06 · Data Analytics
Models, Dashboards, and Signals the Business Can Act On.
Dimensional models, observable data, interactive dashboards, automated narratives, and a path off legacy BI.
SQL, dbt, Power BI, Looker, Snowflake / BigQuery / Databricks
The practice
What analytics covers
Advanced Analytics
Predictive and prescriptive models tied to marketing, supply chain, risk, and ESG decisions.
Data Modeling
Star and snowflake schemas, semantic KPIs, and domain products such as Customer 360.
Data Observability
Completeness, freshness, lineage, and alerts before a dashboard is wrong.
Data Visualization
Interactive dashboards and BI platforms people can explore.
Automated Insights
Narratives, natural-language questions, and copilots on governed models.
BI Migration
Inventory, logic conversion, and a move onto Power BI without losing the metric.
Where it applies
Decisions the models are built for
Marketing and Customer
Media mix, multi-touch attribution, segmentation, journey analysis, and churn.
Supply Chain
Forecast accuracy, control-tower visibility, and cost-to-serve.
Risk and Compliance
Anomaly detection, risk scores, and audit lineage.
ESG
One sustainability metric set, supplier risk, and compliance tracking.
Predictive and prescriptive
Decision support, not a second science practice
Predictive
Forecasts for demand, sales, and supply, plus scores for churn and fraud, early warnings, and the drivers behind a KPI.
Prescriptive
Pricing, inventory, and capacity recommendations, scenario comparisons, and cost-versus-service trade-offs.
Modeling
A semantic layer the rest of the practice reads
Dimensional and Semantic
Star and snowflake schemas with standardized KPIs, hierarchies, and metrics for self-service reporting.
Domain Products
Reusable products such as Customer 360, with the same definitions in dashboards and AI workflows.
Real-Time and Large-Scale
Streaming models for events and behavior, built for high volume and low latency.
Reusable Templates
Dimensional templates and slowly changing dimensions that shorten delivery without dropping the standard.
Validation
Schema, grain, and consistency checks, plus drift, freshness, and lineage from source to output.
Lakehouse Tuning
Models on Databricks, Snowflake, AWS, and Azure, tuned with partitioning, clustering, and indexing.
Observability
Trust before the number reaches a meeting
Quality Management
Completeness, accuracy, consistency, timeliness, and uniqueness, with profiling and business-rule checks.
Pipeline Visibility
Latency, reliability, and SLA adherence, plus schema changes and upstream impact.
Governance and Lineage
Movement, transforms, and dependencies, with access controls and an audit trail.
Detection and Remediation
Alerts with context, routed into incident handling, and change-impact checks so the same break does not return.
Traceable Model Inputs
Feature pipelines, embeddings, and prompts watched for drift, with a path from source data to the output.
Visualization
Dashboards people can use in the review
Dashboard Design
Complex sets turned into a clear view, with a layout that matches the decision.
Interactive Exploration
Metrics, trends, and performance that can be filtered in the moment.
Optimization
Existing boards tuned for speed, visual clarity, the data model, and repeated workflows.
AI-Assisted Visuals
Predictive and automated signals on the same visual layer as the descriptive report.
BI Platform Setup
Secure access, governance, scheduled refresh, and testing so the platform stays reliable.
Automated insights
Answers without a new dashboard for every question
Natural-Language Questions
Governed answers to a business question, without the asker learning the model.
Governed Self-Service
Prompts and questions for people who are not analysts, with the metric definition held in one place.
Narratives
A short explanation of why a metric moved, including the trend, the anomaly, and the driver.
Copilots
Help building reports, measures, and transforms, with the business logic kept consistent.
BI migration
Legacy reports onto Power BI
Assessment and Inventory
Reports, sources, calculations, and dependencies, then a choice of what to migrate, merge, or retire.
Metadata Extraction
Filters, calculations, and tables pulled from Tableau, Cognos, and similar tools so the logic is not retyped by hand.
Semantic Layer
Tables and views on Snowflake or another warehouse, shaped for Power BI.
Logic and Visuals
Legacy formulas recreated, including DAX, with redundant reports consolidated.
Shared Themes
Layout, type, spacing, headers, and navigation applied from reusable templates.
Validation and Training
Acceptance checks, performance, access, and workshops so the new reports get used.
Why this practice
What the work is for
Tied to the Decision
Models, insights, and dashboards are built for a KPI the business already reviews.
One Model
The same definitions serve BI, real-time views, and AI workloads.
Quality in the Design
Lineage, freshness, and checks sit in the model, not in a later cleanup.
Migration and Adoption
The move off the old tool and the training that makes the new one stick are one engagement.
Migration risks
How a BI move stays predictable
| Stage | Risk | How it is handled |
|---|---|---|
| Planning | Scale, cloud cost, and later analytics needs are understated. | A roadmap with a cost view, a contingency, and room to adjust scope. |
| Stakeholder engagement | Low buy-in leaves the new reports unused. | Business owners are in the inventory and the priority calls from the start. |
| Implementation | Logic is lost or governance is thin. | Phased releases, a held metric definition, and automated checks against the old report. |
| Training | People stay on the legacy tool. | Workshops, demos, and written steps for the reports they already use. |
Methodology
How Raw Sources Become a Model the Business Can Trust
Ingest with lineage, harmonize the dimensions, align facts to KPIs, then publish curated sets for BI and experiments.
- 01
Ingest with Lineage
Land source data with a trace back to the system it came from, before any metric is named.
- 02
Harmonized Dimensions
Standardize schemas, dimensions, and definitions so the same customer or product means the same thing everywhere.
- 03
KPI-Aligned Facts
Publish fact and dimension models that match the decisions leadership already reviews.
- 04
Curated Sets
Hand BI, machine learning, and experiments a curated set instead of a raw extract.