CIODigital Disruption

How Modern BI Platforms Enable Self-Empowerment?

With the ever-increasing digitization, self-empowerment has become the norm of the day. People are more inclined to generate business-specific insights on their own with the help of leading analytics and intelligence tools. A recent survey of Gartner suggests that the next step in this direction is augmented analytics. In this article at Information Week, Jessica Davis talks about the role that business intelligence platforms play in delivering self-driven insights. 

The Much-Needed Change

Most of the organizations still believe that business insights are more reliable when worked upon by data scientists. However, this perception is changing with time. Reason being the user-friendly features offered by the latest BI tools that can enable the end users to exercise their analytical skills and interpret the business insights to the best of their ability.

What’s Next?

The business research firm, Gartner has been studying this trend since long. Gartner states that business-driven analytics and implementation of modern intelligence platforms are now more conventional and rapidly evolving. The investments for outdated BI have decreased drastically. So, according to ‘2018 Magic Quadrant Report for Analytics and BI Platforms’, the next big thing that can define the future of BI at an enterprise level is ‘augmented analytics’.

Moreover, Gartner enlists a range of expectations from these modern BI platforms that could bring major enhancements in the field of analytics by 2020:

  • The number of end users capable of using augmented analytics will grow at a double rate and contribute double the business value than others.
  • About 90 percent of the modern BI tools will comprise natural-language generation and artificial intelligence as a typical feature.
  • About half of the analytical queries will be produced through voice or manual search, natural language processing or automation.
  • The growth rate of the self-driven data analysts or employee will be five times quicker than the expert scientists.

Thus, instead of paying huge money to expert data scientists, organizations can spend that amount in empowering their existing set of professionals. To read the original article in full, visit the following link:

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