Augmented analytics is a class of analytics that uses machine learning (ML) and artificial intelligence (AI) to assist with data preparation, insight generation, and explanation. It expands the human ability to interact with data in analytics and BI platforms. It improves how people explore and understand data in analytics and BI systems by assisting with data preparation, insight production, and explanation.
Augmented analytics transforms how analytics can be built, consumed, and shared by automating the process of analyzing data and providing insights that humans can use to make better decisions. In other words, Augmented Analytics helps businesses to gain deeper insights into their data by using AI and ML algorithms to analyze large amounts of data quickly and accurately.
How does Augmented Analytics differ from Traditional BI?
Traditional BI tools have several limitations that can hinder their effectiveness. One of the critical shortcomings of traditional BI tools is that they often fail to dive down into the details of data and key metrics from throughout the organization, making it difficult to make decisions when necessary. Additionally, traditional BI solutions can be inefficient, costly, and lack value and insightful data. They are also not fast or agile enough to keep up with today’s unpredictable and fast-moving business environment. Other disadvantages of traditional BI include high initial costs, data manipulation issues, fightback from employees, data integration challenges, and limited customizability.
Augmented analytics differs from traditional BI in several ways. Traditional BI tools produce fixed reports and dashboards as outputs, while augmented analytics platforms help users interact with data dynamically with capabilities like search, natural language narrations, and even audio-visual data stories. Augmented analytics uses machine learning (ML), artificial intelligence (AI), and various natural language processing (NLP) to speed up insights with fewer manual efforts. It reduces the manual labor of traditional BI by automating the process of data analysis and providing insights that humans can use to make better decisions. Augmented analytics also provides many advanced features such as predictive modeling, forecasting, and anomaly detection to name a few.
What kinds of data can be accessed with Augmented Analytics?
Augmented analytics enables companies to examine data sources and perspectives to provide users with greater insight into their data. This approach can be used to analyze data from multiple perspectives, including but not limited to quantitative and qualitative methods. Augmented analytics tools and software bring analytical capabilities such as recommendations, insights, or guidance on a query to more types of data, including structured and unstructured data. Augmented analytics uses enabling technologies such as machine learning (ML) and artificial intelligence (AI) to assist with data preparation, insight generation, and insight explanation. It can help businesses automatically prepare new analytics reports based on historic data. Augmented analytics can process and analyze key data such as customer acquisition costs, loan requests, and other financial services-related information.
Which industries can benefit from Augmented Analytics?
Augmented analytics platforms are particularly well-suited for data-rich industries such as financial services, healthcare, manufacturing, technology, and retail. Augmented analytics can help energy and utility companies improve customer service, asset management, balance loads, predict maintenance needs, and more. Augmented intelligence examples in retail include capitalizing on customers’ shifting needs while in financial services, Augmented analytics is used to help with fraud detection, customer segmentation, risk management, and investment analysis.
In conclusion, Augmented analytics is a type of analytics that employs machine learning (ML) and artificial intelligence (AI) to aid with data preparation, insight generation, and explanation. It enables businesses to gain deeper insights into their data by analyzing vast amounts of data rapidly and accurately using AI and ML algorithms. Businesses of all sizes and industries can use augmented analytics to improve their decision-making processes. It’s becoming more popular as a tool for translating data into useful insights for decision-making. Augmented analytics is the future of business intelligence since it processes and analyses massive amounts of internal and external company data, identifying trends, revealing insights, and enhancing data-driven actions. It also provides the ability to embed analytics in standalone widgets, workflows, or a native app giving users across the board access to insights from the data.
