Augmented Analytics: What Is It & How Does It Work?

The complexity of today’s data makes it occasionally difficult to read and understand. In this case, technology like augmented analytics can be helpful. It makes sense to use artificial intelligence and machine learning to make data easier to interpret. But what is it exactly, and how does it operate? We’ll discuss augmented analytics in this article, including tools, benefits, and examples.

Augmented Analytics

Artificial intelligence (AI) and machine learning (ML)-based augmented analytics are subsets of analytics that increase a human’s capacity for contextual data interaction. “Augmented analytics” refers to the tools and software that enable more individuals to access analytical capabilities, such as recommendations, insights, or query guidance.

According to Gartner, a leading provider of market research and consulting services, “Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation, and insight explanation to augment how people explore and analyze data in analytics and BI platforms.”

Who is Augmented Analytics For?

Because these technologies enable business users and executives to swiftly extract value from their data without requiring in-depth technical knowledge or data expertise, augmented analytics is incredibly valuable to them. Business users and executives may access pertinent data, formulate the best queries, and swiftly obtain insights in the context of their company with the aid of augmented analytics. While augmented analytics primarily benefits those without deep analytical experience, it also speeds up data preparation and more detailed analysis activities for analysts and skilled users.

Benefits of Augmented Analytics

The use of augmented analytics can improve the speed, effectiveness, and accuracy of analyst work. By lowering technological hurdles to analysis, including opening up more sophisticated techniques to those with less developed data skills and expertise, machine learning and natural language technologies also aid in bringing domain experts—people engaged in the business—closer to their data.

#1. Agility: Quickening the Rate of Insight

AI-powered augmentation can speed up idea discovery by limiting the search field, presenting relevant facts to the right person at the right time, and suggesting promising research paths. By tracking user behavior, systems can improve defaults, promote activities, and customize them over time. When data inquiries are answered promptly, people can focus on more critical tasks and spend less time seeking insights.

#2. Accuracy: Creating a Fuller Picture

Machines are exceptionally good at repeating jobs and calculations since they don’t sleep. Because augmented analytics uses AI and ML, it can effectively look behind every object, allowing the user to base their judgments on a full analysis and be as informed as possible. Humans are able to avoid any confirmation bias in their findings thanks to this kind of comprehensive vision.

#3. Efficiency: Automating administrative duties

Artificial intelligence and machine learning have come a long way in fields where algorithms are driven by repeated, highly specialized tasks. (Consider websites that offer further products or content that “you may also be interested in…” or even fraud detection tools.) When working with data—whether for data preparation, data discovery, statistical analysis, or more—augmented analytics enables task automation that saves workers time and energy.

#4. Confidence: Effective Context-Based Analysis

Augmented technologies can model and surface facts in context, confirming your gut feelings and giving you confidence in your judgments. Business users can use their knowledge of their sector or field to assess how to use augmented analytics results, even though they are often simple to use.

Augmented Analytics Features

Analysts may find it challenging to select the best alternative from the available choices. Therefore, we put together a list of features to look for in a platform for augmented analytics.

#1. Identification of Data Automatically

Some contemporary BI solutions employ AI to automatically identify specific data characteristics, such as whether a field contains personal or geographic data (such as phone numbers or email addresses). The system can also read data tables in formats like PDFs and text documents, automatically stripping them of any specific formatting, and converting them for efficient analysis.

#2. Intelligent Data Preparation

Powerful algorithms work in the background to prepare data for consumers more quickly while minimizing manual cleanup. In order to save time, augmented analytics systems can index and aggregate related words according to their pronunciation or common characters. This eliminates the need for human fields and value updates.

#3. Recommendations

Making AI-driven recommendations to consumers is a standout feature of many augmented analytics solutions. Data preparation, discovery, analysis, and sharing are all covered by recommendations. For instance, depending on the rows and columns of data the user brings into view, the system may suggest data sources to join, cleaning processes to take during preparation, or useful chart types to utilize.

#4. Interactions Using Natural Language

A natural language query is an enhanced analytics feature that enables a user to type a question in everyday language rather than using a data query language or code to query the data. The system offers a guided experience by converting the text into a query and offering suggestions to complete any gaps in order to try to comprehend the user’s question’s intent and context. Insights can now be gained from data by many people without having to comprehend the underlying data model.

How do Augmented Analytics Work?

Machine learning algorithms are used in enhanced analytics. Artificial intelligence (AI) in the form of machine learning enables computers to learn directly from data without the need for human programming. (I.e., a person does not need to instruct the computers what to do explicitly.) Machine learning algorithms can automatically find data patterns, which they can then use to predict future events.

Augmented Analytics Tools

Let’s talk about the top augmented analytics tools in this section.

#1.Domo

By making data available to everyone, Domo as an analytics tool transforms business. Domo’s low-code data app platform allows anybody to create data applications to drive any activity in their company, right where work gets done, by going beyond traditional business intelligence and analytics.

#2. Tableau

You may obtain, generate, and analyze business data and intelligent information using Tableau, an integrated business intelligence (BI) and analytics solution. Spreadsheets, SQL databases, Salesforce, and cloud applications are just a few of the sources of data from which Tableau users can get information.

#3. Microsoft Power BI

With the help of the business analytics tool Power BI, you can show data, distribute insights throughout your organization, and incorporate findings into apps and websites. Connect to a variety of data sources, use live dashboards and reports to make quick, educated decisions, and bring your data to life. Using visual reports that you can create, publish, and share, your data can be integrated, modeled, and then analyzed.

#4. AnswerRocket

AnswerRocket provides a data analytics platform with search features geared toward business customers. Without having any technical knowledge, you may use the software to make reports and analyses and ask questions about the company in a common language. AnswerRocket combines AI and machine learning with strong analytical capabilities. Additionally, the technology can automate time-consuming tasks and quickly react to ad hoc questions. AnswerRocket is a mobile-friendly augmented analytics tool that has native voice recognition.

#5. IBM Cognos Analytics

IBM offers a wide range of business intelligence and analytics capabilities under two distinct product lines. Because the Cognos Analytics platform is an integrated self-service solution, users can access data to create dashboards and reports. IBM Watson Analytics offers a machine learning-enabled user experience by incorporating automatic pattern detection, support for natural language query and generation, and embedded advanced analytics capabilities.

#6. Oracle Analytics Cloud

Oracle Analytics Cloud offers cutting-edge, AI-focused self-service analytics solutions for data preparation, visualization, enterprise reporting, enhanced analysis, and natural language processing and generation. A mobile BI companion app is available that can also detect your interests. Analytics Cloud dashboards combine data from many platforms and sources so that users may access it through a single interface.

#7. Python Analytics

Pyramid v2020, the company’s flagship platform, offers data and analytics solutions. The system provides self-service capabilities and a multi-user, server-based analytics OS environment. Users can handle data in any environment, regardless of technology, thanks to the platform-neutral architecture of Pyramid v2020. Data from mobile devices and web browsers can be prepared, modeled, visualized, analyzed, published, and presented by these users utilizing the technology.

#8. Qlik Sense

Qlik Sense, the company’s main product, is one of the several BI and analytics solutions it offers. Businesses may merge all of their data sources into a single perspective thanks to the augmented analytics tool. Users of the Qlik Analytics Platform can enhance, extend, and integrate visual analytics into already-existing applications and portals.

Augmented Analytics Examples

Not only the data elite can benefit from augmented analytics. It can and ought to be utilized in a wide range of use cases and business sectors. Here are some examples of augmented analytics.

#1. Retail

Retail success requires understanding and meeting client needs quickly. Augmented analytics can help retail managers improve customer analysis, marketing, and inventory management. This translates into higher brand loyalty, better consumer satisfaction, and ultimately more money in the till.

#2. Manufacturing

It goes without saying that a strong supply chain is essential for modern industrial success. The pandemic, inflation, and geopolitical worries have severely affected these supply networks. As one of the augmented analytics examples, it improves supply chain analytics even further by helping manufacturers improve quality control, foresee maintenance needs, optimize production schedules, and increase margins.

#3. Financial and Banking Services

Data is the foundation of banking and financial services, and there is a lot of it. Organizations can gain a lot by adopting a new approach to analytics, from market data to proprietary data. Risk management, client segmentation, and fraud detection are three areas where augmented analytics are especially helpful.

#4. Medical Care

Healthcare is an augmented analytics example that is more data-driven than ever because of the emergence of electronic medical records. The investigation of this data, however, can be time-consuming for many healthcare experts, and in the field of medicine, time literally saves lives. In order to better care for their patients and maintain their own financial stability, healthcare companies can use augmented analytics to predict disease outbreaks, optimize clinical trials, and improve patient care.

#5. Telecommunications

The telecommunications sector in particular is poised to face a truly unprecedented explosion of data as the globe embraces 5G. Network operators can use augmented analytics to better analyze customer behavior, reduce churn, and prevent fraud in order to fully realize the transformative promise of 5G.

#6. Government

The government, as a key service provider, heavily relies on data to run programs and maintain a secure, prosperous society. Regardless of their specific mandate, government organizations are those examples of augmented analytics that manage risk, detect fraud and corruption and improve service delivery.

#7. Education

Teachers are always looking for new ways to customize the learning process and enhance students’ academic performance. Education institutions being one of the examples can employ augmented analytics to better analyze student performance, forecast retention rates, and allocate resources.

8. Entertainment and Media

The entertainment industry has gone digital. Just take a look at how Netflix has become one of the biggest content producers, Disney’s introduction of Disney+, or Spotify’s hegemony over contemporary music. Media and entertainment companies can employ augmented analytics to better understand viewer behavior, boost marketing campaigns, and enhance customer service.

What Is the Difference Between Predictive Analytics and Augmented Analytics?

Based on previous data, predictive analytics employs statistical and machine learning methods to make predictions about the future. On the other side, augmented analytics is a broad analytics tool designed to support human intelligence and speed up time-consuming processes.

Who Is Augmented Analytics Intended For?

What Is the Difference Augmented Analytics and Business
Analytics?

While Augmented Analytics platforms enable users to interact with data dynamically through features like search, natural language narrations, and even audio-visual data tales, Business analytics, and intelligence solutions only generate fixed reports and dashboards as outputs.

What Is the Purpose of Data Augmentation?

By training models on numerous slightly modified copies of existing data, data augmentation is a machine learning strategy used to avoid overfitting while training a machine learning model.