Business data is becoming increasingly complex and difficult to interpret and analyze. Augmented analytics leverages emerging technologies like machine learning (ML) and artificial intelligence (AI) to deliver insights faster and improve decision making.
What is Augmented Analytics?
Augmented analytics, powered by AI and ML, enables you to interact with data at a contextual level. Augmented analytics delivers advanced analytical capabilities such as recommendations, and predictive insights, to all your employees. Gartner research says that “Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms.”
Both business users and senior executives can use augmented analytics to get more value from their data, without the need for specialized skills or IT expertise. Augmented analytics can help you find relevant data, ask the right questions, and get personalized and contextual insights.
Benefits of Augmented Analytics
Augmented analytics can make your data analysis faster and more accurate with the help of AI and ML. You can also make analysis easier for business users by removing technical barriers, and ensuring that advanced techniques are available to people without advanced technical skills.
AI-powered augmented analytics accelerate time to insight by providing the right data to the right people at the right time, and recommending next steps. You can track user behavior to provide smarter recommendations and a more personalized experience over time. By enabling people to get insights faster, you can help them focus on more strategic activities and reduce the time spent sifting through data.
AI and ML powered systems are best suited for repetitive tasks and calculations so you can automate a lot of manual tasks and find hidden patterns and insights faster. You can eliminate human error, improve accuracy, and make informed decisions that are backed by extensive analysis.
AI and ML can easily automate tasks that are highly specialized and repetitive tasks like content recommendations or fraud detection. Augmented analytics can automate most of the tedious cumbersome tasks associated with data, saving a lot of time and effort required for repetitive tasks involved in data discovery, data preparation, data management, statistical analysis, etc.
Use Cases of Augmented Analytics
- Sales teams can use augmented analytics to discover hidden trends in their targets, opportunities, and deals
- Senior executives can use augmented analytics to dive into real-time data during management meetings, instead of presenting static reports
- IT teams can use augmented analytics to find the reasons and prepare for sudden spikes in system or server usage
- Data analysts and data scientists can use augmented analytics to prepare and enrich data faster for analysis
- The supply chain and logistics industry can use augmented analytics to figure out the root causes for fluctuations in supply and demand
- The travel and tourism industry can use augmented analytics to optimize their customer outreach and offer a more personalized experience to their customers
- The healthcare can use augmented analytics to reduce patient wait times, deliver personalized medicine, and improve the overall patient experience