In addition, augmented analytics can, in some cases, empower business users -- people who lack advanced data science skills -- to make informed, data-driven decisions in contexts where they would otherwise lack the technical know-how to process and interpret data effectively. In this way, augmented analytics makes it possible for a broader set of stakeholders within an organization to take full advantage of BI tools, which use data to help drive decisions that impact the business.

Augmented analytics enables a range of applications and capabilities related to data analytics that are challenging to implement without the help of AI, ML and NLP techniques.

Here's a look at common examples of how organizations might apply augmented analytics.

In many cases, raw data sets need to be enhanced before they can be analyzed effectively. They might contain missing, inaccurate or redundant information, for example, that requires cleaning up to avoid mistakes during the data analysis process.

This data preparation work can be performed manually, but augmented analytics can accelerate the process by automatically identifying and addressing data quality, completeness and accuracy issues within data sets. For example, an AI model could identify words within data that are likely misspelled and then correct them to improve data quality.

Identifying noteworthy patterns and anomalies within data sets -- which are often the chief goals of data analytics -- can be challenging, especially when they are large and complex. But, with assistance from augmented analytics tools, data analysts can more easily make sense of data.

For example, an AI model that is trained to recognize certain types of patterns might be able to identify those patterns within a large, multidimensional data set where it is too complex for most humans to recognize the same types of patterns using manual analysis. Or an AI model designed for natural language generation could summarize a data set using natural language that humans can easily understand.

Augmented analytics tools can assist in developing data visualizations, which are visual representations of data or trends. Rather than creating charts and graphs by hand, teams can use AI-powered data visualization tools to determine which data points are worth visualizing and how best to present them in visual form.

Even when data sets are small and simple enough for humans to analyze by hand, it takes time to identify interesting information within the data and figure out how the data points relate to each other. By automating this process, augmented analytics can dramatically speed data analytics, enabling teams to find the information that matters faster and with less effort.

Augmented analytics tools with NLP features make it possible for humans to ask questions about data in natural language instead of having to write computer code to analyze data. This capability saves time, especially for data analysts with limited technical experience.

Sometimes, deriving relevant insights from data is not enough; teams must also be able to explain how they arrived at the insights they did. Augmented analytics can help by automatically linking data insights to the data points or characteristics that support a particular interpretation of the data. While it is possible, in most cases, to derive explanations manually as well, doing so takes much longer than automating the process using ML algorithms or other augmented analytics techniques.

Augmented analytics plays an important role in enabling business users to become citizen data scientists. It does this by enabling nontechnical users to use analytics and BI platforms in a fashion similar to professional data scientists -- but without requiring specialized knowledge.

For instance, citizen data scientists might use augmented analytics to generate summaries of data sets using natural language. Or they could use NLP features to ask questions about data using a no-code approach.