Tableau being a highly self-service tool makes use of emerging technologies to provide its users with cutting edge capabilities to perform sophisticated data analysis. In addition to the existing analytical richness of Tableau platform, it also provides various smart analytics capabilities for better usage with respect to data and visualizations. Smart data prep is one of the smart analytics features to enhance competencies with respect to data preparation.
Have you constantly speculated how emerging technologies like Artificial Intelligence, Machine Learning etc can impact your business decisions when incorporated for analytics purposes? To achieve this breakthrough, Tableau has fused Machine learning capabilities thus making the self-service data preparation features more automatic to ease the endeavours involved in the process of data analysis. This minimizes the manual efforts required to combine, profile, and clean the data which facilitates arriving at decisions from the data more rapidly. There are 3 concepts when it comes to smart data prep and they are as below:
- Data interpreter
- Fuzzy matching
- Smart recommendations
Data interpreter is a straightforward approach that aids in arriving at a faster analysis which automatically detects the sub tables which enables users to pivot and split data from the added data source. It identifies the structure of the data, the titles, footers, values and so on and parses to convert them into a proper format for improved analysis. During the data interpretation tableau never makes changes to the underlying data source rather just cleanses and modifies the data loaded.
Enabling the highlighted option ‘Data Interpreter’ in the data source page displayed after loading the data can interpret and cleanse the data. Tableau also provides an option to review your results after cleansing by the Interpreter. The results can be viewed as an excel where the keys are mentioned for us to understand how our data source has been interpreted. The data, which is construed as columns, data values, values of merged cells, and excluded values are highlighted appropriately in the result sheets. It also cites if a value is a header or data value as a separate field.
This data interpreter works based on the decision tree built internally for the purpose of determining which data is to be included and excluded. Tableau will also identify sub-tables available in the data if any which can be used just like other data in your workbooks.
There may be instances during data prep such that few fields require clean-up to be used for better analysis. Fuzzy matching, a method that has the ability to process word-based matching, indexes, and groups the values that are related by pronunciations or common characters in Tableau Prep. This is advantageous during data prep as it eases manual work during data cleansing.
Click on the icon at the right corner of the field name where it displays an option ‘Group and Replace’ once you add a step in the flow in Tableau Prep.
Tableau groups the values based on the following which are listed on clicking the ‘group and replace’ option as below:
- Manual selection
- Common Characters
The values after being grouped based on the requirements are denoted by a ‘clip’ icon.
Tableau employs efficient and powerful algorithms and machine learning capabilities to achieve the fuzzy matching feature.
Tableau mines the existing data connection patterns and recommends data sources and joins based on the usage of your organization which is achieved using machine learning capabilities employed in the background. Also, dimensions of data are recommended based on user’s past personal data consumption patterns. It takes into consideration the usage metrics of other users when no history is available for a specific user.
The data source recommended can be viewed on the Tableau server and it can be added directly as a data source by the ‘Add Data Source’ option.
Advantages of Smart Data Prep
Using the features of Smart Data Prep reduces the manual work involved in data preparation activities by the user which enables customers to see more automated cleansing and formatting options. ML capabilities employed to achieve these functionalities makes it more efficient to perform your analytics more effectively. This improves the self-service experience of the user which in turn aids in arriving at quicker data-driven decisions.
To know more about the Smart Analytics features provided by Tableau check out our blog here. Reach out to us for enhanced data analytics capabilities to drive your organization based on data-driven decisions.