Augmented Data Discovery

What exactly qualifies a vendor to be included in the Magic Quadrant? Augmented Data discovery. According to the latest Gartner Report, By 2020, the number of users of modern business intelligence and analytics platforms that are differentiated by augmented data discovery capabilities will grow at twice the rate — and deliver twice the business value — of those that are not.

Gartner highlights augmented analytics as a strategic planning topic, a paradigm that includes natural language query and narration, augmented data preparation, automated advanced analytics, and visual-based data discovery capabilities. These features will be commonplace in the space during the next several-year period, so much so that they will be included in 90 percent of available products.

So if you are still uncertain of the factors of Augmented Data Discovery, it’s crucial you understand why startups and some large vendors are beginning to offer a range of augmented data discovery capabilities which can not only potentially disrupt the BI current visual-based data discovery vendors in the long run, but force leaders to re-evaluate investments.

Augmented data discovery includes strategic factors (vendor viability, global presence, support and pricing) and functionality factors. In terms of functionality, there are 5 distinct functionality factors.

 5 functionality Factors Augmented Data Discovery offers.

  • Data access and preparation:

Differentiating itself from current BI and analytics platform through the scope of data it is capable of analyzing, Augmented Data Discovery profiles data and makes intelligent recommendations. These recommendations can include how to cleanse data or combine data-sets

Eg. When analyzing traffic fatalities, it may make sense to combine data about these fatalities with public data about population density.

  • Algorithms, transparency and interoperability:

Augmented Data Discovery allows additional algorithms to be added to the out-of-the-box libraries, refining algorithms by using data science languages such as Python, R and Scala

  • Interactivity and Narration of Insights:

With Augmented Data Discovery, users are able to interact with the system through conversational analytics, enabling a routine inquiries met by instant answers.Relevant results will be narrated by text or voice, with NLG establishing understanding in every user. Augmented Data Discovery secures ease with configuration of language tone, verbosity and domain or vertical-specific ontologies, APIs and native integrations, and languages and types of algorithms supported within the narration.

  • Presentation of Insights:

In addition to the charts, data manipulation, interactivity and other features necessary for visual presentation found in Modern BI and Analytics Platforms, Augmented Data Discovery automatically generates the most statistically relevant insights. The software can assess how a product supports automated forecasts, correlations, factor analysis, decision trees and more.

  • Deployment Options

A large percentage of data advancements in NLG and machine learning are cloud-only options, or cloud-first options. Examples of cloud-only options include IBM Watson Analytics, SAP Analytics Cloud and Sales-force Einstein. DataRobot however offers cloud and on-premises deployment options, as do Sisense and Thoughtspot.

An enterprise sustaining through the constantly evolving business analytics can be as expensive and unpredictable as economic and political changes to the business world. Why is this? Because the way the IT world develops, alternately remolds the expectations of how an enterprise should function, and the ways in which companies can grow. Stagnant companies who aren’t growing simultaneously as the IT world will find it difficult to attract their market or potential investors in ways vendors in the Magic Quadrant of Business Intelligence and Augmented Analytics would.

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