As the field of analytics is growing immensely, it becomes important to provide more sophisticated analysis feature to the customers who are looking forward to using Business analytics extensively for their business. Growth in self-service capabilities is extended to data preparation and predictive analytics. Despite the evolving features in most self-service tools, help is required for non-technical business users to analyse their data. This is a gap is filled in using Machine Learning and Artificial Intelligence.
Tableau incorporates techniques to provide smart analytics which includes various features related to data preparation capabilities and visualizations.
The data involved in the analysis tracks down to various vital decisions taken within the organisation after analysis. Being a self-service tool, it is essential that there are intelligent capabilities which the users can use although they are not trained in it. The implementation of smart analytics features makes analytics accessible and easy to use for non-technical business users and supports advanced users to ask more sophisticated questions for better analysis.
Few smart analytics capabilities of Tableau are discussed below.
Natural Language Interaction
The concept of natural language processing in analytics will take the self-service features to the next level of analysis. From creating visualizations that can answer the questions asked in a simple language to displaying descriptions for any selected visuals using Narrative Science and Automated Insights can help the customer get a clearer and deeper idea of the data involved in the analysis.
Smart Data Prep
Though the self-service feature is provided by tools, it required technical knowledge to prepare the data to perform the analysis. Tableau has made self-service data prep more effective to be even used by non-technical business users. This paved the way for easier modelling, cleaning etc of the data. Data interpreter makes the formatting of data easier which leads to faster analysis. Grouping and matching of data are made simpler using Fuzzy matching which reduces the manual clean-up process. Effective algorithms are deployed to achieve the fuzzy matching concept. Recommendations are made by Tableau for joins and tables using data source metrics using machine learning.
Automatic Data Discovery
Clusters can be created using a feature called Clustering in Tableau, a built-in k-means clustering model to identify and group the clusters.
Predicting your business is made easier using a forecasting model which is a drag and drop feature provided by Tableau which generates a forecast using exponential smoothing on the data used for visualization.
The models built in the tableau can be leveraged using Statistical Model Integration where we can run codes inside tableau, visualize and deploy the models from the predictive services offered like TabPy and Rserve as it supports direct integration with R and Python.
Smart analytics feature by Tableau leverages the limit of self-service analytics to a greater extent that makes it easier for the users to gain insights and analyse the data efficiently. From initial data to visualizations Tableau is working on bringing the best of the techniques to incorporate smart analytics. Reach out to us to expand your analysis in the business and gain insights.
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