What is Augmented analytics and why is it important

Posted by KPISOFT on Aug 12, 2019 9:35:22 AM

With the expedient growth of the term Augmented Analytics being used and discussed, the first question that comes to mind is simply “What IS Augmented Analytics?” and why is it so important? Gartner included Augmented Analytics in their annually published “hype cycle” graph, and announced it as the annual top strategic technology trends at the Gartner Symposium/ITxpo.

So what is it? Gartner describes Augmented Analytics as “an approach that automates insights using machine learning and natural-language generation” which marks the next wave of disruption in the data and analytics market. In this article, we will uncover the key findings of Augmented Analytics, and the reasons why every enterprise who plans to venture into this revolutionizing new wave of IT and business, should adopt Augmented Analytics. 

augmented analytics


  1. Augmented Analytics and Augmented Data Discovery goes through all the data, detecting patterns, correlations, and outliers; presenting only what is relevant. Natural Language Generation (NLG), a software systems that automatically generates narratives and reports, in easy-to-read language, which describes, summarizes and explains input data to any user. All these tools helps collaborate, analyse, simply and present data to those who would benefit off it, making everyone in a business a CDS. This can reduce the consumption of exploration and the identification of false or less relevant insights                                                                                                               
  2. These insights can be shared with a vast range of business users, citizen data scientists (CDS), and operational workers. The idea of CDS is to allow everyday working people within an organization to perform analytic tasks that would previously have required the expertise of a highly skilled data scientist through the advancements of tools and technology. According to strategic planning assumptions, by 2020 there will be more CDS than regular DS in terms of how many advanced analysis they produce.                                                                                                                                
  3. With CDS becoming more independent and authoritative with their own decisions, Augmented Analytics will enable expert data scientists to focus on their specialized problems, and on embedding enterprise grade models into applications. With the reduction of this bottleneck system deficiency, CDS will no longer spend extensive time exploring data, as they can now instantly act on the most relevant insights that are prepared and presented to them.                                                                                                                                                                           
  4. Instead of just presenting insights, Augmented Analytics also presents options, that tackle issues like uncertainties, risk management, and problem solving. Machine Learning (ML) and Artificial Intelligence (AI) algorithms can predict a full probability distribution, permitting all CDS to assess every possible options for the future, along with its specific probability. This gives every user, whether from the floor or the C-suite, a chance to fully explore the benefits and impacts of all operational decisions with scientific precision.                                                                                                          
  5. ML algorithms use something called “gradient descent,” a system that gradually improves a solution by reducing its error. A type of problem it can solve can be Financial analysis. ML will enable continual assessments of data for detection and analysis of anomalies and nuances to improve the precision of models and rules.                                                                                                                                                                                                      
  6. Instead of an analyst manually testing all of the combinations of data, Augmented Analytics can automatically apply algorithms for detecting correlations, segments, clusters, outliers and relationships to data. With this, companies not only reduce human assumption and bias, but avoid the risk of missing insights and detrementing accuracy.

With just these 6 features, your enterprise can increase its efficiency monumentally, enhance decisions through scientific precision, track the KPIs of every individual, and realign your business to reach your goals in faster and greater ways. This is only the tip of the iceberg with what AA has to offer, to further explore how Augmented Analytics is the future of Data and Analytics, download the Gartner Report Below.

augmented analytics

Topics: augmented analytics, machine learning, kpi dashboard

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