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Policy-based query governance

Organisations face significant regulatory and compliance risks from their data management and analytics practices. With the proliferation of data and the significant business opportunities centring around data analytics and AI, real-time policy-based governance of data, operations and development is essential.

January 31, 2020

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Hybrid Cloud Data Warehouse

December 6, 2019

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Are you building a dumb robot?

May 21, 2019

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Making complex queries

June 13, 2019

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Being in the moment with your customer

May 26, 2017

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Blog

Blog
Policy-based query governance

Organisations face significant regulatory and compliance risks from their data management and analytics practices. With the proliferation of data and the significant business opportunities centring around data analytics and AI, real-time policy-based governance of data, operations and development is essential.

Vinay Samuel | CEO, Zetaris
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Blog
Hybrid Cloud Data Warehouse

Scattered and disparate data environments are the norm for most enterprise BI architectures. Customers seeking to move to a modern cloud data warehouse platform (like Snowflake) experience major difficulties due to business logic complexity, transformation logic handling, lack of automated exception processing and other data structuring challenges. Zetaris simplifies this by providing a single location for connecting, modelling and querying all the data. Our Intelligent Semantic Engine and Business Data Fabric builder enables the organisation and preparation of data in different places whilst supporting complex query workloads in real-time. In turn, this makes migration to Snowflake a non-disruptive business agenda.

Vinay Samuel | CEO, Zetaris
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Making complex queries across external databases run is why you need a Data Fabric.

A virtual data warehouse enables a join of data across many data stores and networks or clouds for creating the views that the tools need. This is a step-change in the data platform and integration world where the old approach (build a data warehouse or data lake) means data has to be moved, re-structured, or transformed and ordered before any value can be created. In the traditional data lake or data warehouse approach (the centralized model), data quality issues are created by mistakes being made during the costly physical data integration project. Zetaris has solved these massive cost inefficiencies and technical barriers to analytics at scale. Zetaris changes everything! We don't move or duplicate data for analysis. We implement a virtual data warehouse.

Vinay Samuel | CEO, Zetaris
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Are you building a dumb robot?

Artificial-intelligence presents an exciting business opportunity far beyond simple business process automation. However, organisations who have not addressed their data quality challenges, who are not embracing a real-time business rules reconciliation approach to data architecture are in danger of creating the worst outcomes its business could imagine.

Vinay Samuel | CEO, Zetaris
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Being in the moment with your customer

Over the last few years, Zetaris has been helping clients get closer to their customers by building deep analytics applications embedded in customer interaction technologies. Through several learning cycles we've distilled what works and what is problematic when trying to influence customer behaviour through permission based interaction in real-time. In this article, I've tried to offer these learnings and insights into how In-context Real-time Interaction may be brought to life.

Vinay Samuel | CEO, Zetaris
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Big Data duplication - don't do it

Why do data analytics and big data projects create massive unnecessary cost overheads in the form of data duplication, unnecessary transformation and data lake proliferation?

Vinay Samuel | CEO, Zetaris
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