Application Modernization

What is Application Modernization

We are already seeing Technology-driven transformation occurring at a massive-scale across industries which will continue to grow in the coming years. To keep up with the changing dynamics of customer behaviour, shifting engagement channels and growing scale of data, Organizations need to embrace new technologies in order to maintain competitive edge.

We provide Strategy Consulting and Implementation Services to help Organizations reimagine & reinvent the data & application systems landscape to further enhance business value. 

Benefits of Being Application Modernization

  • Customer Centric: Enable more contextual interactions with customers on devices and channels of their preference
  • Increased Flexibility: Leverage DevOps for faster & frequent deployment of new features and functionality
  • Greater ROI: Increase Customer Engagement leading to better financial outcomes

Analytics Platform Modernization

Rebuild existing Analytics, AI & ML models in legacy platforms and migrate them into new-age analytical environments including OOTB OEM platforms and Open Source Technologies.

Campaign Management Modernization

Modernize existing legacy campaign management platforms to modern ecosystems with focus on consistent, contextual and timelier engagement on digital channels across devices

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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