• Chesapeake Group

Augmented Analytics for increased productivity and competitiveness


Introduction:

Enterprises have been increasingly adopting the use of data analytics and business intelligence to make business decisions. While these techniques help businesses make better decisions, the whole process of deriving and communicating the insights can be cumbersome. A lot of times these reports cease to be of any use by the time they are generated by the data analyst and sent to the requesting user. Moreover, the insights derived are static as they are based only on pre-defined parameters which may limit the explanatory power of the model.

The term “Augmented Analytics” was introduced by Gartner and called it “Future of Data and Analytics”.


What is Augmented Analytics and how it helps:

Augmented analytics automates the process of data preparation, derivation of insights and communicating the same to the users. Instead of asking a data scientist to prepare a report that to answer a mundane question, users can simply ask a question and get insights in real-time. This empowers users with limited knowledge of data analytics to make quicker decisions and become more productive. It also allows the data experts to work on more complex projects and add more value to their organizations.

Augmented analytics leverages ML and AI models in their solutions. This enables such solutions to provide insights that are dynamic i.e. based on the available parameters, user’s job function et al and models that are statistically significant. Hence, organizations that use augmented analytics instead of static dashboards and reports will be able to make better decisions and have a competitive advantage over their peers.


Conclusion:

As enterprises become more and more digital, they produce more data and different types of data. In such a scenario, it is becoming increasingly difficult to identify parameters that might explain a business outcome. This makes augmented analytics indispensable going forward.

Moreover, by 2020, augmented analytics will be a key driver of new purchases of analytics and BI solutions. This is because, by 2020, 50% of analytical queries will be generated via search, natural language processing (NLP) or voice. Data and analytics leaders, hence, should plan to adopt augmented analytics as platform capabilities mature.

While organizations should consider having augmented analytics as part of their business intelligence strategy, they should ensure that data scientists and experts are part of the decision-making process as they will be able to best validate the efficacy of the product.

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