From Data Science to Augmented Analytics: A Paradigm Shift in BI

 


Businesses are increasingly embracing augmented analytics tools due to their ability to simplify data management and analysis, making them highly desirable for both data scientists and other business users. The entire landscape of Data Science, Machine Learning (DSML), and Business Intelligence (BI) is evolving with augmented analytics tools, offering advantages across various use-cases in data management, BPM, and decision-making. This emerging technology is being recognized as the next major disruptive force in the field of data analytics and BI, reshaping the industry.

 

With the influx of complex business data, the importance of using Analytics and BI to make sense of vast amounts of information and make informed decisions is becoming more crucial. Over the years, the Data Analytics and BI space has witnessed significant changes, such as the rise of low coding platforms that replaced complex coding-based systems, and the growing popularity of data visualization tools. These shifts have fundamentally transformed how businesses organize and analyze data to facilitate decision-making. Augmented Analytics stands out as a prominent disruptive shift, harnessing AI, ML, and NLP to automate data analysis tasks.

 

Augmented Analytics empowers users to obtain desired results by simply inputting their requirements, and the tools automatically handle the analysis based on the business needs. Amandeep Singh, an Analyst at Quadrant Knowledge Solutions, highlights how modern technology in augmented analytics enhances users' ability to interact with data at a deeper level, leading to personalized results. Augmented analytics enables businesses to effectively deal with the complexities of large volumes of data through efficient data preparation, management, analysis, and reporting, facilitating insight-driven decision-making.

 

The increasing popularity of augmented analytics tools can be attributed to the following key advantages:

 

Accelerated Real-time Insights: By automating data management and insights generation, augmented analytics offers real-time insights, allowing businesses to make faster decisions. Machine learning algorithms and AI-driven capabilities deliver customized insights in minutes, enabling timely responses to time-sensitive opportunities.

 

Better Analytics with Automation: Augmented analytics automates various aspects of data analytics, leading to faster data preparation, reduced analytical bias, deeper analysis, improved accuracy, and enhanced reliability of results.

 

Better Business Decisions: With augmented analytics handling real-time insights and better analytics, users can focus on asking the right questions and making smarter business decisions. The tools can provide personalized insights and action plans based on user needs.

 

Democratization of Data: Augmented analytics extends access to granular insights and reports to all levels within the organization, empowering employees across various departments to make better decisions based on data.

 

Augmented Intelligence is reshaping the Analytics and BI space, merging the best features of DSML and BI into a new market space. This evolution is evident in various applications of DSML and BI, including data management, BPM, and decision-making, benefiting business users regardless of their coding and analytics skills.

 

The landscape of technology and augmented tools is continuously evolving, leading to changes in Augmented Analytics and the broader DA and BI field. Traditional dashboards are losing relevance as BI dashboards provide more customized insights, and technologies like AI, ML, and NLP further enhance analytical capabilities.

Better Analytics with Automation

One of the most basic advantages of adopting augmented analytics for the business is automation of all the aspects of data analytics. This eliminates the need of manual presence in the tasks. With this automation of tasks, it offers the following benefits in terms of better analytics.

 

Faster Data Preparation 

·       Reduced analytical bias caused by incomplete data sets, lack of context, and wrong choice of data points.

·       Deeper analysis at the most granular levels of data by automated querying

·       Improved Accuracy with AI and ML models, especially in repetitive tasks and re-iterations

·       Increased reliability of results

Better Business Decisions

Augmented analytics platforms are also adapting by introducing more role-based capabilities to cater to diverse user requirements. Additionally, the focus is on virtual self-services to capture detailed user behavior and deliver a personalized customer experience.

 

In summary, augmented analytics is revolutionizing data analysis and management by offering upgraded benefits and user-friendly experiences in the DSML and BI space. The constant research and strategic monitoring by Quadrant Knowledge Solutions keep track of this evolving trend and the numerous opportunities it presents for technology vendors and buyers.

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