Growth, AI, and Data Strategy

The Four-Step Framework That Changes Everything

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Our Align-Design-Build-Adopt methodology emerged from a pattern we observed across failed data projects: teams would build technically impressive solutions that business users couldn't—or wouldn't—use. The disconnect wasn't about capability; it was about alignment.

• Align means starting with what matters most: understanding what's important, why you're building it, and how it will impact growth. When we partnered with Calendly to connect their marketing, Prodcuct-Led-Growth, and Sales-Led-Growth teams, we didn't start by discussing data models. We started by understanding their core challenge: improving audience targeting to drive expansion sales. This alignment phase typically reveals that what organizations think they need differs dramatically from what will actually drive value.
• Design transforms business objectives into technical blueprints. We solve problems on paper first, creating detailed architectures that address complex challenges before writing a single line of code. For Linux Foundation, this meant designing a solution to unify data across 900 operating P&Ls—a complexity that required careful planning to ensure scalability.
• Build is where our AI-augmented approach delivers dramatic acceleration. We turn blueprints into performant, maintainable solutions that drive rapid business adoption. But building isn't just about speed; it's about creating data products that business teams actually want to use.
• Adopt ensures your teams fully leverage rich customer data to run experiments and drive growth. This isn't an afterthought—it's designed into every phase of our process.

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Real Impact Across Industries: From Streaming to SaaS

The power of this methodology becomes clear through results. When MSG Networks faced involuntary churn for their streaming apps, alignment revealed the real problem wasn't data availability—it was timing. By aligning on the objective to unify subscription touchpoints and automate re-engagement, we designed a solution using Snowflake and dbt that delivered real-time payment failure data directly to marketing teams in Braze. The result: improved payment recovery and reduced churn through timely, personalized interventions.

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Similarly, Nutrafol came to us with data spread across multiple platforms, hindering their ability to boost customer insights and empower marketing. Through our alignment process, we discovered their core need was unified, consistent customer data for real-time audience targeting. The design phase proposed a robust analytics stack featuring Snowflake, dbt, Tableau, Segment, Mixpanel, and Iterable working within a well-orchestrated, efficient architecture. The outcome exceeded expectations: a 60% revenue surge that contributed to their successful acquisition by Unilever.

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For Siemens' Next Gen Marketing Automation division, alignment revealed a complex stakeholder landscape with competing needs. Rather than building separate solutions, we designed a multi-phase plan to integrate Salesforce and LinkedIn data, creating a unified framework that optimized campaign targeting and reduced missed opportunities.

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Why Business Questions Beat Technical Specifications

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Traditional data projects fail because they optimize for the wrong metrics. Teams measure success by data volume processed, query performance, or system uptime. But none of these matter if the business can't answer critical questions like "Which marketing channels drive profitable growth?" or "How do we reduce customer acquisition costs?"

Starting with business questions forces uncomfortable but necessary conversations early. When we worked with Tubi to address inconsistencies across three attribution models, the alignment phase revealed that the problem wasn't technical—it was organizational. Different teams used different definitions of success. By establishing shared business objectives first, we created a foundation for technical decisions that all stakeholders could support.

This approach also prevents the most expensive form of failure: building the right thing wrong. CarGurus needed unified customer data to support their shift to end-to-end services. By aligning on business objectives first, we identified that their existing revenue and conversion reporting had multiple points of failure. The solution wasn't just new technology—it was rebuilding their entire reporting infrastructure with unified data sources and modular dbt models that centralized business logic.

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Measurable Outcomes That Justify the Approach

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The proof lies in consistent, measurable outcomes across diverse industries. Tempo achieved a 20% increase in lead generation conversion rates by unifying customer data across six products. Shipt eliminated frequent failures in daily data jobs while launching dynamic abandoned-cart campaigns within two months. The Linux Foundation gained transformative insights across 900 P&Ls, becoming data-enabled for the first time in their history.

These aren't isolated successes. They represent a pattern: when you start with business questions, you build solutions that deliver immediate value while creating foundations for long-term growth. Our methodology ensures that every technical decision traces back to a business objective, every data model serves a specific purpose, and every dashboard answers a question someone actually needs answered.

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Transform Your Data Journey with Proven Methodology

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The difference between data projects that transform businesses and those that become expensive failures isn't technical complexity—it's methodology. Our Align-Design-Build-Adopt framework, refined through 900 projects over 10 years, ensures your data initiatives deliver measurable business value from day one. If your organization is ready to move beyond fragmented data and unclear ROI to unified insights that drive growth, let's start with the right questions.

Ready to align your data strategy with business outcomes? Ready to align your data strategy with business outcomes? Let’s talk!

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