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Arixent

Data Science & Engineering

Data Strategy, Architecture & Governance

Target architecture, operating model and the governance that makes data trusted and AI-ready.

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Overview

A data strategy answers three questions: which decisions should be better because of data, what needs to be true about the data for that to happen, and how the organization will own it. Architecture turns the answers into a platform design, a delivery plan and an operating model.

We design target architectures that are practical for your team to run: cloud-native, open where it counts, governed from the start and sized for the workloads you actually have rather than the ones a vendor imagines.

This page also covers data governance and quality: the catalogs, lineage, access controls and quality checks that make a data platform trustworthy.

Offering 1

Data strategy and architecture

A data strategy answers which decisions should be better because of data and what needs to be true about the data for that to happen. Architecture turns the answer into a platform design, a delivery plan and an operating model.

Data strategy and use-case alignment

Decisions, metrics and data products prioritized against business value and feasibility.

Target-state architecture

Lakehouse, warehouse, streaming and integration patterns designed for your cloud, scale and skills.

Platform and tooling selection

Evaluation of Snowflake, Databricks, BigQuery, Fabric and open-source options against your requirements and budget.

Data operating model

Domain ownership, data product teams, stewardship and the governance forum that keeps them aligned.

Typical use

  • Companies replacing a legacy warehouse and unsure which platform to bet on
  • Leadership teams that want AI but lack a data foundation for it
  • Fast-growing SaaS companies whose analytics has outgrown the production database

Offering 2

Data governance and quality

Governance done right is mostly engineering: automated quality checks, a catalog people actually use, lineage you can trace, access controls that match policy and clear owners for every important dataset.

Padlock formed from points of light over columns of data

Data quality frameworks

Rules, tests and monitoring for accuracy, completeness, freshness and consistency, with ownership and alerts.

Cataloging and discovery

Business glossary, technical catalog and search so people can find and understand data.

Lineage and impact analysis

Column-level lineage from source to report, so changes and incidents can be traced.

Access control and privacy

Role-based access, masking, tokenization and residency controls aligned to policy and law.

Typical use

  • Regulatory reporting that must be traceable and defensible
  • Enterprises with conflicting customer or product definitions across systems
  • Data platforms with growing usage and unclear ownership

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How we work

How we deliver

  1. 01

    Discover

    Interviews, current-state inventory, pain points and the decisions that matter most.

  2. 02

    Design

    Target architecture, tooling recommendation, operating model and security approach.

  3. 03

    Validate

    A thin slice built on the target platform to prove the pattern and the cost model.

  4. 04

    Plan

    Roadmap, business case and the first delivery increment ready for the engineering team.

Stack

Tools we work with

We are neutral on tooling and pick what fits your environment, your team and the cost you can sustain.

  • Snowflake, Databricks, BigQuery, Redshift, Synapse, Microsoft Fabric
  • Delta Lake, Apache Iceberg
  • dbt
  • Airflow and Dagster
  • Kafka
  • Fivetran and Airbyte
  • Collibra, Alation, Atlan, DataHub, OpenMetadata
  • Unity Catalog, Purview
  • Immuta and native masking
  • Terraform

Bring the problem. We bring the team.

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Industries

Where we apply this most

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Banking, Financial Services & InsuranceDocument intelligence, risk models, fraud detection and modern digital banking experiences.Explore
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Retail & E-commercePersonalization, demand forecasting, search and customer data platforms.Explore
Row of white robotic arms on an automated production line
Manufacturing & IndustrialPredictive maintenance, quality inspection, digital twins and plant analytics.Explore
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Telecom & MediaNetwork analytics, churn prediction, content recommendation and OSS/BSS modernization.Explore

FAQ

Questions we hear often

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Next step

Ready when you are.

Tell us what you are trying to build and we will come back with a point of view, not a pitch.

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