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Why Augmented Analytics is the Future of the Data Industry

A poor workman may blame his tools, but a good workman knows how important it is to choose the most effective tools and keep their toolbox up to date at all times.

It’s a saying that’s not just relevant for carpenters or mechanics – it’s just as important for business leaders who use data analytics tools to reveal insights into customer preferences, market trends, organizational efficiency, and more. 

Throughout the decades, these tools available have been steadily maturing to become more innovative, intuitive, and easy to use. 

Today, cutting-edge technologies like augmented analytics are described as the future of the Big Data industry. Augmented analytics applies AI and ML to the gargantuan task of collecting, processing, and analysing data to generate business insights. 

According to Sisense Augmented Analytics is the Future of Business Intelligence. In July 2017 Gartner described “Augmented Data Discovery to be the future of data analytics”.

So, in this article, we will discuss the reasons why Augmented Analytics is described as the Future of the Data Industry.

1) Improved Data Collection and Processing

Improved Data Collection

Big data isn’t just big, it’s vast. Businesses have to grapple with a flood of new data that pours in around the clock and from all sides.

Data arrives in a variety of formats, protocols and structures, making it close to impossible for human-led data processing to integrate it and turn it into the uniform datasets that older BI tools demand. 

As well as being slow, manual data consolidation, preparation and processing also introduce the risk of human error.

In contrast, augmented analytics connects disparate data sources into a frictionless system that agglomerates and processes non-uniform data in the blink of an eye.

AI and ML engines can learn to spot potentially valuable data that humans may overlook, and add it to the collection to enhance analysis. 

Faster data processing slashes the time it takes to generate actionable and meaningful insights. What’s more, the most advanced data cognition systems move data analysis even further forward, condensing data into small neural networks that support light-speed data exploration and interaction.

2) A Fast Track to Unexpected Insights

Human-led BI tools are inevitably restricted by the limits of human imagination and innovation. When a person sits down to look for answers in data visualizations, they can only spot the answers to questions that they already have in mind.

AI-led data analysis overcomes user bias to open up new ways of looking at your business data, producing valuable insights independently. 

ML engines are quick to learn which are the best visualizations for each type of data, turning raw information into visually appealing reports that allow the human user to understand and absorb the impact of data in a single glance.

With the right charts, graphs, and images, augmented analytics platforms can present raw data in a way that encourages the viewer to make new connections between events and draw new conclusions that they may not have noticed. 

Augmented analytics platforms can likewise serve up surprising and unexpected results that you would never have thought to look for, yet desperately need to know.

These tools can spot trends and patterns in data far sooner than the human brain, enabling them to generate insights and predict changes long before they impact the business. Adding AI to human intuition creates a system that is far more capable than the sum of its parts.

3) A Democratized Approach to Data

Democratized Approach to Data

Since data is now the new oil, every department in the organization needs it to fuel their progress. However, it’s rare for any company to have enough data technicians and analysts to serve everyone in the organization who needs to process data, run models, and generate new insights. 

Enter augmented analytics platforms, which democratize access to data by completely flattening out the walls that tend to spring up around it. With augmented analytics, every employee can apply data-driven insights without the assistance of a data science department or dedicated data analyst. 

There’s no risk that data will become siloed in an outdated data warehouse, or that insight will languish in a single department without reaching the people who really need to apply them.

This spreads data-driven decision-making across the entire company, increasing the opportunities for innovative ideas and revelations to appear anywhere in the business. 

4) Newly Streamlined Sharing Networks

Streamlined data sharing

It’s not just accesses to data analysis that needs to be shared around the organization. Improved access to data has to go hand in hand with a more streamlined way of sharing insights and realizations across the board. 

Augmented analytics promises to replace the monthly distribution of reports with instant sharing of dynamically updated visualizations between colleagues, teams, and departments.

These platforms can connect with native apps, chatbots, and AI-powered voice assistants like Alexa to make report-sharing smooth and intuitive.

They suggest co-workers to tag on reports, creating a social sharing network for insights and predictions and resulting in an immersive experience that encourages leaps of creativity. 

The improved user experience makes working directly with data far more pleasant and less threatening to employees who might be timid about their data analysis abilities, ultimately boosting the adoption of data-driven decision-making. 

5) Augmented Analytics for Data-driven Business Success

Augmented analytics pushes data analysis to the next level. Thanks to the improved ability to integrate and analyse raw data, generate unexpected insights, open up access to advanced analytics, and smooth out the process of sharing insights, predictions, and reports, augmented analytics is here to stay.

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