Data Science ↔ IT Collaboration Guide

Why This Collaboration Matters

Data Science and IT may come from different paradigms—one focused on analytics, experimentation, and modeling; the other on infrastructure, systems reliability, and security—but they are increasingly interdependent.

Modern data science requires stable, scalable, and secure data environments. IT is responsible for providing that foundation through cloud platforms, access control, data pipelines, and system integrity. Without alignment, even the most sophisticated models can fail due to data silos, outages, or compliance violations.

Benefits of Strong Collaboration

  • Improved data access and governance: IT provides secure, structured access while Data Science uses it for analysis and innovation.

  • Reliable, scalable infrastructure: Joint planning ensures that compute needs, storage, and APIs meet analytical demand.

  • Faster deployment of insights: Reduced time from model development to integration into products or dashboards.

Perils of Misalignment

  • IT restricts access due to security concerns, blocking insights.

  • Data scientists create isolated tools that strain unsupported infrastructure.

  • Critical models go unused due to integration gaps or performance issues.

Monthly Meeting Agenda: Data Science ↔ IT Sync

Duration: 60 minutes
Cadence: Monthly

Agenda:

  1. Infrastructure & Access Status (15 mins)
    IT updates on changes to environments, permissions, or system performance.

  2. Data Science Projects & Compute Needs (15 mins)
    DS shares active models, expected scale, tooling requirements, and roadblocks.

  3. Data Quality & Integration Issues (10 mins)
    Review anomalies, inconsistencies, or issues across data pipelines.

  4. Tooling & Platform Improvements (10 mins)
    Joint planning on cloud resources, automation tools, or monitoring systems.

  5. Security & Compliance Check-in (10 mins)
    Ensure data usage aligns with internal governance and regulatory expectations.

Collaboration Audit Checklist

Rate each item 1 (never) to 5 (always):

Audit QuestionScoreAre data scientists granted timely access to the data/tools they need?Are infrastructure requirements regularly reviewed jointly?Are data pipelines and storage solutions co-managed or aligned on standards?Is there a shared understanding of governance, privacy, and access protocols?Do both teams collaborate on deploying and monitoring models in production?

Scoring:

  • 20–25: High-impact collaboration

  • 15–19: Needs formalized roles and stronger syncs

  • <15: At risk of technical fragmentation or compliance failure

Joint KPIs / OKRs

Shared KPIs:

  • Time to provision data science environments

  • Frequency of pipeline outages affecting modeling

  • Security incidents related to data use or model access

  • Model deployment cycle time (from final model to production use)

Sample Joint OKRs:

Objective: Build a secure, scalable foundation for data-driven innovation

  • KR1: Provision new data environments within 48 hours for 90% of requests

  • KR2: Reduce data pipeline downtime by 50%

  • KR3: Achieve 100% compliance on access audits for sensitive data

  • KR4: Cut model deployment time by 30% through improved infrastructure coordination

Data Science, IT, InterdisciplinaryFrancesca Tabor