Data Services

Data Analytics Consulting Services for Decisions You Can Defend

Self-service BI, predictive analytics, ML insights, and AI copilots built on a foundation pipelines can actually trust.

Overview

Analytics and BI built on data your team can trust

Our data analytics consulting services turn warehoused data into decisions. We deliver self-service BI, predictive analytics, ML-driven insights, and AI-powered analytics copilots measured against the KPIs your leadership team already cares about. Twelve years of BI delivery, 30+ clients. Business intelligence and data analytics services and BI consulting services under one accountable team.

Years in business: 12
Team members: 65+
Global clients: 30+
Yr avg. client retention: 4+

Who this is for

D2C and eCommerce founders who want LTV, CAC, cohort, and churn analytics that hold up under questioning.
Operations and finance leaders who need forecasting, scenario modeling, and budget variance analytics.
Analytics heads building a self-service culture without burning out their analyst bench.
Subscription businesses looking for retention, win-back, and product-mix analytics.

What you get

Analytics roadmap mapped to the questions leadership is asking, with a clear sequence and success metrics.
Self-service BI - curated semantic layers, certified datasets, and dashboards business users can extend without breaking governance.
Predictive analytics - demand forecasting, churn prediction, LTV modeling, customer segmentation. We have shipped ~90% forecast accuracy in peak seasons.
ML-driven insights - RFM, K-means, classification, regression - applied to live business questions, not academic exercises.
AI copilots and NLP BI - natural-language query layers, AI-generated narratives, and analytics agents where they pay for themselves.
Stakeholder enablement - training, documentation, and a hand-off plan so your team owns the work.

How we work

01 Step

Audit

Map the questions leadership wants answered, the data we have, the data we are missing, and the success metrics.
02 Step

Plan

Sequence the work - what we ship in week 4, week 8, week 16 - and define the semantic and modeling layer.
03 Step

Build

Ship dashboards, models, and analytics layers in increments. Each release is tied to a defined use case.
04 Step

Test

Validate insights against ground truth. Run A/B and multivariant tests where appropriate to confirm directional signals.
05 Step

Scale

Layer predictive models and AI copilots once the foundation is trusted. Roll out self-service progressively.

Tools & stacks we use

The platforms our team is fluent in for this practice. Most engagements span a few of these, picked for the actual problem rather than for the demo.

Power BI
Tableau
Looker
Domo
Sisense
Python
R
Databricks ML
Snowpark ML
dbt metrics
LookML
Snowflake Cortex
Power BI Copilot
Tableau Pulse
Snowflake
BigQuery
Redshift
Databricks

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.

Data Analyst Hire a Data Analyst Power BI Developer Hire a Power BI Developer Tableau Developer Hire a Tableau Developer

See all 22 roles

Frequently asked questions

Still have a question? Talk to a real human on our team - we usually reply within one business day.

Talk to a specialist

What is data analytics consulting and how is it different from BI consulting?

Analytics consulting is the broader practice - it includes data strategy, modeling, predictive layers, and AI applications. BI consulting is a subset focused on reporting, dashboards, and self-service. We do both, in the same engagement.

How does H4H run an analytics engagement?

Audit, Plan, Build, Test, Scale. We start with the questions leadership wants answered and predefined success metrics, then ship in increments validated against those metrics.

How much do data analytics consulting services cost?

Project bands typically start in the low five figures for a focused analytics build and scale with dashboard count, model complexity, and team size. Retainer and dedicated FTE quoted separately.

How long does a typical engagement take?

A self-service BI rollout runs 4-12 weeks. A predictive analytics build runs 6-14 weeks. AI copilot integration runs 4-8 weeks after the foundation is in place.

What results can I expect?

Outcomes we have measured include ~90% peak-season forecast accuracy (Bouqs), 10% NPS lift attributable to forecasting (Bouqs), company-wide dashboard adoption (LegalZoom), and 24-hour client onboarding via templated dashboards (D.Luxury Brands).

Do you build models on top of our existing warehouse?

Yes. We meet stacks where they are. We have shipped on every major cloud warehouse and BI platform.

How do you decide between predictive analytics and AI copilots?

Predictive analytics earns its keep when the decision is repeated and the cost of being wrong is measurable (inventory, churn, ad spend). AI copilots earn theirs when the bottleneck is access - when too many people need to ask the same question, fast.

How is H4H different from a Power BI or Tableau partner?

Tool partners help you implement a tool. We help you decide what to measure first, build the data layer that feeds it, and put the right tool on top - sometimes that is Power BI, sometimes Tableau, sometimes something else.

Proof points

Related case studies

What we have shipped for clients with adjacent problems. Each one is sourced and attributable.

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.

Read case study

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.

Read case study

Related services

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.

Book a free data audit Talk to a data expert