Small Tools for Big Data - Management, Cleansing, Processing
Integration of Disparate Systems & Data Sources
Clean & Simple Data Quality Reporting

Small tools, big data

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Effective Data Management

60% of organisations lack accountability for data quality, while more than 50% have severe doubts about the validity of their data.

Many organisations find their data is riddled with errors and missing information.

Managing Data - It's not Rocket Science

eSensible aims to simplify big data with small tools and sensible methods. Whether it be data validation, integration, migration, or handover, we can help you manage your data and improve quality. We provide solutions to gather data from disparate systems, process and validate that data, and automatically report errors.

Please read about our vision, and view our products.

We live in a big data era, and for many industries the management of that data determines the success or failure of the business. Big data is daunting, but we urge you to remember one thing – it’s only data, it’s not rocket science.


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Data Management : Simplified

Class Library Driven

Class Library Driven Engineering SoftwareOur methods, and most of our products, are Class Library-driven. Consider this a ‘data model’ that defines structure, relationships, and validation rules. Using a Class Library model we can efficiently process, analyse, and report, separating valid data from invalid data, and migrating what’s needed.

Effective ReportingBeing able to report on data errors in a simple manor is critical for most organisations. Our solutions provide means of effectively producing reports on segments of data, both as a document or a data file, which can be automatically emailed to a relevant custodian.

By keeping the tools simple, it makes integration easy and scalable. From managing a simple Excel file to a vast multi-faceted system-intensive project, our tools can help you out.

Feel free to start small and build up. Most data-centric operations can be compartmentalised and dealt with in simple ways. We prefer to think of data management in small parts, building a cohesive whole.