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Data Intelligence as a Service - DIaaS | Vinod Sharma's Blog

Last updated: 03-15-2019

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Data Intelligence as a Service - DIaaS | Vinod Sharma's Blog

We all know that AI and Machine Learning has huge potential to unlock Big Data’s hidden values. In many instances, AI is an immediate solution for the volumes and velocities for which big data is known for. Artificial Intelligence and Machine Learning is emblematic of so much of big data’s promise, combining the speed and size of those technologies alongside alternative cloud paradigms and the evolution of mobile applications. Additionally, there are related fields of Artificial Intelligence that study intelligent methods that also learn from data and their environment. Examples include computational intelligence and mateheuristics.

Data science will be one of the primary expressions of big data in the subsequent decade, as its emergence should become much more apparent in the coming 12 months largely due to the maturing influence of AI and the data intelligence techniques itself. The year 2018 will see additional organizations experimenting with ways those capabilities render big data less daunting and perhaps even more enjoyable. In coming times data intelligence service be most eminent application that would be provisioning a prototypes for security measures to truly fortify the DIaaS.

There are certain market dynamics which determine the growth of the data and its related analytics.  Thats where Data Intelligence’s adaptive dynamics comes into play to assess the factors driving organisation to adapt their existing, profitable lines of business. This help them stay relevant in the future of the rapidly evolving world and enormous helper for Blue Ocean shift strategy. Artificial Intelligence Needs a Strong Data Foundation which help to transform data Into Insights. Transformation of data in to Intelligence is the highest stage that many service providing organizations ever reach in the data pyramid. Data is collected from many sources, cleansed, and turned into valuable reports that are used internally to measure performance.

Driving factors include high demand for automations of analytics services, demand for data-driven decision marketing, and an increase in productivity and revenue. Data intelligence  is used to explore the external business environment (i.e. industries, markets and other competitive factors). Applicable to any business, data intelligence, if carried out correctly, can yield a significant return on investment either through increased revenues or by simply avoiding a bad investment decision. Analytics models should be supporting enough to prescriptive and predictive outcomes to avoid chaotic situations as mentioned earlier.

Opportunities include high demand for predictive analytics across verticals. Factors such as lack of awareness among industry professionals and so called data scientist (no I am not sarcastic here). Have You Put Your Data to Work? Through the actionable analytics of data intelligence, you can discover the smartest business solutions for your data enterprise strategy. This should not be confused with market research, which typically focuses on customers and customer preferences only. Today, many large budgets are aimed at efforts around collecting and harnessing the rapidly expanding volumes, rapid velocity, varied forms, and uncertainty of historical data.


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