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Table of Contents

    What exactly do you need from me or the executive team to get our customer data ready for predictive analytics next quarter?

    Here’s exactly what I need from you and the rest of the leadership team to move this forward effectively in the next quarter:


    1. Clear Business Priorities and Use Cases
    Predictive analytics is only valuable if it’s aimed at problems that matter. I need your input to pinpoint 2–3 priority questions or outcomes you want to drive—examples might be:

    • Which customers are most likely to churn?

    • Which prospects have the highest propensity to buy?

    • What products should we promote to increase basket size?

    The clearer we are on what we want to predict, the more targeted and efficient our data preparation will be.


    2. Agreement on Success Metrics
    Before we start, I’d like the leadership team to agree on how we’ll measure success—for example, revenue uplift, churn reduction, or marketing efficiency improvements. This helps avoid scope drift and ensures we can demonstrate ROI.


    3. Executive Backing to Access and Align Data
    One of our biggest hurdles is that critical data—CRM, billing, website activity, support tickets—is owned by different departments and systems.
    I’ll need:

    • Formal endorsement from the executive team that this is a priority initiative.

    • A mandate that data owners cooperate in providing timely access.

    • Support to resolve conflicts when priorities or resources compete.

    When teams see that this effort has your backing, they’re much more likely to prioritize data sharing and cleanup.


    4. Resource Commitment and Participation
    Data readiness isn’t something IT can do alone. We’ll need:

    • Data stewards from each business function to validate and explain data (e.g., what fields mean, what’s trustworthy, what’s obsolete).

    • Subject matter experts to help us understand business context and edge cases that models need to handle.

    • Participation in workshops—ideally 2–3 sessions over the quarter—to align on definitions, governance, and data flows.


    5. Decisions on Governance and Privacy
    Predictive analytics means combining data in new ways, which can raise compliance and privacy considerations.
    I’ll need executive decisions on:

    • What data we are and are not comfortable using (especially PII and sensitive data).

    • Retention and access policies—who gets to see the outputs.

    • Alignment with legal and compliance to avoid surprises later.


    6. Budget for Tools and Potential Data Enrichment
    Depending on the gaps we uncover in our data—like missing firmographics, demographics, or engagement signals—we may need modest investment in third-party data sources or new pipeline tools. I’ll provide a detailed proposal once we’ve completed the initial assessment.


    Summary of Your Role:

    If I had to distill this to a short list, here’s what I need most from you:
    ✅ Help define and prioritize business problems to solve.
    ✅ Endorse and communicate that this initiative is strategic.
    ✅ Empower teams to provide data access and validation.
    ✅ Make timely decisions on governance, privacy, and scope.
    ✅ Approve any budget needed to fill critical gaps.


    With your backing on these points, we’ll be in a strong position to have clean, integrated, and governed customer data ready for predictive models next quarter—and to do it in a way that’s sustainable, secure, and aligned to business value.

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    Authors

    • Mike Sargo

      Mike Sargo

      Chief Data Officer & Co-Founder

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