Is augmented analytics the future of business intelligence?
Yes, augmented analytics is considered the future of business intelligence (BI). Here is a more detailed explanation of why this is the case:
Democratizing Data Analysis:
Augmented analytics is democratizing data analysis, allowing more people to use data to make informed decisions. It provides business users with selfservice analytics tools that are easy to use and can deliver insights in real-time. This means that employees across an organization can access and analyze data without requiring specialized training or expertise in data analysis. This democratization of data analysis empowers business users to make informed decisions that are backed by data, which can lead to better business outcomes.
Automated Data Analysis:
Augmented analytics automates the data analysis process, reducing the time and effort required for data analysis. With traditional BI tools, data analysts spend a significant amount of time preparing data for analysis. Augmented analytics automates this process, allowing data analysts to focus on more strategic tasks such as interpreting the results of data analysis. This automation also allows businesses to analyze data in real-time, providing a more up-todate view of their operations.
AI and ML for Data Analysis:
Augmented analytics leverages advanced technologies such as artificial intelligence (AI) and machine learning (ML) to analyze data. These technologies enable businesses to identify patterns, anomalies, and relationships in data that may not be visible with traditional BI tools. For example, ML algorithms can analyze large volumes of data to identify trends and patterns that may be difficult to identify using traditional methods.
NLP for Data Analysis:
Augmented analytics also leverages natural language processing (NLP) to enable business users to interact with data using natural language queries. This means that business users can ask questions about the data in the same way they would ask a colleague, without requiring specialized training or expertise in data analysis.
AI-Assisted Decision Making:
Augmented analytics can also assist in decision making by providing AIpowered recommendations based on the results of data analysis. For example, an augmented analytics solution may recommend a course of action based on an analysis of sales data, allowing business users to make informed decisions quickly.
Overall, augmented analytics is transforming the way businesses approach data analysis and decision-making. By automating and streamlining the data analysis process and leveraging advanced technologies such as AI, ML, and NLP, augmented analytics is making data analysis more accessible and efficient for business users. As the amount of data generated by businesses continues to grow, augmented analytics will become an essential tool for businesses looking to gain a competitive advantage by making data-driven decisions.