7 examples of augmented analytics in the enterprise

What is augmented analytics?

Augmented analytics is the use of machine learning (ML), AI and natural language processing (NLP) to make data analysis more efficient and effective. In most cases, the primary goal of augmented analytics is to help humans interpret large and complex data sets faster and more accurately than they could using a manual analytics approach.

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.

Types of augmented analytics applications

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.

1. Data preparation

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.

2. Data interpretation

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.

3. Data visualization

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.

4. Data analytics acceleration

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.

5. Data queries

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.

6. Insight explanation

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.

7. Enabling citizen data scientists

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.

Industry use cases for augmented analytics

The ability to streamline and simplify data analytics is beneficial across a range of industries. Following are some examples of augmented analytics use cases within specific business contexts:

• Assessing risk in finance. In finance, the ability to analyze data is often crucial when assessing risk. Augmented analytics helps by making it easy for anyone in the organization to do so. For example, a loan officer at a bank could use augmented analytics to interpret the data a business submits about its finances when applying for a loan. Augmented analytics tools enable the loan officer to ask and answer questions about the data without having to prepare the data, write queries or identify trends manually.
• Explaining loan rejections in finance. In another finance use case, a bank might need to explain why it rejected an applicant for a loan. Here, the insight explainability capabilities of augmented analytics tools make it possible for loan officers to identify the specific information, such as a low credit score or a poor loan-to-value ratio, within the application data that led to the loan rejection.
• Managing retail inventory. Retailers can use augmented analytics to analyze inventory data so they don't run out of stock or purchase more items than they can sell in a given time period. For instance, an inventory manager might use augmented analytics tools to generate a summary of current sales trends to determine which items are selling the fastest. The manager could then compare fast-selling products against current inventory levels to determine which ones to stock up on.
• Speeding assembly lines in manufacturing. By using augmented analytics to interpret data from the factory floor, such as how long it takes to assemble various parts of a product and where defects are introduced, manufacturers can improve the efficiency and reliability of the production process. For example, a manufacturing architect could pose a question in natural language about where bottlenecks are occurring during manufacturing and then use the insights to improve the process.
• Improving learner outcomes in education. When teaching large groups of students, educators often want to be able to identify outliers who are starting to fall behind so they can take proactive action. They could do this with help from augmented analytics tools that enable them to determine quickly which students have the lowest test scores or attendance rate, for example. Later, they could use augmented analytics to assess the extent to which the interventions they've taken with those students have improved their standing in the class.
• Planning marketing campaigns. Running an effective marketing campaign requires the ability to parse large volumes of data about different groups of customers, their interests and the types of marketing channels that are most effective in reaching them. When working with complex, multidimensional data sets like this, the ability to pull out key trends quickly using augmented analytics is critical. A marketer could, for example, use augmented analytics to determine whether prospective customers in a certain demographic are more receptive to television or internet ads and then plan a marketing campaign accordingly.
• Reducing shipping times in the logistics industry. Logistics companies must analyze large amounts of continuously changing data to determine the current status of the items they're shipping and to identify and respond to obstacles that could impede their operations. Augmented analytics helps them do this by making it possible to ask questions about data that reflects the status of logistics networks. Plus, whenever the data changes, logistics companies can ask the same questions again to determine whether they need to modify their routes to avoid shipping delays.

Chris Tozzi is a freelance writer, research adviser, and professor of IT and society who has previously worked as a journalist and Linux systems administrator.