The traditional approach to Business Intelligence (BI) involves collecting and analyzing data to derive insights that can inform business decisions. However, this approach has several limitations, including the need for specialized skills and the inability to handle large volumes of data.
HostReview has reported that the field of data analytics is experiencing rapid expansion, with global data volumes projected to reach 181 zettabytes by 2025, according to Statista. The true value of this massive amount of raw data can only be unlocked through effective analysis.
Augmented Analytics offers a new approach that leverages automation and machine learning (ML) to address these limitations.
In this article, we will explore the evolution of Business Intelligence from traditional BI to Augmented Analytics, discussing the benefits of this new approach and providing examples of its implementation across various industries.
Understanding Augmented Analytics
Augmented Analytics is a key feature of any modern analytics platform. These systems use computerized algorithms and machine learning approaches to improve data analysis.
By automating several tasks involved in data analysis, including data preparation, analysis, and visualization, analytics platforms powered by Augmented Analytics can reduce the time and effort required to analyze data and improve the accuracy of insights.
According to Comparative, augmented Analytics is designed to make data analysis more accessible to a broader range of users, including those without specialized skills in data analysis. This is achieved by automating several tasks involved in data analysis, making the process less reliant on human intervention.
By reducing the need for manual data preparation, analysis, and visualization, Augmented Analytics can make data analysis more efficient, accurate, and accessible.
Benefits of Augmented Analytics
Augmented Analytics offers several benefits over traditional BI approaches to data analysis, including:
- Increased Efficiency:
Augmented Analytics can automate several tasks involved in data analysis, reducing the time and effort required to analyze data. This can allow organizations to analyze more data in less time, allowing them to make more informed decisions more quickly.
- Improved Accuracy:
By leveraging machine learning techniques, Augmented Analytics can improve the accuracy of insights derived from data analysis. This is achieved by allowing the system to learn from previous data analysis, enabling it to improve its accuracy over time.
- Increased Accessibility:
Augmented Analytics can make data analysis accessible to a broader range of users, reducing the need for specialized skills in data analysis. This can empower more people within an organization to make data-driven decisions.
- Real-Time Insights:
Augmented Analytics can provide real-time insights, allowing businesses to make faster and more informed decisions. This is achieved by automating several tasks involved in data analysis, making it possible to analyze data in real time.
Augmented Analytics in Action
Augmented Analytics is already being used by several leading organizations across various industries.
For example, financial services companies are using Augmented Analytics to analyze large volumes of financial data and identify fraud patterns.
Retailers use Augmented Analytics to evaluate consumer data and create customized marketing campaigns. Augmented Analytics is being used by healthcare organizations to evaluate patient data and enhance patient outcomes.
The BBN Times recently published an article emphasizing the importance of making accurate decisions in order for businesses to succeed.
Many businesses struggle to improve their decision-making processes, but by combining the contextual business knowledge of humans with augmented analytics, retailers can make better decisions that lead to business growth.
Augmented Analytics vs. Traditional BI
Augmented Analytics and traditional Business Intelligence approaches are both used to analyze data, but they differ in several important ways. Below we have discussed some ways in which they differ:
- Data Preparation:
One of the primary differences between Augmented Analytics and traditional BI is the role of data preparation. Traditional BI requires human analysts to manually prepare data, while Augmented Analytics automates data preparation, reducing the need for human intervention.
- Data Analysis:
Traditional business intelligence requires human analysts to manually examine data, whereas Augmented Analytics analyzes data using automated algorithms and ML techniques.
- Data Visualization:
As per a post by TechTarget, data visualization is central to businesses because it helps users comprehend complex data sets quickly. Traditional BI requires human analysts to create visualizations, while Augmented Analytics automates data visualization, reducing the need for human intervention.
Traditional BI can be complex and require specialized skills, while Augmented Analytics is designed to be more accessible to a broader range of users.
By automating several tasks involved in data analysis, Augmented Analytics can reduce the need for specialized skills and make data analysis more accessible to non-technical users.
In essence, Augmented Analytics is an advanced data analysis approach that automates data collection, analysis, and visualization using machine learning algorithms.
By reducing time and effort in data analysis, improving accuracy, and providing real-time insights, Augmented Analytics outperforms traditional BI approaches in terms of efficiency, accessibility, and accuracy.
The evolution of Business Intelligence from traditional approaches to Augmented Analytics marks a significant shift in the way organizations approach data analysis.
With the increasing volume of data generated by businesses today, Augmented Analytics has emerged as a powerful tool to help businesses make faster, more informed decisions.
By automating several tasks involved in data analysis, Augmented Analytics has the potential to reduce the need for specialized skills and make data analysis more accessible to non-technical users.
As businesses continue to adopt and integrate Augmented Analytics into their operations, we can expect to see a greater emphasis on data-driven decision-making and more efficient use of resources.