From strategy to roadmap: 18-month execution planning

A strategy without a roadmap is a vision statement on a conference room wall. A roadmap without a strategy is a list of projects that don't add up to anything.

The 18-month roadmap bridges these two failure modes. It translates your data vision into concrete initiatives with owners, timelines, budgets, and success metrics. And it gives you the tool you need to say "no", the CDO's most important skill.

Strategy vs. roadmap: the distinction that matters

Your strategy answers *why and what*: Why does data matter to this organization? What capabilities will you build? What will you be able to do that you can't do today?

Your roadmap answers *how and when*: In which order will you build these capabilities? What resources do you need? What does success look like at 6, 12, and 18 months?

The most common mistake: confusing a technology roadmap with a data strategy. "We will migrate to Snowflake and implement dbt by Q3" is not a data strategy. It's infrastructure planning. The strategy should drive technology choices, never the other way around.

The 70/20/10 portfolio approach

Borrow from Google's innovation portfolio framework:

70%, Core programs: Initiatives directly serving your current business model and near-term priorities. Improving Data qualityData qualityThe degree to which data is fit for purpose: accurate, complete, consistent, timely, valid and unique. Poor quality data undermines analytics, reporting and AI.View full definition → for customer analytics. Building the unified customer Data productData productA data asset managed like a product, with an owner, defined users, guaranteed quality, and measurable business value.View full definition →. Rationalizing your Business IntelligenceBusiness IntelligenceTechnologies and processes that turn raw data into actionable insights via reporting, dashboards and analysis, so teams can decide based on facts rather than intuition.View full definition → stack. These are your bread and butter, they keep the lights on and build credibility.

20%, Adjacent bets: Initiatives extending your capabilities into new areas. Building a Data product for a new internal use case. Piloting machine learning in a high-value operational process. Exploring a data partnership.

10%, Strategic bets: Long-horizon, high-uncertainty initiatives. An external data monetization business. A real-time personalization engine at scale. An AI-driven operations center.

This allocation prevents two failure modes: spending all your budget on core infrastructure (safe but not transformational) or spending too much on moonshots before the foundation exists.

Building quarterly okrs for data

OKRs (Objectives and Key Results) are the best mechanism for translating your 18-month roadmap into actionable plans.

Structure your OKRs at three levels:

• Organizational OKR: The big-picture outcome your data function drives
• Program OKRs: The 3-5 initiatives running in parallel
• Team OKRs: What each data team delivers this quarter

Example:

• Organizational OKR: Achieve Level 3 data maturity in customer and product domains by Q4
• Program OKR 1: Achieve 95% completeness on customer master data by Q2
• Program OKR 2: Deploy self-service Business Intelligence to 300 business users by Q2
• Program OKR 3: Complete Data governance frameworkData governance frameworkData governance is the set of policies, roles, and processes that ensure data is accurate, secure, well-defined, and used responsibly across an organization.View full definition → rollout to 3 core business units by Q3

Key discipline: fewer OKRs, not more. A data function tracking 40 KPIs is tracking nothing. Three to five critical outcomes get the attention they deserve.

Dependencies: people first, then tech, then governance

The biggest roadmap mistake: sequencing technology before talent.

You can buy Snowflake, dbt, and Looker in 90 days. You cannot hire 20 senior data engineers, train 50 business analysts in data literacy, and build a governance culture in 90 days.

Sequence your roadmap accordingly:

1. People: Hire or upskill the team before you start building
2. Technology: Implement tools once you have the people to run them
3. Governance: Build governance around real use cases, not in the abstract

Organizations that fail at data transformation almost always got this sequence wrong. They bought the technology first, hired the people second, and never reached governance at all.