The future of all BI tools lies in their capabilities to give the most important features to the users that help in gaining insights. One of those important features is the usage of augmented analytics which is finding its way to the top of the Business Intelligence ladder. In this blog, we will see the important and exciting augmented analytics features available in various BI tools.
Tableau plans to integrate Einstein analytics in a full-fledged swing after announcing that the entire Salesforce Einstein Analytics had been merged with Tableau. Einstein analytics uses various AI technologies to observe and analyze the data to provide predictions and suggestions based on those observations. The availability of features such as Ask Data, Explain Data shows that the industry is going beyond the traditional visualization-based tools. Tableau incorporates technologies like Natural Language Processing (NLP) and Natural Language Generation (NLG) as smart analytics features to give users a better experience analyzing the data. To learn more about smart analytics features in Tableau, check out our blog here.
The availability of advanced augmented analytics features and machine learning capabilities makes Power BI effective in its analytical capabilities. Power BI has incorporated its AI functionality in features like Quick Insights and Q&A visuals which help users to analyze and understand the data quickly. Azure Machine Learning feature, which is available in Power BI Desktop, allows data scientists to develop machine learning models that analysts can use. Various other features such as text analytics and vision analytics pave the way for users to use the augmented analytics capabilities in their data analysis effectively.
SAP Analytics Cloud
SAP Analytics Cloud has extended its capabilities using augmented analytics features that help users gain real insights and respond to data more accurately. Features such as Search to Insight use Natural Language Querying, which generates visuals based on the questions asked by the user. SAP Analytics Cloud provides features such as Smart Insights, Smart Discovery, Smart Grouping that incorporate the power of machine learning and augmented analytics to equip the users with cutting-edge capabilities of analyzing the data. The Smart Predict feature helps SAC users to build predictive models based on their historical data, which helps in optimizing strategic decisions. Options such as Time-series Forecasting, Predictive Forecasting also allow users to arrive at efficient business decisions based on the forecasted data.
Qlik offers a high-performance Associative Engine that offers its users with any skill level to search and explore all data sets. Qlik supports data exploration using its augmented analytics feature, Insight Advisor, which auto-generates insights by analyzing the data, automates and accelerates the data preparation process, and supports natural language interactions. The hidden insights are displayed as impactful visuals by its Search-based Visual Analysis, which can be edited and adjusted to create efficient dashboards. Conversational, Natural Language Analytics uses Natural Language Processing (NLP) which lets users analyze data with conversational experience. This is intended to fill the gap between natural language and visual analysis. Users can quickly create, modify and personalize data using Qlik’s Accelerated Creation and Data Preparation. The Real-time Advanced Analytics capabilities put the power of analyzing the data to search for insights in the hands of all users irrespective of the level of skills which makes it easier and useful for users with no analytics experience in real-time.
From the list of features offered by each tool, it is evident that augmented analytics has become a significant part of every BI tool as the field of analytics is snowballing due to the generation of a massive volume of data.
To know more about the analytics capabilities of BI tools, check us out here.