This article will explore augmented analytics, as well as how its technologies and tools are used by the Tableau platform.
What is Augmented Analytics?
Augmented analytics is a process of automating insights using technologies like natural language processing, artificial intelligence (AI), and machine learning. It provides solutions that can help organizations handle complex datasets at scale, provide more universal access to insights, and engage workers at all levels with the data.
The field of augmented analytics is quickly and significantly transforming how data is cleaned, analyzed, visualized, and acted upon. Advanced analytics ensures that more people can have access to the insights and recommendations in a shorter amount of time. It relies on machine learning, a branch of computer science in which data is used to create learning models and algorithms, for many of its features. Machine learning is a powerful tool for Data Analysts because it drastically reduces tedious work in data cleaning, shaping, examining, and filtering, which means insights can be extracted quicker and decisions made in a shorter amount of time.
Why use Augmented Analytics?
Those working with data benefit greatly from applying augmented analytic techniques and tools to their datasets. Here are just a few of the ways augmented analytics can help:
- Data Preparation: By augmenting the data preparation process, it’s possible to collect data from various sources at a much quicker speed than manually working. Algorithms can point to joins and schemas in data, and repetitive acts like integrations can be completely automated. In addition, various actions that occur before data preparation, such as profiling, tagging, and annotation, can also be automated so that the data is cleaned and ready in a much shorter amount of time.
- Instantaneous analysis: Automating the process of analyzing data and setting it to always run is another time-saving measure. In addition, if the augmented tool spots a change or drop in the data it’s analyzing, it can be set to automate the process of delivering that information to the user, which enables them to act as soon as possible.
- Decreases bias: When analyzing data, humans often make assumptions, especially when they aren’t completely certain what they are searching for. Having a machine perform the analysis ensures that analytical bias is reduced and that factors that affect statistical significance are what is examined.
- Statistical techniques: The technologies that drive augmented analytics are able to choose from the most effective clustering, forecasting, or statistical algorithms and apply the one that provides the greatest certainty.
- Data literacy: Natural language can be applied to the data exploration process, as well as the analysis of results, in order to enhance data literacy.
- Agility: By incorporating AI-powered recommendation engines, businesses can receive recommendations at a much faster rate. This means that even those who lack formal technical training can analyze trends such as changes in purchasing patterns for customers, and eventually enhance the AI models to receive even more helpful recommendations.
- Deeper analysis capabilities: It’s long been a challenge for those working with data to approach the minute details of data for extraction and analytics purposes. Augmented analytics aids with this process by automatically querying dozens of variables and billions of points of data in mere seconds. Not only does this increase the speed at which insights can be extracted, but it also leads to more helpful insights that can be used to offer better predictions.
- Recommendations: Most augmented analytics systems provide users with AI-driven recommendations for various parts of the analytics process, from data preparation to sharing. Suggestions pertaining to data cleaning or joining, which chart types to select to visually depict data, and even which analytical content they may wish to explore based on their browsing behavior and professional role in an organization, are just some of the ways AI-driven marquees can aid with data analytics.
- Breaking down data silos: In order to account for all available data that can be used to answer unique queries, augmented analytics offers a collection of data that can be shared with all members of an organization. In order to do so, when natural language queries are made, all datasets from big data storage locations, databases, and integrated platforms are explored to generate a thorough answer. In addition, machine learning models are updated with each new query so that all new dataset connections are included. Then, anyone performing queries within the organization can draw from these new connections.
- Overall cost reduction: By automating some, if not all, of the stages of the data analytics process, decisions that affect a business can be made much faster, and often without the need for Data Scientists. This helps companies reduce operational costs and still receive quality insights.
- More effective business decisions: The sheer speed at which augmented analytics can perform data analysis provides business insights in seconds rather than weeks so that a business need not miss a crucial window of opportunity to act on insights.
How Tableau Uses Augmented Analytics
With the help of augmented analytics, Tableau users are able to understand and visualize data in new and helpful ways. Best of all, Tableau users don’t have to learn any advanced analytics to reap the benefits. Tableau has embedded AI and machine learning capabilities in a user-friendly manner with features like:
- Ask Data: When working with their data, users can enter a question and get an immediate response directly in Tableau. These answers are displayed as data visualizations. They don’t even require users to use drag-and-drop features or have a deep understanding of the structure of the data. Ask Data can work with data sources already in Tableau. It is fully integrated into Tableau’s platform.
- Ask Data for Salesforce: This tool helps Salesforce users to ask data-driven questions using semantic search and natural language technology. Answers are provided immediately via insights, generated reports, recommended dashboards, each of which is geared toward their business context. The structure and context of your organization’s language are learned by Einstein directly from CRM, which means that Data Analysts don’t have to spend as much time generating perfect questions and can instead focus their efforts on acting based on insights.
- Explain Data: With just one click, those using Explain Data can receive an AI-fueled explanation for the value of a data point. This tool relies on complex statistical models to provide integrated and pointed explanations. Not only does it help Data Analysts to spot insights they may have otherwise overlooked, but Explain Data is also a valuable time-saving tool.
- Einstein Discovery for Reports: This tool generates AI-driven insights automatically within Salesforce Reports. This allows users to reach fast conclusions about what occurred and why it happened so that swift action can be taken. In addition, Einstein Discovery for Reports leverages machine learning to quickly and efficiently review report data, then performs statistical analysis on it. Then, an Einstein Discovery story is generated in just a few clicks, which can be used for a more thorough analysis.
Tableau is committed to investing in augmented analytic technologies that will continue to change the way its users interact with data. In the future, it is expected that new augmented analytic capabilities will help Tableau continue to democratize data and provide a greater audience with the necessary tools to perform faster, better analysis.
Hands-On Data Analytics & Data Science 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 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. In addition, 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 60 bootcamp options are available for beginners, intermediate, and advanced students looking to learn more about data mining, data science, SQL, or FinTech.
If you are interested in finding a data science class nearby, Noble’s Data Science Classes Near Me tool makes it easy to locate and learn more about over 100 courses currently offered in the in-person and live online formats. Class lengths vary from 18 hours to 72 weeks and cost $800-$60,229. This tool allows users to find and compare classes to decide which one is the best fit for their learning needs.