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How Can Data Science as a Service Help Your Organization

Among the most popular Data Science solutions are recommendation and banking scoring services, as well as, for example, systems for intelligent selection of apartments, taking into account the places of work and study of all family members. There are online projects where neural networks generate non-existent human faces based on multiple photographs or write coherent and meaningful texts.

5 business areas where DaaS (Data as a service) is rather useful:

  • benchmarking analysis, when the automatic collection and global analytics of big data allows you to compare the performance indicators of your company with competitors and industry peers;
  • a single version of data for third-party services, when the DaaS platform acts as a single secure data store with search capabilities, for example, logistics applications can use public data to integrate supplier information or datasets with geographic locations.
  • a marketplace (marketplace) for configuring data between clients, including inter-corporate data exchange or Big Data analysis results;
  • billing (billing), where data exchange between applications or API calls must be commercially assessed and charged accordingly;
  • optimization by data science as a service of near real-time performance, from logging statistics to analyzing and adjusting them at runtime. For example, the dynamic ordering of report filters as it is built based on calculated field statistics or transaction execution logic that can be optimized for each task.

How it helps in business

The benefits of using data science in business are direct, both for the entrepreneur and for the client. The user is constantly comparing products and making decisions, for example, choosing products in a supermarket or a movie in an online cinema. This process is known as “shadow work” and algorithms can help the user make choices.

For example, Netflix, YouTube, Amazon and others are already using smart recommender systems. Netflix analyzes the behaviour of its users and offers everyone a personalized selection of content based on their past preferences. YouTube creates personalized recommendations for users based on views, likes and dislikes, and many more parameters. Google and Yandex show targeted ads based on where the user goes and what they buy. And the American retailer Target analyzes the history of purchases and changes in the behaviour of shoppers, sending them individual coupons as if anticipating their wishes. In other words, there are many successful examples of the application of artificial intelligence algorithms in business.

In conclusion, we emphasize that ready-made BI and DaaS platforms will in no way replace full-fledged Data Science solutions that are needed by data-driven businesses with a high degree of management and digital maturity.