PlatformsIntegrationMaster DataMetadataSemantic Models

Intelligence

AnalyticsEnterprise PlanningMachine LearningGenerative AIDecision Intelligence

Illustrative engagement

Creating an enterprise data strategy that executives and delivery teams can use

Global Enterprise

Moving from fragmented data initiatives to one business-aligned roadmap

Challenge

Multiple business units were funding overlapping data, reporting, cloud, and AI initiatives with inconsistent definitions, governance, and architecture.

Approach

Aligned executive priorities, identified critical decisions and data domains, assessed maturity, defined governance and target architecture, and created a sequenced investment roadmap.

Business Impact

Clearer ownership, reduced duplication, stronger data trust, improved investment prioritization, and a practical foundation for enterprise analytics, planning, and AI.

Translate strategy into scalable enterprise capabilities