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ONEOK × Slalom
Collaborative Working Session
Data & AI
Strategy
Session
Agenda
Session Overview
Current State Overview
Build a shared understanding of the current environment
Market Perspective
Slalom perspective on how leading organizations are evolving data & AI
Focused Working Session
Deeper working discussion based on priority areas
Target State Definition
- 12–24 month vision for data & AI
- Maturity goals across capabilities
- Key success metrics & outcomes
Operating Model
- Align capabilities to business domains
- Team structure: embedded vs centralized
- Governance, funding & ownership models
Product & Delivery Framework
- Define data products & lifecycle
- Ownership, TCO & sustainment
- Platform & tooling implications
Prioritization & Portfolio
- Evaluation criteria: value, feasibility
- Balance quick wins vs long-term
- Consistent intake framework
Industry Context
Data & AI in midstream energy
Key trends shaping the data and AI landscape across the midstream sector today.
Post-merger data integration
Large-scale M&A activity creates overlapping data architectures, duplicate systems, and institutional knowledge risk across hundreds of applications. Establishing a unified data foundation is the critical first step.
OT/IT convergence
SCADA and operational technology data is increasingly flowing into enterprise analytics platforms. Midstream operators are pursuing pipeline performance monitoring, anomaly detection, and predictive maintenance — but the data architecture must support it.
AI-energy nexus
Dedicated gas infrastructure serving 500MW+ data centers is creating new data modeling, forecasting, and asset management complexity. The intersection of energy and AI infrastructure is a growing strategic opportunity.
Governance under pressure
As AI adoption accelerates in energy, regulators, boards, and insurers are asking harder questions. Data lineage, model explainability, and access controls are moving from aspirational to required.
CDO role evolution
The CDO is shifting from data steward to business value driver. Leading organizations are embedding data product owners in business domains to drive measurable outcomes.
Platform consolidation
Midstream operators are rationalizing fragmented analytics stacks toward unified lakehouse architectures — reducing complexity while increasing analytical power and governance control.
Slalom + Databricks
Partnership & platform expertise
As a Databricks Premier partner, Slalom brings deep platform expertise, certified practitioners, and a joint go-to-market model designed to accelerate time to value.
With Databricks already part of the technology landscape, the opportunity is acceleration — not adoption. Slalom's Premier partnership means we bring certified practitioners, proven accelerators, and a joint delivery model to help you get value from the platform faster.
500+
Databricks engagements delivered
200+
Certified Databricks practitioners
15+
Industries served
Premier
Partner tier — top 1% globally
Premier partner status
Slalom is one of a select number of Databricks Premier partners nationally, with certified practitioners across data engineering, ML, and platform architecture.
Joint delivery model
We co-deliver with Databricks — shared account planning, aligned success metrics, and a single escalation path. This means faster resolution and better outcomes.
Lakehouse architecture expertise
Delta Lake, Unity Catalog, MLflow, and Databricks SQL are core delivery competencies. We help organizations move from proof of concept to production without rebuilding.
Energy sector data patterns
We've applied Databricks to pipeline operations data, field sensor integration, and regulatory reporting workflows — use cases directly relevant to midstream challenges.
Governance accelerators
Unity Catalog implementation, data lineage documentation, and access control frameworks that satisfy both technical and compliance requirements.
Joint solution areas
Lakehouse Implementation
End-to-end Delta Lake deployment with medallion architecture, optimized for midstream data volumes and real-time ingestion patterns.
MLOps & AI at Scale
Production ML pipelines using MLflow, model registry, and Feature Store — from predictive maintenance models to demand forecasting.
BI & Analytics Modernization
Databricks SQL endpoints powering self-service analytics, replacing legacy warehouse sprawl with a unified semantic layer.
Data Mesh & Governance
Unity Catalog-driven governance with domain-oriented ownership, automated data quality, and enterprise-grade access controls.
Databricks certifications across our team
Capabilities
How Slalom can help
Our core competencies across data, AI, cloud, and organizational design.
Data Engineering
Cloud + Platform
AI & Agentic
Governance & Controls
Operating Model Design
Industry Depth
What's Next
Potential next steps
Based on today's conversation, here are a few directions we could explore together.
Data & AI Readiness Assessment
A focused 4–6 week engagement to evaluate current data maturity, identify quick wins, and build a prioritized roadmap aligned to business outcomes.
Follow-on working session
Bring broader stakeholders into the conversation — a deeper target state discussion with the CDO, platform owners, or business domain leads.
Joint Databricks alignment
A combined session with Slalom, ONEOK, and the Databricks account team to align on platform strategy and acceleration opportunities.