A term coined by Gartner, Augmented analytics is used to describe the integration of natural language generation, text mining, natural language processing, and automated data processing capabilities into Business Intelligence (BI) systems. Augment Analytics has been on the wish list of IT vendors as they are on a rush to build or buy these capabilities into their BI platforms to make it easier for enterprise customers to democratize analysis.

The next fundamental shift into analytics evolution is here. Similar to the tech wave where self-service business intelligence disrupted the first wave of traditional BI, augmented analytics technologies will prove to be a game changer once again.

Augmented Analytics has the power to automate data science steps and machine language involved in an advanced analytics process giving business people unprecedented freedom to guide machines in terms of right insights in right business context while validating machine or the human mind generated hypothesis along the way.

Early adopters of augmented analytics will be top on the competition ladder leveraging the speed to insight and enhanced competitive advantage.

Business Intelligence and Artificial Intelligence

Augmented analytics uses machine learning and automation to supplement human intelligence in the entire analytics life-cycle process. The next generation augmented analytics capabilities are powered to prepare and cleanse data, find key insights, hidden patterns and perform feature engineering.

Augmented Analytics Tools and BI

Augmented analytics uses machine learning to automate data preparation, discover insights and share insights for business users, operational workers, and citizen data scientists. Some of the key capabilities of augmented analytics tools include augmented data discovery, augmented data science and machine learning, and augmented data preparation. Augmented data preparation focuses on automating the ingestion of data into analytics systems in a process which includes, modeling data, adding metadata, data profiling, ensuring data quality and storing in catalogues.

Augmented data discovery assists users to find relevant data by automating, visualizing and narrating relevant findings. Machine learning helps to reduce skill-gap required to build models to test out new hypotheses or write algorithms. Startups and large vendors have the potential to disrupt data integration, leading BI and analytics, data science and embedded data analytics vendors.

It's All About Augmented Analytics Tools

An analytics manager must consider the support for the following five augmented analytics capabilities in their interaction with the top BI vendors:

•  Natural language processing:BI users should be able to use the power of technology language text-based and/or voice-enabled in a bid to interact with the data in a conversational mode.

•  Natural language generation:BI tools with augmented analytics capabilities should be able to narrate performance results in an interactive way leaving complexities behind.

•  Recommendation:The system should recommend what is the best visual for specific data, how to enrich data for deeper analysis and understanding, and how to clean and prepare data for business use.

•  Insight generation:Augmented analytics tools should be able to bring their own ideas (biases) to the process to seek out results to support their hypotheses. It is important for these algorithms to describe the data, identify key drivers, and explain what segments influence the outcomes. The tools must identify the outliers behaving differently than the anticipated results.

•  Prediction:Augmented analytics tools should be able to forecast and predict a trend, identify cluster groups and outliers like values with the click of a button. This process additionally includes using algorithms to train predictive models made on churn, attrition or customer behavior.

BI and Augmented Analytics Pilot Projects

As analytics reaches a new dimension, Analytics and BI leaders should begin planning for augmented analytics pilot projects and adoption. Augmented Analytics complements rather than replaces existing enterprise BI, data science platforms and self-service BI. Augmented analytics can additionally be embedded into a different line of business applications to improve decision-making processes.

Augmented analytics is bound to transform the manner business intelligence processes are undertaken completely in the coming years. Augmented analytics has transformed the entire analytics workflow and the way data analysts access data and work on insights. The mainstream adoption of augmented analytics is not too far from reality for all business enterprises. Thus, modern business intelligence analytics which works with automated insights, automated data preparation, data science platforms will be embedded in the conversational analytics and enterprise applications in the future. This trend is bound to reach beyond data scientists and technical specialists and thus transform businesses significantly.