Data Services
Data Consulting Services That Move Numbers, Not Slideware
Strategy, warehousing, engineering, analytics, and BI - for D2C, eCommerce, and digital-first businesses that want decisions tied to revenue.
Overview
Strategy, build, and outcomes - under one team
H4H delivers end-to-end data consulting services for mid-market businesses. We build the warehouse, ship the pipelines, model the data, design the dashboards, and stay accountable to the KPIs we agreed in audit. 12 years in BI, 30+ clients, a named team, and measurable outcomes.
Who this is for: D2C and eCommerce founders who want one source of truth across Shopify, GA4, paid platforms, email, and CRM; heads of analytics at mid-market businesses who need to extend their bench without hiring a full team; operations and finance leads who need forecasts, cohort views, and inventory analytics the existing stack cannot deliver; and agencies building client-facing dashboards at scale. Our work spans data management, data integration, data processing, data governance and quality, business intelligence, and cloud data migration - with data strategy development guiding every build.
- Years in business
- 12
- Team members
- 65+
- Global clients
- 30+
- Yr avg. client retention
- 4+
Years in business
Team members
Global clients
Yr avg. client retention
Our services
Data Warehousing
Cloud data warehouse and lakehouse architecture on Snowflake, BigQuery, Redshift, and Databricks Delta Lake. Legacy migration, scalable storage, performance optimization.
Explore →Data Engineering
Pipeline development, ETL/ELT, real-time streaming, schema design, orchestration on dbt, Airflow, Kafka, and Fivetran.
Explore →Data Analytics & Business Intelligence
Self-service BI, predictive analytics, ML insights, autonomous analytics copilots, NLP in BI - built on top of cleaned, modeled data.
Explore →Data Visualization & Dashboarding
Power BI, Tableau, Looker, Domo, and Sisense dashboards that drive decisions instead of decorating walls.
Explore →
Why work with us
What makes the engagement different
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A consulting-driven approach
We start with the problem statement and predefined success metrics - not with a tool recommendation. Accurate KPIs in audit are the precondition for everything that follows.
-
Solution-driven, not complexity-driven
We pick a stack that solves the purpose. We have shipped on Snowflake, Databricks, BigQuery, and Redshift - and we have killed projects where a simpler stack would have worked.
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Scalable architecture from day one
Schemas, fact and dimension tables, pipelines, validation scripts, governance - all of it set up so the warehouse holds up when volume and questions multiply.
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ROI-focused AI enablement
Where AI helps, we apply it. Where it does not, we say so. Generative dashboards, NLP queries, and predictive layers go in when they move attributable outcomes.
How we work
- 01 Step
Audit
We map data sources, owners, KPIs, gaps, and the decisions the team is trying to make. Output is a problem statement, scope, and success metrics.
- 02 Step
Plan
We pick the stack, define the architecture, schema, and pipelines. We document data definitions for every field that matters.
- 03 Step
Build
We stand up the warehouse, ingestion, transformation, and visualization layer in increments. Each increment is signed off against the success metrics.
- 04 Step
Test
Validation scripts, data quality checks, performance benchmarks, and stakeholder review before each rollout.
- 05 Step
Scale
Once adoption is proven, we layer predictive models, AI copilots, and self-service BI on top of the foundation.
Need dedicated experts?
Hire a specialist embedded with your team
Pre-vetted senior talent for this practice - hourly, retainer, dedicated FTE, or Micro-GCC. Vetted in 48 hours, managed end-to-end by H4H operations.
Frequently asked questions
Still have a question? Talk to a real human on our team - we usually reply within one business day.
What is data consulting and what do you actually deliver?
How does H4H run a data consulting engagement?
How much does data consulting cost?
How long does a typical engagement take?
What results can I expect?
How is H4H different from a traditional consulting firm or a freelance data engineer?
Can you work inside our existing stack?
Will the work transfer cleanly to my in-house team?
Proof points
Related case studies
What we have shipped for clients with adjacent problems. Each one is sourced and attributable.
LegalZoom - Professional Services / LegalTech
Company-wide adoption from execs to department leads
Built data warehouse, pipelines, and a Tableau visualization layer balancing performance with drill-down to grain-level data.
Bouqs - D2C / Subscription Flowers
~90% forecast accuracy in peak season. NPS +10%.
ML-driven demand forecasting across SKUs cut buffer-stock costs for Mother's Day and Valentine's Day fulfillment.
CandyClub - D2C / Subscription
Subscription data architecture enabling rotation, churn, and product-mix analytics
End-to-end ingestion, validation, and modeling for a curated subscription brand. Pipelines surfaced rotation, churn, and product-mix patterns the prior stack could not.
Ready to put your data to work?
Book a free audit and we will map the problem, the metrics, and the smallest first build that proves value.