From the IT-led, semantic-layer-based approach of traditional Business Intelligence (BI) tools to today’s easy-to-use visual data discovery tools, BI and Analytics have come a long way in the past 10 years. These tools have significantly transformed the business scene and changed the way organizations function. However, business users have realized that even though these visualizations are a powerful way to consume insights, they still have to struggle to find the most significant insights in their immediate business context.

Let’s say for example, the data shows a decrease in revenues. What does it mean for the business? Is it an anomaly or a pattern? What are the underlying causes? What should you do to tackle the situation?

In the current context, your data scientist probably helps you model the data and generate insights, after which your executives further interpret results in the business context to identify actionable insights.

The next big BI revolution powered by Augmented Analytics will be a faster, more accurate and automated way to gather actionable insights. Gartner introduced this concept in the 2017 hype cycle and said it was the “future of data analytics”. According to Gartner, an approach that automates insights generation and access using machine-learning and natural language generation will mark the next wave of disruption in the data and analytics market.

The concept of Augmented Analytics is based on three main pillars – Machine Learning, Natural Language Processing and Automated Insights.

As Gartner sees it, machine-learning automation will be used to augment data profiling and data quality, harmonization, modeling, manipulation, enrichment, metadata development, and cataloging.

Machine learning-based automation of insights generation will enable business users and citizen data-scientists to expedite their decision making process using real-time actionable insights without having to build models and write algorithms. This advanced use, manipulation, and presentation of data simplifies data to present clear results and provides access to sophisticated tools so business users can make day-to-day decisions with confidence. Users can go beyond opinion and bias to get real insight and act on data quickly and accurately.

In addition to this, Natural Language query technologies will further add to the digital dexterity of your workplace with the ability to generate personalized insights using natural conversational methods like chat or voice. In other words, business users can phrase questions in the same way they would ask a colleague!

The combination of machine learning and NLG will allow businesses to automate the labor-intensive process of analyzing data and communicating important findings to business users. In effect, an augmented analytics engine can automatically go through a company’s data, clean it, analyze it, and convert resulting insights into action steps for executives or marketers with little to no supervision from a technical person.

Augmented Analytics and Conversational Analytics are emerging paradigms with several leading analytics vendors are working in these directions. Course5 Discovery is one such ’Personal Insights Analyst-on-Demand’ catering to the real-time needs of Analysts, Influencers, and Decision Makers by facilitating them with actionable insights around their customers, channels, content, categories and regions in a personalized manner.

Deploying augmented analytics solutions will allow the data-scientists of your organization to –

  • Focus on strategic issues and special projects. Will help you create Citizen Data Scientists (people who can create models that use predictive/prescriptive analytics without having advanced training in analytics or statistics), improve accountability, and empower them.
  • These solutions produce better decisions, more accurate business predictions and measurable analysis of product and service offerings, pricing, financials, production, etc. which, in turn, can positively impact your ROI and TCO.
  • Augmented data preparation and related tools will improve user adoption, data popularity, social BI integration and data literacy.

Moreover, augmented analytics will move us closer than ever to the vision of “democratized analytics” because it will be cheaper, easier, and better. At last, we will see more and more businesses of all sizes using and benefiting from analytics in the years to come.

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