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.

This article will explore the primary uses of augmented analytics for Data Analysts.

How is Augmented Analytics Used for Data Analytics?

The field of augmented analytics has many applications for those working with data. Here are just some of the many ways this field is being used by Data Scientists and Data Analysts:

  • 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.
  • Data identification: One of the features of newer business intelligence platforms is their ability to use AI technology to automatically find specific attributes of data, such as personal or geographical information.
  • 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.
  • Collaboration: Natural language generation also plays an important role in the process of sharing insights with end users. It allows insights to be delivered in real-time, and then be viewed using an online dashboard.
  • 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.

The Future of Augmented Analytics

Now more than ever, businesses are in a position where they must quickly adjust to changes in technology and the way data is handled in order to stay competitive. The time of big data is upon us, and those who have the most effective tools to handle this data have the upper hand because they have access to the most helpful insights in the shortest amount of time.

In the future, augmented analytics is expected to continue to play an integral role in the field of data analytics. In order to continue to use this tool, it’s important for organizations to devote time and resources to explore how best to implement this technology. As more organizations introduce AI technologies, machine learning, and natural language processing capabilities, the speed, accuracy, and overall effectiveness of the data analytics process will continue to improve. In the future, data will be an even more effective tool to drive successful business decisions for those organizations that have the best automation capabilities.

Hands-On Data Science & 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.

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 the nearly 100 courses currently offered in the in-person and live online formats. Class lengths vary from 18 hours to 72 weeks and cost $915-$27,500. This tool allows users to find and compare classes to decide which one is the best fit for their learning needs. This tool can also be used to choose from more than 100 computer science classes as well.