Data Strategy

A structured discovery engine designed to define business value before execution begins.

Why most data projects fail

Why most data projects fail

Trial-and-error delivery is still common in data consulting. We only move to implementation once value has been clearly defined through structured discovery.

Jumping straight into delivery
Fixing problems in production
Ticket-driven data teams
Vague ROI expectations

Our framework

Diagnostic & Opportunity Map

Define where value actually lives

We conduct a structured assessment across People, Process, Technology, and Data, including stakeholder interviews, pipeline audits, and knowledge mapping, to identify structural weaknesses and immediate value levers.

Output → A prioritized Opportunity Map tied to business stakeholders.

Diagnostic & Opportunity Map
ROI Model
Strategy & Technical Design Document
Roadmap & Execution Plan
Executive Presentation

Our framework

Diagnostic & Opportunity Map

Define where value actually lives

We conduct a structured assessment across People, Process, Technology, and Data, including stakeholder interviews, pipeline audits, and knowledge mapping, to identify structural weaknesses and immediate value levers.

Output → A prioritized Opportunity Map tied to business stakeholders.

Diagnostic & Opportunity Map
ROI Model
Strategy & Technical Design Document
Roadmap & Execution Plan
Executive Presentation

Solutions in practice

Real Estate

Building a data strategy for a data-driven, AI-ready future

Neho's data stack was fragmented and reactive. Astrafy implemented a unified infrastructure on Google Cloud with Terraform and dbt Cloud, turning data into a governed, reliable product — reducing costs and shifting the team from firefighting to strategic work.

Where it starts

Your path to AI

Your path to AI

Many AI initiatives stall not because of model performance, but because business value was never clearly defined.

Many AI initiatives stall not because of model performance, but because business value was never clearly defined.

We define the business case first, so AI becomes a working system, not an experiment.

We define the business case first, so AI becomes a working system, not an experiment.

Why it matters

The foundation for every data decision

Investment
Clarity

Investment Clarity

Every initiative is tied to quantified business impact, so decisions are made on measurable value.

Controlled
Execution

Controlled Execution

Scope, dependencies, and ownership are defined before resources are committed, so execution is not improvised.

Stronger Data Ownership

Clear roles and governance prevent decay after implementation begins.

The earlier the strategy, the lower the cost and risk.

The earlier the strategy, the lower the cost and risk.

The earlier the strategy, the lower the cost and risk.