Data discovery platform for AI

As data lifecycles become more intricate, Datagusto ensures effective and accurate data for AI systems, eliminating inconsistencies and preventing costly failures.

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Connect with your data systems

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and many more

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Shift happens

As data ecosystems and pipelines become more complex in the AI era, maintaining consistent and synchronised data is essential. However, existing solutions often struggle to manage the rising risks and costs of inconsistencies and disruptions.

This gap highlights the urgent need for an innovative data discovery solution that ensures reliability and accuracy across all data sources.

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Data Discovery is mandatory for maintaining consistent and synchronised data across complex systems.

New complexities demand innovative solutions

Introducing

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Data Discovery

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What is data discovery?

Data discoverability is about uncovering, understanding, and managing your data landscape to ensure consistency, accuracy, and seamless integration. Datagusto takes on the complexity of this process, providing key benefits:

Understand Your Data Architecture

  • Visualise how data moves and interacts across your organisation.
  • Identify potential issues quickly, ensuring data accuracy.
  • Minimise time-consuming, error-prone manual processes, reducing the need for deep system knowledge and documentation.

Proactive Data Quality Management

  • Automatically detect inconsistencies, synchronisation issues, and integration gaps that can lead to data quality problems.
  • Prevent disruptions before they occur, ensuring consistent data and maintaining AI model accuracy.

Cost Optimisation


  • Reduce unnecessary integration costs with streamlined processes.
  • Avoid costly inefficiencies and unnoticed errors in data management, preventing operational disruptions caused by inconsistent data.

Disruption Risk Management

  • Pinpoint how and where to resolve issues without impacting operations.
  • Maintain seamless functionality while implementing improvements, addressing integration challenges to ensure data consistency.
With Datagusto, your data remains discoverable, accurate, flexible, and reliable—empowering your business to thrive in the AI era.
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Map

Charting Your Data Landscape
Imagine your data as a vast territory. Our AI processes raw values and SQL queries from each system to create a detailed data catalogue, mapping out the various regions of your data environment.

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Route

Navigating Data Connections
Just like understanding how different locations are connected, we analyse pipeline code and the data dictionary to reveal data lineage. This helps you navigate the paths between your data points and understand how they interact.

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Go!

Travelling with Confidence
As you embark on your data journey, Datagusto ensures a smooth ride. We continuously monitor for inconsistencies, alerting you to changes and maintaining data integrity across your architecture, so you can travel confidently without disruptions.

Use Case

Scenario

Data Quality Challenges for Bank's ML Ecosystem

A financial services company relies on data from an external data source to train and update its ML models. Recently, changes in the data quality went unnoticed, leading to degraded model performance and unreliable decisions.

Here’s how the situation looks from different roles.

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Chief Data Offficer's Perspective

Without Datagusto

The CDO constantly monitors Slack channels, checking updates and status reports from engineers. When data from the external data source changes unexpectedly, issues are only discovered after model performance drops. Even after engineers address the problem, there's lingering uncertainty about data accuracy and potential impacts on executive reports. Recovery from such issues incurs high costs and time.

With Datagusto

The CDO receives instant alerts about changes in data quality directly from Datagusto. The impact on key metrics is clear, enabling quick action to prevent further disruptions. Recovery is faster, with reduced costs and more time for strategic priorities.

ML Engineer's Perspective

Without Datagusto

The ML engineer manually checks each data update from the external data source, a tedious and time-consuming process. Data quality changes are often missed, resulting in degraded model performance. Fixing the issue requires manual validation, delaying projects and creating frustration.

With Datagusto

The ML engineer gets automated alerts when data changes, with insights into what’s different. Inconsistencies are quickly identified and resolved, reducing manual work. Data quality issues are addressed before they impact models, allowing the focus to shift to optimisation.

Data Lifecycle Ingestion Consistency Monitoring Data System Cataloging
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A single source of truth for your entire data lifecycle

Seamlessly integrate and manage data systems with intelligent, automatic target-to-source matching. Gain complete visibility into your data systems. Datagusto ensures consistency across your data lifecycle by understanding all systems and their pipelines.

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Streamline integration, detect changes, and predict impact

Automatically identify and alert data inconsistencies, reducing manual oversight and the need for specialised expertise. Datagusto ensures data consistency across all systems, maintaining integrity and preventing disruptions in your pipelines

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Automated documentation updates

Keep your data system documentation up to date effortlessly, saving time and effort. Datagusto integrates seamlessly with other data management tools, enhancing precision and accuracy in data discovery for your AI systems.

About Us

Datagusto Manga
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Mao Parr

CEO

In 2017, I joined a consulting firm, where I worked on data governance and BI/AI projects. And I was selected as a Tableau Evangelist. Afterwards, I took on the role of business development at an AI start- up.

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Tatsuya Nakamura

CTO

I am a Ph.D. expert in Web Data Mining with a wide range of skills from research to development. I worked as an engineer at Yahoo JAPAN, responsible for designing, developing and operating web systems on the company's cloud infrastructure.

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