Analytics-Driven Applications: the magic formula for digital businesses

Why Data Science Doesn't Have to be Rocket Science


In the “application economy”, there is a growing interest among companies from banking to retail to media, to make their consumer-facing applications “analytics-driven”.

Examples of Applied Analytics

Many businesses are hard at work exploring their customers’ digital interactions to understand how to optimize the user experience and increase satisfaction.

A few examples of common use cases:

  • Improving online stores to give customers easy access to personalized content and support.
  • Enriching mobile experiences by predicting the customer journey and search patterns.
  • Proactively identifying network issues; provisioning capacity and applying fixes without the need for human involvement.


..and that’s just to name a few!

The Problem with Doing it Yourself

These trends all demonstrate the value of advanced analytics. But, how do you apply it? Implementing analytics manually requires information workers – digital technicians capable of programming analytics-driven applications by creating and training algorithms which analyze incoming data and predict future outcomes of current events. There is a growing need for these “data scientists”. Unfortunately it might be a while until the supply side catches up. The time and resources it takes to hire and train a data scientist are frequently mentioned by both customers and industry analysts as a high barrier for adoption of advanced analytics. So, what do you do?

Analytics-Driven Applications at CA Technologies

At CA Technologies, we are working with a number of partners to create analytics-driven application prototypes for hundreds of industry-specific use cases. The data science generated by these applications is refined and enhanced over each iteration. This investment is the first step towards what we believe the future of advanced analytics will be. Rather than companies hiring an army of data scientists to tackle their individual needs, the application economy will likely turn to pre-packaged data science solutions.

A Look Ahead

Solutions providers with access to multiple data streams from various applications and analytics engines capable of handling hundreds of terabytes of data hold the competitive advantage in this technological landscape. That is because they have the infrastructure to properly train their algorithms, and the horsepower to generative significant machine learning gains.

These companies will likely be the ones to bridge the data science talent gap, and provide the market with ready-to-use, pluggable data science. Industry-specific data science packages accompanied with consulting services would be an attractive proposition for digital businesses who want to spend their time delivering value to their customers, instead of reinventing the advanced analytics wheel.


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Natasha Festa
Natasha Festa is a Sr. Principal Product Manager at CA Technologies, working on CA Jarvis,…


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