AI Needs Good Data.

Good Data Needs AI.

Reliable Data is the Bottleneck to AI

Organizations have invested heavily in modern data platforms, yet still struggle to deliver reliable, governed, AI-ready data into production. Pipelines break, governance arrives too late, and every new analytics or AI initiative feels like starting over, so teams spend more time fixing than progressing. 

The issue isn’t the tools; it’s the operating model. Traditional, human-heavy delivery and platform-first implementations create complexity without consistent outcomes, leaving most organizations without a repeatable way to produce high-quality data at scale. 

Without a trusted data foundation, analytics and AI are built on sand. 

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The Accelerate Data Approach

Tools alone don’t fix the problem, and you can’t hire your way out with unicorn engineers who magically keep everyone aligned over time. What works is a shared way of working, backed by a platform, that makes “governed” the default outcome—not a heroic exception. 

A governed data product is something you can trust because it’s defined and validated against a clear spec. That spec has:

Data Contract 80

1. Data Contract

defined via a model contract spec that locks down schema, freshness, and other guarantees;

Semantic Model 80

2. Semantic Model

describes the business entities and metrics the product exposes;

Business Logic 80

3. Tested Business Logic

expressed through lineage, DQ tests, and transformation‑logic tests that encode the rules your business actually cares about.

Every change is validated in sandbox against the spec before production—turning delivery into a repeatable loop: intent → spec → model → validated, operationalized product, with governance-as-code and AIOps built in. 

What this means for you

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AI Speed Delivery

Ship governed, reusable data products 50% faster, instead of one-off dashboards stuck in manual review queues

less

Less Rework

Use intent-driven specs and automated validation to reclaim engineering time and reduce firefighting

faster

Faster Recovery

Cut MTTR and alert noise by 75% with correlated signals and self-healing runbooks in your Azure observability stack

secure

Higher Trust

Improve data quality with DQ checks, SLOs, and policy-as-code embedded in the AIOps pipeline

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Stay In Control

Keep everything in your Azure tenant using open and first-party tools (Fabric, Databricks, Power BI, Data Factory, dbt, Airbyte). Your code and data remain yours

50%

reduction in time to build new governed data products in a priority domain.

75%

reduction in effort to maintain and troubleshoot data products  as incidents are detected, auto-healed, and measured instead of manually chased.

About Accelerate Data

Accelerate Data is led by founders with over 60 years of combined Data & AI experience, who previously built Just Analytics into an award‑winning data consultancy that was acquired to form the core of a global cloud data services business. Together, they’ve repeatedly turned complex data estates into performant, scalable, governed data platforms across the major clouds—experience that now shows up in how Accelerate Data blends platform, automation, and services. 

Co-founder

Hemanta Benerjee

Hemanta co‑founded Just Analytics and led it through multiple pivots and eventual acquisition, then went on to lead public‑cloud data services at global scale. Over 30+ years in data and analytics, he’s worked as an engineer, product leader, and services exec, helping enterprises across regions turn cloud data platforms on Azure, AWS, and Snowflake into real business outcomes. 

Co-founder

Umesh Kakkad

Umesh co‑founded Just Analytics and has spent more than a decade running delivery for complex data and analytics programs across APAC, including post‑acquisition leadership roles. He brings deep experience in getting enterprise data projects over the line: aligning architecture, delivery teams, and operations so that platforms don’t just get built—they stay reliable and supportable in production. 

Co-founder

Shwetank Sheel

Shwetank co‑founded Just Analytics and now leads data presales and solutions work with global customers, helping them architect data and AI foundations that actually land. A long‑time practitioner in Data & AI, he’s known for helping organizations modernise their data estates, connect data from R&D through to production, and make AI initiatives work in the messy reality of enterprise IT. 

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