Augmented Analytics: Transforming Business Intelligence with Advanced Insights

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In today’s data-driven world, organizations are constantly seeking ways to harness the power of data to make informed decisions, uncover opportunities, and address challenges. However, as the volume of data continues to grow, traditional business intelligence (BI) techniques are proving insufficient for managing and extracting valuable insights. Augmented analytics, a revolutionary approach that leverages artificial intelligence (AI) and machine learning (ML), is addressing this challenge head-on. In this article, we will delve into the concept of augmented analytics and explore its significant role in transforming business intelligence.

Augmented Analytics:

Empowering Users with Advanced Insights: Coined by Gartner, a renowned technology research and consulting firm, augmented analytics represents the next evolution in analytics. This cutting-edge technology empowers both business users and data scientists to extract meaningful insights from unstructured data by utilizing AI and ML techniques.
Augmented analytics enables data scientists to explore data without preconceived notions about the interrelationships within the data. Consequently, businesses no longer require specialized expertise to create and manage advanced analytics models. Moreover, this technology empowers data scientists and developers to generate valuable content on machine learning and data science, enabling them to focus on creative work and produce more accurate and realistic models.

Augmented Analytic’s Impact on Business Intelligence (BI):

Augmented analytics plays a crucial role in reducing manual efforts, automating data scientists’ tasks, and generating valuable insights while minimizing inconsistencies and errors. It facilitates decision-making processes, enhances transparency, and transforms customer engagement with data. Let’s explore the three critical stages of business intelligence that typically involve manual operations and are prone to errors.

1. Data Preparation:

Advanced analytics systems excel at handling large volumes of data. However, data cleansing and preparation, a necessary step before analysis, often require manual intervention by data scientists. They must create metadata, perform data profiling, modeling, data quality checks, and data manipulation, which increases the likelihood of human errors.

Augmented analytics automates the data preparation process by leveraging machine learning capabilities. Machine learning algorithms can identify relevant information, provide optimal data profiling techniques, and streamline the data refinement process. As a result, the speed of data preparation improves, and data scientists become more productive.

2. Identifying Data Patterns:

While modern analytic systems can identify and analyze various patterns and relationships in data, there is a risk of overlooking crucial trends and distortions that impact businesses, especially as the data’s size and complexity increase.

Data exploration often occurs with biased elements and preconceptions, and exhaustive permutations and combinations must be examined, making the process time-consuming and prone to oversight. Augmented data discovery overcomes these challenges by employing powerful algorithms to automatically identify relationships and data outliers, ensuring that critical insights receive the necessary attention.

3. Utilizing Data Insights:

Modern BI platforms provide highly interactive and visual dashboards. However, interpreting and extracting meaningful information from these dashboards is not always straightforward for all users. Augmented analytics addresses this issue by incorporating Natural Language Generation (NLG) capabilities to inform users about noteworthy observations, making insights more accessible and actionable.

The Benefits of Augmented Analytics:

Augmented analytics offers numerous benefits that drive better decision-making, broaden access to analytics for employees, and enhance the agility of the analytical process. It enables faster data exploration while mitigating the risk of false findings. Additionally, a wide array of algorithms ensures that critical insights are identified and given the necessary attention.
Augmented analytics promotes greater accessibility to analytics tools within organizations. For instance, when order management teams have access to analytics, they can optimize data to improve customer satisfaction, while finance and accounting teams can gain insights to enhance financial performance.
According to Gartner, the growth of citizen data scientists is expected to outpace that of professional data scientists, highlighting the growth potential of augmented analytics.


With the exponential growth of IoT devices and the resulting vast volumes of data, AI-powered analytical tools are essential for extracting the full potential of this data. Augmented analytics platforms have become a critical need for businesses, enabling them to uncover meaningful insights, make critical decisions, and drive success. By automating vital components of insight generation, enhancing data governance, improving data scientists’ productivity, and reducing costs, augmented analytics is revolutionizing the world of business intelligence. Embracing this transformative technology will empower organizations to thrive in the data-driven era.