Today’s enterprises are living in a profitable time; the ones that understand their data call the shots. However, companies are always looking for ways to realize the highest potential of their data. With a goldmine of data from sales, business operations, vendors, customers and other critical business activities, modern enterprises feel it’s becoming overwhelming to manage data the ordinary way.  

According to a study by Mckinsey, the global market for augmented analytics is on an upward trajectory and will reach $78.1 billion by 2030.  

A decade ago, figuring out what your data meant was like needing a team of super-smart data specialists to climb a mountain of information. It was expensive and not everyone could afford it. But now, there has come a whole new advancement that can help businesses manage their data as efficiently as possible. Experts call it Augmented Analytics; a cutting-edge technology that combines artificial intelligence (AI) and machine learning (ML) with data analytics.  

This potent combination democratizes data-driven decision making, making it accessible to businesses of all sizes, regardless of their budget for a dedicated data science team. In essence, augmented analytics acts as a force multiplier, unlocking the full potential of data for organizations across the spectrum. And very recently, many businesses have put traditional data analytics on the back burner with augmented analytics taking over. This blog talks about what augmented analytics is and explores its transformative impact on the contemporary business landscape.  

Augmented Analytics Explained 

Here is the simplest definition of augmented analytics: an emerging class of analytics that are poised to create a paradigm shift in the way businesses interact and make use of their data to bring analytical capabilities to more people in the most advanced way.  

Even though the recent advancements in data analytics dramatically changed what data analytics can do for enterprises, predictive and descriptive analytics can be time-consuming and produce narrow insights. Here’s where augmented analytics steps in: it revolutionizes analytics by automatically pulling in and processing relevant context from a wide range of disparate data sources. This way the classic pain points of navigating siloed data and poorly maintained catalogs can be eliminated, and tasks that previously burdened analysts can be enhanced to address the modern data conundrum every company faces from time to time. 

How AI and ML make Augmented Analytics More Accurate 

Unlike traditional data analytics services, augmented data analytics don’t require human analysts’ interpretations, which can be swayed by their experiences and expectations. Since augmented analytics uses AI and machine learning, which are objective and data-driven, the insights you get are based purely on what the data reveals, not any human biases. Moreover, augmented analytics can handle massive datasets efficiently. It’s like removing the keyhole wall and seeing the entire room – you get a more complete picture, reducing the chance of missing crucial information that could impact your conclusions. 

Reasons Why More Businesses Are After Augmented Analytics in 2024: 

Given how competitive today’s market is, most CEOs and CDOs think that conventional dashboards and static reports are becoming more time-consuming, expensive and limited in capabilities. Augmented analytics, powered by AI and ML, hold great promise to bring a world difference in the realm of data management and decision-making. Here are some compelling reasons why most businesses are increasingly drawn to augmented analytics.  

Accelerated Speed-to-Insights 

Imagine searching for a specific needle in a massive haystack. Augmented analytics, powered by AI, acts like a smart magnet. It trims down the search area, highlighting relevant data for the right people at the right time. This “smart guidance” suggests promising avenues for data analysts, saving valuable time. The system learns user behavior over time, personalizes recommendations, and fine-tunes search defaults further enhancing speed and focus. With augmented analytics data and business analysts can spend less time sifting through data and dedicate more time to strategic tasks. 

More Control Over Your data 

Machine learning and AI have revolutionized repetitive tasks across many industries. Augmented analytics applies this automation power to the world of data analysis. Repetitive tasks like data preparation, discovery, and statistical analysis are handled by the system, freeing up analysts’ time and energy. This allows them to focus on higher-level activities like interpreting insights and formulating strategies. Businesses are swimming in a sea of data, and without proper control, it can be like trying to navigate a stormy ocean with a leaky boat. Augmented data analytics act as your life raft, giving you the tools and capabilities to steer your data effectively.  

Improved Data Management 

Poor quality data leads to unsuccessful business decisions. Traditional data analytical strategies might miss inconsistencies or errors within datasets. Augmented analytics can analyze data and identify potential quality issues like missing values, outliers, or duplicates. These insights help data management teams prioritize data cleaning efforts and ensure the accuracy of the information used for analysis. Think of it like a mechanic identifying potential problems with your car before you embark on a long journey. By leveraging the insights and automation capabilities of augmented analytics, companies can significantly improve their data management practices. This results in a more organized, secure, and reliable data foundation, empowering them to get the most out of their data modernization initiatives.  

Improved Predictions 

Companies don’t only want to know what is happening in their business but also want to know what is likely to happen in the future. Augmented analytics can continuously analyze vast amounts of data in real-time, identifying trends and patterns much faster than traditional methods. This allows businesses to stay ahead of the curve and make data-driven predictions about future market conditions, customer behavior, or potential risks. 

Data Democratization 

As data analysis expands beyond dedicated departments and embraces a wider range of employees, data literacy flourishes throughout the organization. This cultural shift empowers teams to become more comfortable with data, fostering collaboration and ultimately unlocking its potential to generate significant business value. Augmented analytics goes beyond just access. It provides user-friendly tools with features like natural language querying and automated insights. This allows business users with less technical expertise to not only access data but also understand it more easily. They can ask questions in plain English and receive clear, AI-powered insights, enabling them to make data-driven decisions without relying heavily on data specialists. 

Augmented Analytics: A new powerful catalyst for businesses to rise above the ordinary 

In today’s competitive business landscape, gaining valuable insights quickly isn’t enough. You need actionable intelligence – the “right information, at the right time.” This is where augmented analytics comes in. By combining augmented analytics with traditional business analytics tools, companies can not only gain clearer insights into trends and events, but also proactively plan for the future, turning insights into action and concepts into reality. 

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