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Get an independent perspective on your data estate. Discuss a specific challenge with a senior Ardent architect and receive practical observations and recommended next steps. Free initial review. Written summary and next steps. No production access required initially

TRUST AND PROOF POINTS

Since 2008

Engineering data platforms and pipelines for organisations worldwide.

2m+ hours

Engineering delivered across data and software projects.

Platform agnostic

Expertise across data integration, governance and quality.

ISO 27001

Information security supported by independently certified processes.


WHY REVIEW YOUR DATA ESTATE?

Understand what's holding your data back.

Understand what's holding your data back.

Understand what's holding your data back.

Discrepancies between reports, lengthy processing windows and overlapping technologies can make it difficult to identify where problems originate or which improvements will deliver the greatest value.

Ardent works alongside your team to examine the challenges affecting your data estate, helping distinguish symptoms from potential causes and identify where further investigation could make a difference.

Reporting inconsistencies

Conflicting figures and inconsistent metrics can undermine confidence in business reporting and decision-making.

Performance bottlenecks

Slow pipelines, extended processing windows and repeated failures can affect data availability and operational efficiency.

Growing complexity

Overlapping tools, unclear dependencies and fragmented ownership can make your data environment harder to manage and improve.

WHAT WE ASSESS

A closer look at what matters.

From data architecture to reporting and performance, we focus on the areas relevant to your challenge, helping identify potential risks, inefficiencies and opportunities for improvement.

Architecture & Dependencies

Trace data sources, ingestion, transformations and downstream dependencies to identify complexity and overlapping technologies.

Data Models & Transformations

Examine core data structures, transformation logic and historical data to assess consistency and support for downstream reporting.

Reporting & Reconciliation

Review metric definitions, reporting periods and source-to-report totals to investigate discrepancies and potential processing errors.

Quality & Observability

Consider data freshness, integrity checks and failure alerts to identify gaps in monitoring and data reliability.

Performance & Cost

Explore refresh patterns, workload bottlenecks and duplicate processing to identify opportunities for improved efficiency.

Ownership & Change

Review responsibilities, knowledge dependencies and release controls to highlight potential operational risks.

HOW THE FREE REVIEW WORKS

Bring a challenge. Leave with a clearer direction.

Our free initial review focuses on an agreed area of your data estate, providing an independent perspective without requiring a comprehensive technical investigation.

01

Discuss your challenge

Tell us what needs attention, from inconsistent reporting and processing delays to data quality or platform complexity.

02

Review with a senior architect

An Ardent architect works through the agreed area with your team, using existing documentation and discussion. No production access is required.

03

Receive practical next steps

Get a concise written summary of initial observations, potential risks and recommended next steps, with areas requiring further investigation clearly identified.

WHAT YOU RECEIVE

Useful insight. Clear next steps.

Following your free Data Estate Review, you'll receive a concise written summary of the agreed area, helping you understand where attention may be needed and what to consider next.

High-level observations

An independent perspective on the challenges discussed, based on the information and documentation shared with Ardent.

Potential risks & uncertainties

Areas of concern that may warrant attention, with uncertainties and issues requiring further validation clearly identified.

Recommended next steps

Combine anomaly detection with code-based validation, catching both unexpected changes and known rule failures before they reach business users.

Your findings. Your choice.

Your findings. Your choice.

Use the summary with your own team or explore further support from Ardent. There's no obligation to commission additional work.

Use the summary with your own team or explore further support from Ardent. There's no obligation to commission additional work.

Request a Free Data Estate Review

Request a Free Data Estate Review

FEATURED SUCCESS STORY

FEATURED SUCCESS STORY

Optimising big data queries for large volumes of complex data

Optimising big data queries for large volumes of complex data

Optimising big data queries for large volumes of complex data

Optimising big data queries for large volumes of complex data

Read the success story →

80%

80%

reduction in manual data entry

reduction in manual data entry

3X

3X

faster document processing

faster document processing

DATA ESTATE REVIEW FAQS

Frequently asked questions

The free review focuses on an agreed area of your data estate. A senior Ardent architect discusses your challenges, reviews relevant existing documentation and provides a concise written summary of initial observations, potential risks and recommended next steps.

No production access is required for the free initial review. We work with your team using existing documentation and discussions to understand the challenges. Any additional access required for a deeper technical assessment would be agreed separately.

The free review provides high-level observations and guidance on where to focus next. The paid technical assessment involves a more detailed investigation, delivering evidence-backed findings, documented architecture, recommendations and a sequenced action plan. The scope and fixed fee are agreed in advance.

Yes. Where appropriate, Ardent can support implementation and ongoing management. During a paid technical assessment, our engineers may also implement and validate selected improvements where feasible, subject to your approval and the agreed scope.

No. The free review comes with no obligation to commission further work. You can use the findings with your own team or engage Ardent for a more detailed assessment or implementation support.

Most organisations feel the pain of integration first because unreliable or slow data movement is immediately visible. It is reasonable to start there, but governance should not lag far behind. Faster data movement without access control, lineage and quality checks simply moves unreliable information around more quickly. We usually design the integration and governance layers together, then phase implementation around the most urgent business need.

They solve different parts of the data engineering problem. Kafka moves events between systems and Debezium captures database changes so downstream platforms stay current. Unity Catalog, Collibra and similar governance tools control, catalogue and trace the data once it is available. A modern data platform often needs both layers: reliable movement on the way in and trusted, governed access when the data arrives.

Where a well-supported connector already exists, using it is usually faster and easier to maintain than writing extraction code from scratch. Fivetran is useful when low operational overhead is the priority, while Airbyte offers more flexibility and control, including self-hosted options. Custom engineering still has a place for unusual sources, specialised transformations or requirements that an off-the-shelf connector cannot meet, but it should be a deliberate choice rather than the default.

Monte Carlo focuses on data observability, automatically looking for unexpected changes in areas such as freshness, volume and schema. Great Expectations validates explicit rules you define as code, such as required fields, ranges or accepted values. They are complementary: observability helps surface problems you did not know to test for, while pipeline-embedded validation enforces the conditions you already know must be true.

Both. Ardent can assess the existing estate, design the architecture and build the integration, governance and quality layers. Our managed services team can then take on ongoing pipeline monitoring, data-quality management, incident response, stewardship support and continuous improvement, helping internal teams focus on new data products and business priorities rather than day-to-day firefighting.

Most organisations feel the pain of integration first because unreliable or slow data movement is immediately visible. It is reasonable to start there, but governance should not lag far behind. Faster data movement without access control, lineage and quality checks simply moves unreliable information around more quickly. We usually design the integration and governance layers together, then phase implementation around the most urgent business need.

They solve different parts of the data engineering problem. Kafka moves events between systems and Debezium captures database changes so downstream platforms stay current. Unity Catalog, Collibra and similar governance tools control, catalogue and trace the data once it is available. A modern data platform often needs both layers: reliable movement on the way in and trusted, governed access when the data arrives.

Where a well-supported connector already exists, using it is usually faster and easier to maintain than writing extraction code from scratch. Fivetran is useful when low operational overhead is the priority, while Airbyte offers more flexibility and control, including self-hosted options. Custom engineering still has a place for unusual sources, specialised transformations or requirements that an off-the-shelf connector cannot meet, but it should be a deliberate choice rather than the default.

Monte Carlo focuses on data observability, automatically looking for unexpected changes in areas such as freshness, volume and schema. Great Expectations validates explicit rules you define as code, such as required fields, ranges or accepted values. They are complementary: observability helps surface problems you did not know to test for, while pipeline-embedded validation enforces the conditions you already know must be true.

Both. Ardent can assess the existing estate, design the architecture and build the integration, governance and quality layers. Our managed services team can then take on ongoing pipeline monitoring, data-quality management, incident response, stewardship support and continuous improvement, helping internal teams focus on new data products and business priorities rather than day-to-day firefighting.

Most organisations feel the pain of integration first because unreliable or slow data movement is immediately visible. It is reasonable to start there, but governance should not lag far behind. Faster data movement without access control, lineage and quality checks simply moves unreliable information around more quickly. We usually design the integration and governance layers together, then phase implementation around the most urgent business need.

They solve different parts of the data engineering problem. Kafka moves events between systems and Debezium captures database changes so downstream platforms stay current. Unity Catalog, Collibra and similar governance tools control, catalogue and trace the data once it is available. A modern data platform often needs both layers: reliable movement on the way in and trusted, governed access when the data arrives.

Where a well-supported connector already exists, using it is usually faster and easier to maintain than writing extraction code from scratch. Fivetran is useful when low operational overhead is the priority, while Airbyte offers more flexibility and control, including self-hosted options. Custom engineering still has a place for unusual sources, specialised transformations or requirements that an off-the-shelf connector cannot meet, but it should be a deliberate choice rather than the default.

Monte Carlo focuses on data observability, automatically looking for unexpected changes in areas such as freshness, volume and schema. Great Expectations validates explicit rules you define as code, such as required fields, ranges or accepted values. They are complementary: observability helps surface problems you did not know to test for, while pipeline-embedded validation enforces the conditions you already know must be true.

Both. Ardent can assess the existing estate, design the architecture and build the integration, governance and quality layers. Our managed services team can then take on ongoing pipeline monitoring, data-quality management, incident response, stewardship support and continuous improvement, helping internal teams focus on new data products and business priorities rather than day-to-day firefighting.

Ready to build data foundations your business can trust?

Whether you are modernising pipelines, improving governance or putting automated quality around the data that feeds reporting, automation and AI, Ardent can help you design, build and run the right data engineering foundation.

Talk to a data engineering specialist

Talk to a data engineering specialist

Talk to a data engineering specialist