What is Augmented Analytics & benefits for digital companies is significant

augmented analytics

The term of increased analytics arose as a result of the annual Hype Cycle report of the market research company Gartner, in 2017. Since then, in the field of Business Intelligence it has not stopped talking about it, however, it has not transcended too much, why? In this post I want to tell you what augmented analytics is, why they call it “the future of data analysis”, what advantages it has, and why you should start using it. Are you interested Go for it.

Augmented Analytics , or Augmented Analytics , is defined by Gartner in his Magic Quadrant for Analytics and Business Intelligence Platforms document as “ a paradigm that includes natural and narrative language queries, increased data preparation, automated advanced analysis and visual data discovery capabilities ” . In that same document, they influence the importance of Augmented Analytics in 2020, acting as a driver of Business Intelligence , Data Science and Machine Learning .

Perhaps, all this may sound like Chinese … or not. In both cases, I can only tell you that augmented analytics does not bring anything new. What is really innovative is the twist that it gives in terms of extracting knowledge from the different data sources that a business owns. This twist is supported under these 3 pillars:

  • Artificial Intelligence (AI). I recommend that you take a look at the post I wrote about “ How to do Customer Intelligence with Artificial Intelligence ” so you can have a wider view of its use today in Business Intelligence.
  • Machine learning That is, a method of data analysis that automates the construction of analytical models. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
  • Natural Language Processing (NLP). That is, a field of knowledge of Artificial Intelligence which is responsible for investigating the way of communicating machines with people from natural languages, such as Spanish, English or French.

The real importance of this approach is that the combination of these 3 branches of analytics makes it possible to extract information automatically. But best of all, it can be done without the need for great technical knowledge. The introduction of the NPL, makes any query as simple as asking Siri or Alexa what the weather will be tomorrow.

Until now, there is a wide variety of systems that allow data collection, normalization and analysis. The big difference is that until this moment, only the big companies or those specialized in the data, had the necessary personnel and tools.

This type of analytics will therefore imply the introduction to advanced analytics for companies that cannot have a complete team of data scientists today, due to the high cost and the difficulty of finding suitable professionals. Medium-sized companies that cannot afford tailored solutions and developments, as well as large companies with the need to value their data in record time, will obtain a perfect partner in increased analytics.

Also read Trends: Digital Twin Technology – advantages

Therefore, with the Augmented Analytics, the democratization of the data would be achieved, but above all, the democratization of the conversion of the knowledge offered by the data, in insights, something highly valued by any business and marketing department of any company.

At the moment, the best known tool and the one that is working more in augmented analytics, is IBM Watson Analytics , although there are already others working in the same direction. Tableau Insights and Qlik Sense are other tools that are betting on it. However, it is expected that as Artificial Intelligence advances there will be more players in the platform market.

The importance of these benefits for digital companies is significant.

It is well understood that for organizations to compete in the digital age, data is key to obtaining relevant and actionable information.

However, the underlying complexities inherent in manual analysis processes create many obstacles.

On the one hand, data that is trained in the practice of data science and also has a good understanding of business models and operations are rare.

In addition, the data is valuable; however, a large percentage of time is spent on manual data preparation through cleaning and labeling. This shortage of time and capacity means that most data analyzes are performed on a small part of the data, while a large part of the data assets are not undermined.

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