● SERVICES
Data Strategy
Most data programs fail because they lack a clear data strategy and business alignment. We help leadership teams define what data matters, why it matters, and how it should be used to support decision-making.
● WHAT’S INCLUDED
What we cover
01
End-to-end data strategy
Review the full data landscape, including systems, data flows, reporting, analytics usage, and organizational capabilities and identify structural gaps, redundancies, and constraints.
02
AI readiness
Assess whether data, infrastructure, governance, and operating models are prepared to support AI use cases and identify gaps in data quality, accessibility, security, and feedback loops required for reliable AI deployment.
03
Tools / Technology architecture
Evaluate existing data tools, platforms, and integrations across the stack and determine whether current architecture supports scalability, performance, cost efficiency, and future analytics or AI needs.
04
Differentiating through Data Analytics
Evaluate how data is currently used in decision-making and identify missed opportunities where data could influence planning, execution, or performance management more effectively.
05
Data governance
Review how data is owned, managed, and controlled across the organization and identify gaps in accountability, definitions, access controls, and change management.
06
KPI Definition
Review existing KPIs, definitions, and reporting structures and identify inconsistencies, misaligned incentives, accountability and gaps between metrics and business objectives.
● HOW WE DO IT
Data Strategy Evaluation Process
STEP 01
Assess current and future data maturity
Evaluate existing systems, reports, workflows, and decision processes to identify gaps and define a realistic future-state data model aligned with business objectives.
STEP 02
Translate business objectives into data priorities
Convert executive goals into concrete data use cases and map each use case to required datasets, metrics, and analytical capabilities.
STEP 03
Facilitate executive and stakeholder alignment
Conduct structured workshops and working sessions to align leadership, functional teams, and technical stakeholders around priorities and trade-offs.
STEP 04
Document architecture and operating models
Produce clear documentation outlining target architecture, operating model, governance, and sequencing to support execution and investment decisions.
STEP 05
Prioritize initiatives and build a roadmap
Build a backlog and prioritize initiatives based on impact and complexity and define scope, dependencies, timelines, and expected business impact for each phase.
STEP 06
Establish data ownership and governance
Assign clear ownership to data domains and define how data is created, validated, maintained, and consumed.
● WHY IT MATTERS
If in doubt, start with strategy
If you are not clear where to start or where to go next with your data journey, always start by evaluating and establishing a clear data strategy.
- Create clarity before building solutions
- Focus on the decisions that create value
- Align leadership before execution begins
- Eliminate fragmented and redundant investments
- Begin with business priorities, not technology
- Lead from the top
● CASE STUDY
Medical device manufacturer
Objective
A medical device manufacturer wanted to define a future-proof data architecture connecting three systems to eliminate manual workflows for BOMs and billing and establish a scalable foundation for reporting and AI-driven analytics.
Challenge
- Siloed core systems with no unified data layer
- Error-prone billing workflows
- Fragmented reporting across Power BI, spreadsheets, and native ERP reports
- Ongoing system migration with no defined integration strategy
Approach
- Interviewed Finance, Operations, Engineering, Quality, Sales, and IT
- Documented end-to-end data flows (parts/BOMs, time & billing, reporting)
- Reviewed existing workflows, reports, and exports
- Designed a centralized data warehouse as Single Source of Truth
- Defined integration patterns for the three operating systems
- Built use-case-driven data pipeline/backlog and implementation roadmap
Outcome
- Clear, executive-aligned vision on how to fix their data issues
- Scalable architecture ready for implementation
- Reduced dependency on manual workflows
- Reporting foundation built for consistency and growth
- AI-ready data platform roadmap
- Prevented fragmented integrations for the system migration
- Created a single direction before execution began
- Enabled faster, lower-risk delivery in future phases
