AI & Data Strategy That Delivers Business Value

What is Data Strategy?

Data strategy is the blueprint for how your organization captures, manages, analyzes, and leverages data to achieve business objectives. It's not just about technology—it encompasses people, processes, and governance alongside the technical infrastructure.

A good data strategy answers fundamental questions: What data do we have? What data do we need? How do we ensure data quality? Who can access what? How do we turn data into decisions? Without answers to these questions, companies often build expensive infrastructure that doesn't deliver business value.

At Salt, we believe data strategy should be driven by business outcomes. We start with your goals—revenue growth, cost reduction, operational efficiency, customer experience—and work backward to the data and technology needed to achieve them.

Components of a Modern AI & Data Strategy

An effective AI and data strategy encompasses several interconnected components that must work together:

• Data Infrastructure – The technical foundation including data warehouses, lakes, pipelines, and integration systems
• Data Governance – Policies, processes, and ownership for data quality, security, and compliance
• Analytics & BI – Reporting, dashboards, and self-service tools for business intelligence
• Machine Learning – Predictive models, recommendations, and automated decision-making
• AI Applications – LLM-powered assistants, agents, and intelligent automation
• Organizational Capability – Skills, culture, and operating model to leverage data effectively

Building a Modern Data Platform

The foundation of any data strategy is a robust data platform—the infrastructure that collects, stores, transforms, and serves data. Modern data platforms have evolved significantly from traditional data warehouses.

Key Components of a Modern Data Stack

• Cloud Data Warehouse – Scalable storage and compute (Snowflake, BigQuery, Databricks, Redshift)
• Data Integration – Tools to extract data from sources (Fivetran, Airbyte, Stitch)
• Data Transformation – Convert raw data into analytics-ready models (dbt, Dataform)
• Orchestration – Schedule and monitor data pipelines (Airflow, Dagster, Prefect)
• Data Quality – Monitor and ensure data accuracy (Great Expectations, Monte Carlo)
• Data Catalog – Discover and understand available data (Atlan, Alation)

We help clients design and implement data platforms that balance capability with complexity. Not every company needs every tool—we recommend the right stack for your scale, budget, and use cases.

Analytics & Business Intelligence

Analytics and BI transform raw data into actionable insights. This spans from basic reporting and dashboards to advanced analytics and data exploration.

Modern BI emphasizes self-service—enabling business users to answer their own questions without waiting for IT. This requires thoughtful data modeling, clear metrics definitions, and intuitive tools.

Our Analytics Capabilities

• BI platform implementation (Looker, Tableau, Power BI, Metabase)
• Dashboard design and development
• KPI frameworks and metrics modeling
• Self-service analytics enablement
• Embedded analytics integration
• Real-time operational dashboards

Machine Learning in Production

Machine learning enables predictions, recommendations, and automated decisions at scale. But there's a vast gap between ML experiments and production systems. Most ML projects fail not because the models don't work, but because they never make it out of notebooks.

We build ML systems designed for production from day one. This means proper feature engineering, reproducible training pipelines, model versioning, A/B testing capabilities, monitoring for drift, and automated retraining.

Common ML Use Cases We Implement

• Forecasting – Demand prediction, revenue forecasting, capacity planning
• Recommendations – Product suggestions, content personalization, next best action
• Classification – Customer segmentation, fraud detection, intent prediction
• NLP – Sentiment analysis, entity extraction, document classification
• Computer Vision – Image classification, object detection, quality inspection

AI Copilots & Intelligent Agents

Large Language Models (LLMs) have created new possibilities for AI applications. Beyond chatbots, we're building systems that can reason, plan, and take actions to complete complex tasks.

Types of AI Systems We Build

• RAG Systems – Question-answering over your documents and knowledge bases
• AI Copilots – Assistants that help employees with specific workflows
• Autonomous Agents – Systems that can plan and execute multi-step tasks
• Document AI – Extract, classify, and route information from documents
• Conversational AI – Customer-facing chatbots and voice assistants

We integrate with leading LLM providers (OpenAI, Anthropic, Google) and use frameworks like LangChain and LlamaIndex to build production-grade AI applications. Security and data privacy are paramount—your data stays yours.

Responsible AI & Governance

As AI becomes more powerful, governance becomes more critical. We believe responsible AI isn't a constraint—it's a prerequisite for successful deployment. Systems without proper guardrails create risk and erode trust.

Our Responsible AI Practices

• Bias Detection – Identify and mitigate unfair outcomes in model predictions
• Explainability – Make model decisions interpretable for stakeholders
• Monitoring – Track model performance, drift, and fairness over time
• Guardrails – Implement controls that prevent harmful outputs
• Audit Trails – Maintain records for compliance and debugging
• Human-in-the-Loop – Design systems that keep humans in control

Salt's Approach to Data & AI

What makes Salt different from other data and AI consultancies? We combine strategic thinking with hands-on engineering. We don't just write recommendations and leave—we build and deploy production systems.

Key Principles

• Business-First – Every initiative ties to measurable outcomes
• Production-Ready – We build for production, not just demos
• Iterative Delivery – Show value quickly, then expand
• Knowledge Transfer – Build your team's capability alongside systems
• AI-Native – We use AI in our own delivery for better results

Our Data & AI pods bring senior expertise immediately—data engineers, ML engineers, analytics engineers, and AI specialists. You get a functioning team in weeks, not months.

Getting Started with AI & Data Strategy

Ready to explore how data and AI can transform your business? Start with a Discovery & Assessment engagement—typically 2-3 weeks—where we understand your current state, identify opportunities, and create a prioritized roadmap.

From there, we can tackle quick wins while building toward larger capabilities. Many clients start with a specific use case—a dashboard, a prediction model, an AI assistant—and expand as they see results.

Schedule a strategy call with our team to discuss your data and AI challenges. We'll share honest advice on where to start and what's realistic for your situation.