How to Maximize Your Data Value With
Tableau’s Smart Analytics?
BI Connector Team |
Data Analytics for business users is not just about self-service data visualization anymore.
The Data Analytics platforms such as Tableau give more superpowers to end-users by extending the scope of self-service to data preparation and predictive analytics as well.
Tableau is leaving no stone unturned to simplify data analysis and decision-making for its users. In fact, Tableau not only helps users tap into their prebuilt AI but also enables them to build their own no-code ML models, as a part of Tableau AI Analytics.
In this blog post, we’ll take a quick look at Tableau’s Smart Analytics and how it can change the analytics game for end-users while reducing the burden for IT teams.
Tableau Smart Analytics
The Smart Analytics features in Tableau are primarily about smart data preparation, automated data discovery, natural language capabilities and statistical model integrations.
Let’s look at them one by one.
Smart Data Prep
In general, the time taken to prepare the data for analysis, especially in creating table relationships and format/structure the data (apart from connecting to data sources), is significantly higher than the actual time of analysis.
The users are usually frustrated with the manual efforts going into the data preparation phase. The Smart Data Prep feature helps the users by smartly executing the behind-the-stage tasks required for data analysis.
The feature comes with a Data Interpreter to analyze the data and create table relationships, and format/structure it.
For example, if one of the tables has dates in MM/DD/YYYY format, and another table has dates in the YYYY-DD-MM format, the smart data prep feature will help users save their time by standardizing the dates in both tables.
Additionally, the feature can also do fuzzy matching and reduce the manual errors made in the data.
For example, if the state name data is wrongly entered as Califonria instead of California, then the fuzzy matching feature will jump in and help users fix the error.
The users can also perform predictive analytics on their data with simple drag-and-drop actions. Tableau accounts for seasonality with exponential smoothing.
Natural Language Features
The thought process of the users plays an important role when analyzing data.
Before the Natural Language features came into existence in Tableau, the users had to perform the following tasks to answer each of their questions flashing in their thought process!
- Identifying and selecting the visualization that will effectively answer the question (this step usually results in a trial-and-error learning curve for the beginner-level end-users)
- Selecting the appropriate data points for the chosen visualization
The above tasks demanded a further thought process from the users about how to use Tableau’s features to find answers to the questions in their actual thought process. Hence, the time consumed for finding the answer was slightly more, despite the absence of any performance issue.
However, Tableau has its own “Hey Google” version, for data analytics. You don’t have to embed the “Hey Tableau” to the beginning of your question, though!
Tableau’s Natural Language Processing (NLP) feature helps users to simply ask a human conversational question like – “What’s the sales revenue last year?”, and get the answers in the form of data visualizations instantly.
Now one might get the question – What if the user’s thought process missed a critical insight from the visualization. That’s where the Natural Language Generation feature fills the gap.
The other Natural Language feature is the Natural Language Generation, where Tableau summarizes the key insights from the visualizations into natural language sentences and presents them to the users.
Automated Data Discovery
Tableau has an amazing feature to explain your data to you!
In human analysis, some hidden yet critical insights could be missed. The “Explain Data” feature helps users uncover insights that they could have missed otherwise.
Tableau provides the “Explain Data” option, which runs on Artificial Intelligence (AI) technology to automatically show you which data points affect the data point you want an explanation for.
The feature is extremely useful when the users are analyzing a dataset that is a grey area to them.
For example, the Chief Sales Officer (based in the USA) can analyze and uncover insights from the sales data in the Australian market, and find how the sales are affected in Australia based on the pricing, festival seasons in Australia that he/she is not actually aware of.
Tableau can point out the significant variations in sales in a certain month, day, locality, etc, through the explain data feature and help users to uncover these insights for taking appropriate actions. Of course, Tableau presents the data with the accuracy of the insight as well.
All the users need to do is just select the part of the visual they want an explanation for, and click on the Explain Data option.
Further, Tableau helps users to categorize data into clusters also, to help them uncover much deeper insights.
Statistical Model Integrations
Tableau is helping the data scientists as well, despite its quest for simplifying Data Analytics for Business users.
Data Scientists would love to tap into the existing functions or models built in technologies like Python, R, etc for performing complex custom calculations.
Tableau provides a statistical model integration feature as well, for data scientists to take a deep-dive, and come up with even deeper insights.
Tableau is progressing in leaps and bounds to meet the end-user needs not just for Data Analysis, but also for making decisions.
As mentioned earlier, Tableau, as a part of its AI Analytics enables business users also to build no-code ML models, apart from helping them leverage the prebuilt AI solution Einstein Discovery from Salesforce.
If you’re using OBIEE, you can connect Tableau to OBIEE (and OAC/OAS) through BI Connector and uncover insights from your OBIEE data using the smart analytics features of Tableau!
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