Data Strategy Workshop
Valuable data is everywhere. Usable business knowledge is not.
Most enterprises already have more data than they can effectively use. Critical information is fragmented across systems, reports, spreadsheets, documents, and operational workflows. Leaders struggle to trust the numbers, teams spend too much time reconciling information, and AI initiatives stall because the underlying data is not ready.
ERP, CRM, billing, finance, legacy apps, spreadsheets, PDFs, and folders do not operate as one coherent data foundation.
Reports do not match, business definitions vary, and teams spend time reconciling numbers instead of acting on insight.
AI ambition is high, but data quality, access, governance, history, and semantic meaning are not yet ready to support scale.
Platform decisions are difficult without first understanding business value, data gaps, governance needs, and consumption patterns.
Principles to Evaluate Data Opportunity and Readiness
Data strategy starts with priority outcomes, decisions, workflows, and value levers – not a generic inventory of available data.
Unique customer, operational, financial, product, service, and document knowledge can become a strategic differentiator when properly unified and activated.
Recommendations are grounded in existing systems, access constraints, security needs, governance maturity, data quality, and implementation feasibility.
Analytics and AI require shared definitions, certified metrics, business entities, relationships, rules, and governed context – not just raw data access.
The workshop creates a direct path to a 90-day data pilot and a scalable data foundation or data services build.
Data Strategy Workshop Scope of Work
The Data Strategy Workshop enables organizations to identify where data can create the most business value, evaluates whether their current data estate is adequate, and defines a modern data architecture that turns fragmented information into trusted insight, reusable data services, and AI-enabled business capabilities.
Identify business outcomes, value levers, critical workflows, and decisions where better data can create measurable impact.
- Clarify growth, retention, margin, productivity, risk, and visibility priorities
- Identify where the business is flying blind today
- Define success criteria for evaluating data and AI opportunities
Assess the data required to support priority outcomes across structured, semi-structured, and unstructured sources.
- Review systems of record, operational applications, databases, spreadsheets, and reports
- Evaluate digital folders, PDFs, contracts, scanned documents, emails, and other unstructured sources
- Identify access constraints, owners, known issues, and gaps
Evaluate whether priority data is adequate to support trusted analytics, automation, and AI-enabled capabilities.
- Assess availability, completeness, accuracy, history, timeliness, and granularity
- Review security, access control, lineage, ownership, and systems-of-record clarity
- Determine readiness for natural-language analytics, RAG, agents, and decision support
Define the right-sized architecture and platform options that fit the business, maturity, cloud posture, data complexity, and AI ambition.
- Evaluate lakehouse, warehouse, data mart, and cloud-native patterns
- Consider Databricks on AWS or Azure, Microsoft Fabric, Snowflake, AWS-native, Azure-native, or lighter-weight approaches
- Define centralization, unification, governance, integration, consumption, and operations layers
Define how raw data becomes shared business meaning and reusable services that support business capabilities.
- Identify business entities, certified metrics, definitions, relationships, and rules
- Define candidate data services such as Customer 360, Revenue Intelligence, Attrition Intelligence, and AI Context
- Prioritize reusable capabilities that support business teams, applications, analytics, and AI
Translate workshop insights into a clear execution path and define the recommended first pilot.
- Prioritize use cases by value, feasibility, data readiness, and business relevance
- Define the recommended 90-day pilot scope, dependencies, and success measures
- Identify team, skills, ownership, governance, and operating model needs
Let's Workshop Your Data Strategy
Book a complimentary discovery call to align business priorities, data readiness, platform direction, and the right 90-day pilot path.
Data Strategy to Business Capability
The workshop identifies data and AI opportunities that are specific enough to act on, valuable enough to matter, and reusable enough to build into a durable foundation.
Executive KPI Foundation
Create trusted metrics, source-of-truth clarity, and AI-generated performance summaries.
Revenue Intelligence
Unify leads, accounts, pipeline, proposals, and sales outcomes to improve ICP clarity and conversion.
Attrition Intelligence
Connect customer, billing, service, complaints, and cancellation history to identify churn patterns and account risk.
Document-to-Data Intelligence
Extract business value from PDFs, contracts, forms, scanned documents, emails, and digital folders.
Operational Performance Intelligence
Use work orders, tickets, schedules, labor, assets, and service history to expose bottlenecks and exceptions.
ERP / CRM Readiness
Prepare master data, system-of-record rules, field mapping, and migration readiness before modernization.
Customer / Account 360
Unify account, contract, invoice, service, interaction, and sales history for relationship intelligence.
Natural-Language Analytics
Enable governed Q&A over trusted metrics, curated data marts, and semantic definitions.
AI Agent Readiness
Prepare APIs, governed data services, permissions, workflow events, and audit trails for grounded AI agents.
"We came into the Data Strategy Workshop with disconnected systems, unclear priorities, and more questions than answers. DevIQ helped us turn that complexity into a practical roadmap – one tied to business value, governance, and the data foundations we need to scale."
Data Strategy Workshop Questions
The DevIQ Data Strategy Workshop is the starting point for aligning business value, data readiness, modern architecture, governance, and AI enablement into a practical roadmap. More questions? Ask Us ->
A structured strategy engagement that helps organizations identify where data creates the most business value, assess the readiness of their current data estate, and define a roadmap for unified, governed, AI-ready data capabilities.
It is designed for executive, technology, operations, finance, product, and data leaders responsible for improving business visibility, modernizing data foundations, preparing for AI, or creating reusable data services.
No. The workshop helps determine what architecture is appropriate based on current systems, data complexity, business priorities, cloud posture, governance needs, and implementation readiness.
Yes. AI enablement is built into the workshop. We identify where AI can create practical value and what data, governance, semantic layer, and architecture must be in place to support it responsibly.
You leave with a business value map, data estate assessment, readiness scorecard, target architecture concept, semantic layer recommendations, data services roadmap, operating model recommendations, and a 90-day pilot recommendation.
The workshop is designed to lead into a focused 90-Day Data Pilot that proves business value while validating data access, quality, governance, architecture, and usability.
Let's Workshop Your Data Strategy
Schedule a complimentary session to build the foundation for better decisions, smarter workflows, and measurable AI value.