What is Automation in Data Analytics?

When used in data analysis, automation pertains to replacing the human factor with computer processes or systems. The act of automating analytics requires constructing systems that can automate one part of a data pipeline, or the entire pipeline. The various mechanisms that automate data differ in complexity. Whereas some are basic scripts that are compatible with pre-established data models, others are complex, full-service tools that enable users to carry out actions such as exploratory analysis, statistical analysis, and model selection.

Automated analytics is especially useful for businesses in that it offers insights that may not be available through any other means. This powerful technology draws from machine learning and AI, which enables it to analyze huge stores of data, offer hypotheses, train hundreds of machine learning models, and generate thousands of data patterns. Although automation can’t completely take over the data science process, it helps to eliminate some of the more tedious aspects.

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What is Tableau?

Tableau is the leading analytics platform for business intelligence on the market. It allows users to simplify raw data into a format that’s easy to access and understand by those working at any level of an organization. Even non-technical Tableau users can create customized dashboards and worksheets with the help of this versatile tool. Some of Tableau’s most remarkable features include its capacity for data blending, real-time analysis, and data collaboration. It can be installed directly onto one’s hardware from a web download and be operational in just twenty minutes.

This article will explore some of Tableau’s automated capabilities, as well as the benefits of automation for data analytics and visualization.

What Automated Capabilities does Tableau Have?

Tableau relies heavily on automation to help users streamline and speed up many data analytic tasks that would otherwise be tedious and time-consuming. The following are just several of the ways automation is helping Tableau users more effectively analyze and visualize data:

  • Webhooks are a helpful Tableau feature used by one computer system to notify another about an event that has occurred using web technologies like JSON or HTTPS. Those using Webhooks in Tableau can subscribe to events, and if these events are triggered, an HTTP POS notification will be delivered to a pre-specified URL. Webhooks are particularly useful in the following situations:
    • If your Slack channel needs to be immediately notified about workbook updates.
    • In instances when an extract refresh fails. A ticket can be automatically filed in ServiceNow.
    • If a workbook refresh is successfully completed and must be posted on SharePoint.
    • Upon publication of a data source, an email is sent to a data steward requesting them to certify it.
  • For Tableau users who wish to monitor server statuses, the Tableau Server Status page as well as the Tableau Services Manager status page both have Tableau Server processes and troubleshooting documents. Users who hover a mouse over the status indicator of a process can see the node name and port on which the process is running. This helps Tableau users quickly pinpoint any irregularities that must be dealt with.
  • TabPy, or Tableau’s Python Server, incorporates an Analytics Extension implementation designed to broaden Tableau’s capabilities. This powerful tool enables users to carry out Python scripts and saved functions with the help of Tableau’s table calculations. TabPy is great for predictive algorithms as well as data cleaning.
  • Tableau alerts make it possible to respond quickly when action is needed. Users can elect to receive server interface notifications or an email if a flow process fails. This ensures that the necessary steps can be taken to study the errors, repair them with the offered suggestions, and then return to analysis.
  • Permissions can be set for prep flows that are managed by Tableau Prep Conductor. These dictate who can view the flow, make changes to it, run the flow, or perform other necessary actions. For a flow that’s connected to a database, users can also choose the type of authentication and establish credentials to access data when the flow is published.
  • In order to make sure that the appropriate people have access to data, Tableau allows users to use tagging when handling workflows. Data Analysts can apply keywords to flows so that users can locate, filter, and even categorize content. This is especially helpful for situations in which work is being shared across an organization, as the flows can be found, reused, or modified as needed. In addition, multiple flows can be simultaneously tagged.
  • Prep flow updates on Tableau can be set to run automatically during non-work hours. This helps save the time it may take for an employee to manually update data and ensures that the updates occur at times when fewer jobs are competing for resources. In addition, pre-scheduling these updates for desired times guarantees that they can be executed on a stable server rather than their desktop.
  • Tableau’s Web Data Connector provides users with a way to connect to data that’s available over HTTP and doesn’t yet have a connector. Users can make their own web data connectors or use those created by others, so long as it is hosted on a webserver running locally on one’s computer, on a third-party web server, or on a web server in one’s domain.
  • To learn more about flow performance, those working with Tableau can choose out-of-the-box Administrative Views. This allows users to incorporate the tools they already use to keep track of prep flows and to answer pressing questions about how their organization is treating its data. Views are available that track task duration, user actions, statistics on space usage, as well as scheduled vs. ad hoc flows.
  • Tableau’s Document API provides users with a supported means for automatically performing updates to database connection strings in workbooks and data sources. It can also be used to generate and deploy templates.

Although automation is currently still in the relatively early stages of development, it is already playing an integral role in the speed and efficiency with which businesses can gain insights from data when working with platforms such as Tableau. According to a 2020 survey, nearly a third of businesses have completely automated at least one function. This estimate is projected to continue to increase, as more data is created, and as new machine learning and AI techniques become more commonly applied to the data sector.

Hands-On Automation & Data Analytics Classes

For those who want to learn more about automation, as well as the other tools available to efficiently work with big data, Noble Desktop’s data science classes provide a great option. Courses are available in-person in New York City, as well as in the live online format in topics like Python and machine learning. Noble also has data analytics courses available for students with no prior programming experience. These hands-on classes are taught by top Data Analysts and focus on topics like Excel, SQL, Python, and data analytics.

If you want to learn more about how Python can be used for automation, Noble’s Python for Automation class is for you. This six-hour class teaches students how to collect, store, and analyze web data using Python.

Those who are committed to learning in an intensive educational environment can enroll in a data science bootcamp. These rigorous courses are taught by industry experts and provide timely, small-class instruction. Over 40 bootcamp options are available for beginners, intermediate, and advanced students looking to learn more about data mining, data science, SQL, or FinTech. Courses run between 18 hours and 72 weeks in length.