Data & Analytics Advisory

Enterprise data architecture design, governance program development, and AI/ML readiness assessments that transform data assets into competitive advantage.

From Data Complexity to Decision Intelligence

Enterprise data environments have grown dramatically in complexity over the past decade. The proliferation of SaaS applications, cloud data warehouses, real-time streaming platforms, and edge devices has created data landscapes that most organizations struggle to govern, let alone extract analytical value from.

Enigma's data advisory practice helps organizations design coherent data architectures, implement governance frameworks with real operational bite, and build the technical and organizational foundations required to support reliable, trustworthy business intelligence and AI-assisted decision-making.

Our data advisors bring deep hands-on expertise across Snowflake, Databricks, AWS Redshift, Azure Synapse, dbt, Apache Kafka, and the full Tableau/Power BI/Looker BI landscape — enabling us to provide guidance that is simultaneously strategic and technically credible.

Data analytics professional reviewing multiple dashboards and data visualization charts on dual large-format monitors in a modern analytics workspace

Data & Analytics Capabilities

Enterprise Data Architecture

Assessment and redesign of enterprise data architecture spanning data lakes, warehouses, operational databases, and streaming pipelines — aligned with analytical and operational requirements.

Data Governance Framework

Design of enterprise data governance programs including data ownership structures, stewardship roles, data quality standards, lineage tracking requirements, and policy enforcement mechanisms.

BI Platform Evaluation

Structured evaluation of business intelligence platforms — Tableau, Microsoft Power BI, Looker, Qlik, and emerging competitors — against your specific analytical use cases and technical requirements.

AI/ML Readiness Assessment

Evaluation of your organization's readiness to deploy and govern AI/ML systems — covering data quality, infrastructure, talent, governance frameworks, and ethical AI considerations.

Master Data Management

Strategy and platform advisory for MDM programs — covering customer, product, supplier, and financial master data domains — including golden record design and integration architecture.

Data Quality Program Design

Design of systematic data quality measurement, monitoring, and remediation programs — including DQ dimension frameworks, profiling tooling evaluation, and executive KPI dashboards.