Data Driven Architecture

What is Data Driven Architecture

Data is the fuel for today’s digital economy.

For organisations striving to be data-driven, formulating a comprehensive data-centric strategy is the cornerstone for empowering stakeholders with consistent and accurate information that fosters transparency with customers and partners; thereby building a value-ecosystem that benefits both them and their customers.

We provide direction & strategy to organisations to manifest foundational data-driven architectures that can adapt to the dynamic data landscape and evolve seamlessly over time.

We help organisations transform legacy data landscape to fit-for-purpose data ecosystem that can adapt to modern data needs.

Benefits of Being Data Driven Architecture

A data driven culture supported by the right combination of technology and processes enables organisations to gain and maintain competitive edge in the following manner.

  • Agile Decisioning: Respond effectively to events before context decay
  • Empowering Democratization: Focusses on data discoverability to empower various stakeholders
  • Driving Innovation: Experiment with data on-demand regardless of size and form

Why Datalogy?

Digital Transformation

Expert Skillsets

Industry Domain Expertise

Timely & Quality Delivery

Solution Accelerators

Blogs

Data-Driven Superlative Customer Experience

Consumers are increasingly seeking seamless digital experiences, particularly given that many of our daily needs can be addressed with a single click. As a result, many individuals have become accustomed to using digital, from purchasing goods online to making payments using banking apps. However, when it comes to buying insurance,

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Top 9 Ways AI Is Transforming the Retail Industry

With large-scale digitalisation, it’s safe to say that AI is becoming an indispensable part of our everyday life. A prominent example of the same could be, recommendations. If you are into buying e-books on Amazon, you’ll notice you get suggestions based on your recent purchases or you might’ve noticed how

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6 Steps that are Key to Data Preparation in Machine Learning

Typically, raw data cannot be used directly in predictive modelling projects like classification or regression. For algorithms to function, data must be in numbers, and statistical noise and errors must be corrected, while some algorithms impose requirements on data. Raw data, clearly, is prohibited by all means. As a result,

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