7 Questions Every CDO Should Ask Before Building a Data Strategy

 

Why Building a Data Strategy Matters?

 

If you’re a Chief Data Officer (CDO) or preparing to become one, you’re probably under pressure from every angle to turn sprawling data assets into measurable value, to align with business strategy, to manage risk and compliance, and to do so all while building trust across your organization.  

 
The hard truth is that too many companies have a “data strategy” only in name loose visions, fragmented efforts, and low stakeholder buy-in. According to one survey, 83 % of companies reported having a CDO or CDAO role, yet 62 % of those respondents said the CDO’s remit was still poorly defined. (Harvard Business Review)
 
In this post, we’ll dive into the seven critical questions every CDO should ask before building or refining a data strategy. These aren’t checkbox queries they’re the foundation for a strategy that works. We’ll walk through what each question means (what), why it matters (why) and how to approach it (how). And yes, we’ll also show how your solution, eZintegrations™, can plug into the narrative and help you succeed.
 
If you’re operating in the U.S. market, leading a data office, or preparing your organization to do so, this blog post is for you.
 

 

TL;DR – Key Takeaways

 

• A data strategy must align clearly with business goals, or it fails.
• Stakeholders’ buy-in, governance, data quality, and culture are as vital as technology.
• Use the seven questions below to diagnose your readiness and strategy priorities.
• Solutions like eZintegrations™ help operationalize integration, governance and analytics execution.
• Build measurable KPIs early, avoid isolated pilots, and create a data-strategy roadmap that’s pragmatic and scalable.

1. What Are Our Business Objectives and How Will the Data Strategy Support Them?

 

What?

 
This question asks you to identify your organization’s top business goals (revenue growth, cost reduction, product innovation, customer retention, etc.) and define how a data strategy will enable them.
 

Why?

 
If data initiatives aren’t explicitly tied to business objectives, they become isolated, costly, and unlikely to produce meaningful ROI. As one article put it: “A data strategy isn’t going to generate a single incremental dollar for your business, it’s an enabler.”
 

How?

 

• Work with the executive team (CEO, CFO, business unit heads) to map top 3–5 business priorities.
• Translate each priority into data-enabled use cases (e.g., “reduce churn by 15% within 12 months” becomes “leverage customer behavioral data + predictive model to intervene”).
• Document how many of those use cases will be addressed in Year 1 vs Year 2.
• Use eZintegrations™ to create the integration layer and analytics-ready datasets aligned with those use cases.

2. Why Do We Need a Data Strategy Now and What’s Driving Urgency?

 

What?

 
Define the triggers that make the data strategy urgent: competitive pressure, regulatory change, cost pressures, data sprawl, merger/acquisition, AI/ML initiatives.
 

Why?

 
Without urgency, a data strategy risks getting pushed aside. According to research, many CDOs struggle because the role lacks clarity and urgency.
 

How?

 

• Identify top 2–3 “pain points” today: e.g., “We have over 10 000 applications producing duplicate data sets,” “Our regulatory risk is rising under CCPA/CPRA,” “We have 35 % of projects delayed due to poor data quality”.
• Quantify the cost where possible (lost revenue, manual work hours, risk/fines).
• Use that to secure stakeholder alignment and budget.
• Leverage eZintegrations™ as your platform for overcoming complexity (data integration, governance, monitoring) and reducing time to value.

3. How Mature Are Our Data, Analytics and Governance Capabilities?

 

What?

 
This is a reality check: where does your organization stand with data infrastructure, data quality, governance frameworks, analytics, maturity, and data culture?
 

Why?

 
If the foundational capabilities are weak, launching ambitious use cases will likely fail or stall. A survey found only 24 % of firms considered themselves truly data-driven, and only 21 % say they’ve developed a data culture. MIT Sloan Management Review
 

How?

 

• Conduct or refer to a maturity assessment across dimensions: data infrastructure, data governance, analytics/ML, data culture, roles & skills.
• Identify top gaps: e.g., no data catalogue, no data steward role, data quality issues, disconnected data silos.
• Prioritize these gaps in your strategy roadmap.
• With eZintegrations™, you can accelerate integration and governance pipelines, reducing the maturity gap.

4. Who Are the Stakeholders and How Will We Secure Their Engagement?

 

What?

 
Map the stakeholders (C-suite, business units, IT, data science, compliance, marketing, operations) and define how you will engage them and secure buy-in.
 

Why?

 
Data strategy success depends on cross-functional collaboration. Governance frameworks and infrastructure alone don’t guarantee adoption. The article from DataIQ noted: “There needs to be step-by-step explanation … and success needs to be demonstrated.” DataIQ
 

How?

 

• Create a stakeholder matrix (role, interest, influence, required buy-in).
• Develop communication and engagement plan: workshops, dashboards, proof-points.
• Define the business case for each stakeholder: e.g., “sales can get a single view customer”, “compliance gets audit trail”.
• Use eZintegrations™ as the platform that provides business stakeholders with self-service access, dashboards and data integration flows, ensuring they see value quickly.

5. What Data Governance, Compliance and Ethics Frameworks Must We Put in Place?

 

What?

 
Define the policies, standards, roles and tools needed for data governance, regulatory compliance (e.g., CCPA, GDPR if applicable, HIPAA), and ethical data use (especially if you’re using AI/ML).
 

Why?

 
Poor governance undermines trust, exposes risk, and blocks scaling of analytics. Many organizations focus on technology but neglect governance and ethics.
 

How?

 

• Define data ownership and stewardship roles (who owns which data domains).
• Establish data quality metrics, escalation processes, metadata management, and data catalog.
• Build compliance checklist: data retention, consent, privacy, auditability.
• Establish ethical guardrails for analytics/ML: e.g., bias mitigation, transparency.
• Leverage eZintegrations™ to record lineage, monitor data flows, support audit logs and compliance dashboards enabling governance embedded in operations, not just in slides.

6. Which Use Cases Will We Prioritize and How Will We Measure Success?

 

What?

 
Select the initial high-impact use cases for your data strategy, define success metrics (KPIs) and define how you will measure and monitor them.
 

Why?

 
Use case prioritization prevents “data paralysis” and ensures your strategy produces visible results. As noted in HBR research: the CDO role is often ill-defined, and success must be demonstrable.
 

How?

 

• Pick 3–5 pilot use cases with these attributes: alignment to business objectives, data available, stakeholder commitment, measurable outcome.
• For each use case, define baseline metric (e.g., churn rate today 20 %), target outcome (e.g., reduce to 16% in 12 months), owner, timeline.
• Define the governance/monitoring process for tracking the use of cases.
• Use eZintegrations™ to spin up the data pipelines quickly, monitor delivery, and link the analytics output into dashboards that measure the use case results helping you show value fast.

7. How Will We Scale the Strategy, Sustain It Over Time, and Adapt Change?

 

What?

 
Consider the roadmap beyond pilot use cases: how will you scale across domains, how will you sustain processes, how will you adapt as business and data environments change?
 

Why?

 
Many strategies stall during scaling because initial programmes succeed but fail to become part of business-as-usual. Research shows that human/culture issues remain the major barrier.
 

How?

 

• Define a multi-phase roadmap: pilot, scale, embed.
• Build the operating model: roles, data organization, funding model, centre of excellence (CoE).
• Establish a continuous improvement process: data maturity review, feedback loops, adapting new technologies (AI, ML, generative AI). For example, 83% of organizations moving forward with Gen AI say it is a strategic priority. AWS Static
• Use eZintegrations™ as your foundation for ongoing scaling: modular, extensible, built for integration and analytics so that new domains, sources and use cases can be added without starting from scratch.

 

How eZintegrations™ Powers Your Data Strategy?

 
At this point you might ask: “Okay, that’s the strategic side but how do I make it operational?” That’s where eZintegrations™ comes in.
Here’s how we help:

• Rapid integration of disparate data sources (internal systems, cloud, third-party).
• Governance and lineage tracking built in, so compliance and auditability are not afterthoughts.
• Analytics-ready data pipelines and self-service dashboards for business stakeholders.
• Scalable architecture so once pilot use cases succeed you can expand without rewriting the stack.
• Support and advisory services to help you build the data-strategy roadmap, prioritize use cases and secure stakeholder engagement.

If you build your strategy using the seven questions above, and eZintegrations™ gives you the platform to execute.

 

Ready to Turn Strategy into Measurable Impact?

Building a successful data strategy is a critical lever for modern organizations. As a CDO, you need to ask the right questions up front to ensure your strategy aligns with business goals, is grounded, has stakeholder buy-in, is governed, measurable and scalable.

Use the seven questions above as your workshop agenda. Map your answers, build your roadmap, prioritize your use cases, and set yourself up to deliver measurable value. And when you are ready to operationalize the strategy, eZintegrations™ is here to help you integrate, govern and scale your data ecosystem.

Ready to move from strategy to action? Book a free demo of eZintegrations™ today and let’s explore how you can turn your data strategy into a business impact.

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FAQ Section

 
Q1: What is a data strategy?
A data strategy is a roadmap that defines how an organization uses data to achieve its business goals.

Q2: How long should a data-strategy initiative take to show results?
Most organizations see measurable results within 6–12 months of implementation.

Q3: What are common pitfalls in building a data strategy?
Lack of alignment, weak governance, poor stakeholder buy-in, and unrealistic scaling plans.

Q4: How do we measure the success of a data strategy?
By tracking defined KPIs like revenue uplift, cost savings, and faster time insights.

Q5: When should we involve external partners or platforms like eZintegrations™?
When internal capacity is limited, you need faster integration and governance execution.