As businesses continue to collect massive amounts of data, the need to derive insights and value from that data has become increasingly important. Augmented analytics is a rapidly growing area of data analytics that leverages machine learning and artificial intelligence to help businesses extract insights from data more efficiently and accurately. In this blog, we will explore the what and why of augmented analytics.

What is Augmented Analytics?

Augmented analytics refers to the use of machine learning and artificial intelligence to automate and enhance the process of data analysis. It involves the use of algorithms and statistical models to identify patterns, relationships, and anomalies in data, and to generate insights and predictions based on that analysis. Augmented analytics can help businesses extract insights from data more quickly and accurately and to make data-driven decisions more efficiently.

Why is Augmented Analytics Important?

Time-saving

Nova Techset Augmented analytics can help businesses save time by automating repetitive and time-consuming tasks such as data cleansing, data modeling, and data visualization. This allows data analysts to focus on more complex and high-value tasks such as identifying trends, making predictions, and providing insights.

Improved Accuracy

Augmented analytics can also help improve the accuracy of data analysis. By using machine learning algorithms to identify patterns and relationships in data, augmented analytics can help reduce the risk of errors and biases that may be introduced by human analysts.

Deeper Insights

Augmented analytics can help businesses gain deeper insights into their data. By analyzing large datasets and identifying patterns and trends that may not be visible to the human eye, augmented analytics can help businesses uncover hidden insights that may be missed by traditional data analysis techniques.

Better Decision-making

By providing more accurate and deeper insights into data, augmented analytics can help businesses make better-informed decisions. This can lead to improved business outcomes, increased efficiency, and reduced risk.

In conclusion, augmented analytics is a rapidly growing area of data analytics that leverages machine learning and artificial intelligence to automate and enhance the process of data analysis.

By saving time, improving accuracy, providing deeper insights, and supporting better decision-making, augmented analytics can help businesses extract more value from their data and gain a competitive advantage in an increasingly data-driven world.