Augmented Analytics: The Future of Data Analytics

Augmented Analytics: The Future of Data Analytics
November 14, 2022
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Augmented Analytics: The Future of Data Analytics

The future has finally arrived and it does not slow down for stragglers. While augmented technologies remain fairly new, they offer interesting developments in the area of business analytics.

Business insights can accurately process real-time data through Natural Language Processing. Plus, augmented analytics programs can run independently, which lets analysts do more advanced solutions-based tasks.

While you may have some basic knowledge about augmented analytics, you might still be wanting more information. How do you decide if augmented analytics are what your company needs?

What are its benefits? And are those benefits what your company needs? This article answers any questions that you have in this area.

Keep reading to learn about this exciting new technology.

What Is Augmented Analytics?

The words "augmented" and "analytics" both refer to significant areas in technology. Let's briefly consider both terms apart from each other.

Augmented means taking digital information to add to an experience. You will have heard about augmented reality, which people also call virtual reality. In this case, human senses and experiences mix with digital technology for a different experience.

Analytics describes systems that find patterns in data using statistical models. We've always found ways to draw conclusions from data offline. Digital technologies open up more options for the collection and interpretation of data.

Augmented analytics uses AI technology like machine learning. Computers automatically sift through data faster and with more efficacy than human-led efforts.

Augmented analytics uses continuous processes that mine data in order to create data models. Data models describe visualizations automatically created by software programs when you input information. Natural language processing has been a growing field where language and text-based data have quantitative value.

Programmers start by adding what is called "training data" to NLP programs. This creates a foundation of data to draw from when it needs to classify future unfiltered data sets. Data models rely on deep learning to constantly refine patterns as it collects more data, which generates more accurate results.

Computers can now perform many data-related tasks automatically. Because of this, analysts don't have to waste time doing more basic tasks manually.

They can focus their attention on using technology for more advanced business insights. This leads to better decision-making and finely-tailored solutions.

Traditional BI vs. Augmented Analytics

Augmented analytics goes beyond the basic levels of data collection completed through traditional business insights. For many years, companies have gained their intelligence through business insights processes which can be informative, but demonstrate significant limits when compared to new technology.

Traditional Business Insights, also called BI, commonly uses one of two methodologies. These include:

1. Customer Relationship Management (CRM)

2. Enterprise Resource Planning (ERP)

CRM programs track information about B2C interactions, or how businesses interact with their customers. Analysts can get general information about how customers' felt during a transaction and their level of satisfaction with the business.

ERP software stores and manages data used by businesses to get insights into human resources, operations, or sales. It sets up a database system that analysts can use to manually generate business insights.

Moving Past the Surface-Level Data

Augmented analytics brings the future wave of new data-driven technology to businesses. Complex data processes, like data mining, designing data models, and generating insights can occur instantly using AI-automated software programs.

These processes can be performed on a larger scale at much faster speeds than before. Traditional BI practices have already been useful for helping businesses improve their products and learn about consumers.

But, as the name suggests, these analytical processes do more when augmented by artificial intelligence and machine learning processes.

How to Use Augmented Analytics

Platforms that use augmented analysis create numerous possibilities. They allow processes that can be applied in creative and flexible ways. Major industries like banking, retail, insurance, pharmaceuticals, and manufacturing benefit from adopting augmented analytics.

Companies can implement augmented analytics for the following use cases:

1. Sales

2. Human Resources

3. Finance

4. Marketing

5. Operations

6. Product Development

Augmented analytics lets lower-level repetitive tasks be automated. The term itself describes a category of newer software and technology processes that use Machine Learning and Natural Language Processing for data.

It functions as one of the many rising fields to come from AI automation. Augmented analytics relies on NLP through conversational interfaces. It also automatically generates metrics from search engines.

Platforms can run using specialized algorithms that perform many of the following data management tasks, which include:

1. Machine-assisted insights

2. Automated Analytics

3. Natural Language Processing (NLP)

Machine-Assisted Insights

Computers can now create visualizations and perform calculations. They also can complete variance analysis. This means that it can easily look into how predictive models compare to real data.

Analysts will no longer need to write coding to come up with queries. They can simply input questions directly because of NLG.

Automated Analytics

This means that data processing doesn't need to have an off-switch. New data insights can be constantly created non-stop. Computers work while you sleep. It also takes away the need for people to do tasks manually.

Instead, they can use visualizations and data stories from the automated data and not spend time on building separate data models.

Analyst jobs can now take on a more specialized role. Workers can be better trained to do advanced work and have more agency and authority. This will happen because computers take care of menial tasks.

Natural Language Processing

NLP lets people engage with analytics technology in a way that feels more intuitive. Augmented analytics can read many variations of information from text and voice.

Because of NLP, these platforms can also quickly create bodies of text that have a lot of information while also being readable. AI automation improves constantly as more data gets added to systems. With this in mind, augmented analytics' ability to process language will continue to grow.

More Benefits From Augmented Analytics

Augmented analytics streamlines a wide variety of complex tasks that lead to more growth and better insights. Some of the other benefits of this technology include the following:

1. More accurate insights

2. Does the job faster

3. Works on a larger scale

4. Can use a broad range of sources

Switching to Data-Driven Decisions

Most of the apps and software platforms used by businesses and consumers collect unimaginable amounts of data. Machines have advanced data management features along with real-time updates that lead to a constant feed of data analysis.

These processes happen so quickly and frequently that analysts can rely on data exclusively for creating solutions, making predictions, and measuring results. All of this leads to insights taken directly from automated data processes.

Making decisions based on data takes away most of the guesswork out of business intelligence. People can now be more confident that their conclusions come from an evidence-based approach. They can instantly track the success of their decisions.

Augmented analysis also helps in locating areas that need improvement.

Real-Life Examples of Augmented Analytics

We've discussed what industries will benefit from the rise of augmented analytics. So far, we can see how they can use automated technology, but only as hypotheticals. Let's take a deeper look into how real-life businesses have created actual results after adopting augmented analytics.

Coca-Cola and Social Analytics

Consumers leave behind so much data when they make purchases, but how they talk about what they buy can offer an unlimited source of data. Most living people have a lot of familiarity and personal experiences with Coca-cola products as well as the brand.

As a company, Coca-Cola has been pouring massive resources into Big Data solutions and AI technologies as early as 2012. They remain a frontrunner in using augmented analytics, most recently with how they've managed to make use of social data.

Coca-cola has been making use of its hundreds of millions of followers on Facebook, Twitter, and other social media platforms to see what people want and what they enjoy. They were able to launch a new flavor, Cherry Sprite, because of data taken from self-serve soda fountains.

They discovered the most popular user-created flavor combinations to come up with their new offering. We can see how this democratizes product development, always giving people what they want for the best results.

Coca-cola has found new ways to mine data in real-time through customer interaction. Newer soda machines will have "virtual assistants" to use data to create highly personalized experiences.

Other innovations include taking data from images. When people take photos of coke products and post them online, Coca-Cola can automatically create new ad content.

Citigroup Advancing Customer Analytics

For the last couple of years, banks have been using mobile features to make their services better. Mobile apps can be used for transactions anywhere. They also let banks work with retail companies to accept payments easily.

Using newer 5G technology, the possibilities for augmented analyticsgrow. It becomes much better and faster. Citigroup now has direct knowledge of how consumers interact with and feel about their services. User-generated data that pours in allows them to gain insights to develop newer products.

Product development that's led by AI has allowed even the design and modeling of products to be automated.

Citigroup has been starting to rely on user experience data to move its company forward. They've done this through insights from constant customer feedback. This helps banks because augmented analytics can assist advisors when working with new and returning clients.

This led to the creation of one of Citigroup's newest products, called CitiPayments Insights. This platform tracks the following processes, such as:

1. Tracking deductions

2. Payment history

3. Processing status and timelines

4. Access to on-demand payments

5. Payments visible in real-time

Augmented analytics has informed the developments in these latest advancements.

Your Business Needs to Catch Up to AI

At this point, you should have a thorough understanding of all the benefits you can gain for your business by adopting augmented analytics. This will be one of the most important investments in your future to stay competitive and profitable.

You want to work with the best software platforms that offer these new technologies. Visit our website today for a free demo.

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