Augmented business intelligence is a powerful tool that can unlock hidden correlations between variables and datasets, and provide new insight into business performance. Gartner coined the term “augmented capabilities” in 2017 to describe this new technology, and it has been described as a new way to access information and create actionable insight. According to the Business Application Research Center, many companies still rely on hunches and experience when making business decisions. In fact, 58% of respondents claim that their companies rely on guesswork at least 50% of the time. And 70% of these companies lag behind market performance and are not even in the top tier.
Benefits of augmented business intelligence
Augmented business intelligence provides the benefits of BI, without the complexity of traditional analytics. By automating the process of data preparation, quality assurance, and recommendations, augmented analytics can provide clean, fast insights. In addition, it minimizes analytical bias and provides a broad set of data for analyzing. It is particularly beneficial for large data sets with high dimensions. Moreover, augmented analytics enables direct collaboration between team members. For example, employees can work together on a multi-departmental project through a dashboard that is shared across teams.
Augmented business intelligence combines artificial intelligence and machine learning to help companies analyze and understand data faster. Machine learning algorithms help organizations to clean and shape data to improve their decision-making process. They can even surface recommendations based on advanced algorithms. Some augmented analytics applications are also capable of learning user preferences and semantics, which allows users to analyze data more easily.
In addition to democratizing access to granular data, augmented analytics also facilitates faster decision-making. As a result, companies can reduce analysis time and cost by up to 98%. Moreover, these systems can be configured to work in real-time, allowing them to process data around the clock.
Machine learning is a form of artificial intelligence that is able to analyze massive amounts of data and identify patterns. This technology is useful in a variety of industries. Its capabilities are growing all the time. For example, it can recognize complex relationships and identify factors that influence brand health. This is information that humans would never be able to recognize on their own. Using this technology, companies can improve business intelligence systems.
By automating analysis, augmented analytics reduces the amount of work analysts need to do. Instead of spending days or weeks analyzing large data sets, users can just ask the tool to generate insights. This allows for immediate action. In addition, augmented tools use natural language technologies to simplify the data discovery process for business users.
The use of artificial intelligence in business intelligence is becoming a common practice for companies looking to make better business decisions. Machine learning algorithms have the ability to automatically detect and analyze data sets. This can help identify anomalies in the data and help companies make better decisions. Moreover, this technology can also help businesses detect patterns in data and predict future events.
Natural language processing
Artificial intelligence technologies such as natural language processing are transforming the process of analytics. They automate and simplify many analytics processes, and they also enable untrained users to ask questions about data. These technologies are now available for businesses and analysts to use, and they are considered to be the future of BI and data analytics.
Augmented analytics can help companies make better business decisions, reduce the analytic burden on their teams, and surface new opportunities and risks immediately. It is especially useful for industries with a lot of data and complex processes. With the help of augmented analytics, business leaders can quickly answer relevant questions and transform their businesses. The software allows users to interact with data in natural language, providing guided search suggestions and instant answers. The results come in the form of rich data visualizations.
Another advantage of using NLP is its ability to process large amounts of text data and analyze the meaning of it. This can help a company improve customer satisfaction by automatically sorting customer service tickets by sentiment and intent. This frees up employees to perform higher-value tasks. Moreover, NLP helps businesses make better decisions by analyzing unstructured text and delivering actionable insights.
Examples of use cases
Augmented business intelligence (ABI) has a number of uses in the enterprise, including automating complex data analysis. It reduces the amount of time required to gather data from multiple sources. It automates tasks such as joining and transforming data and can be set up to run automatically. It can also deliver insights instantly, allowing you to act on them quickly.
One example of augmented business intelligence in action is in the airline industry. Ryanair used an augmented advisor that can analyze 52,000 documents to answer their client’s questions. They used this information to help the relationship manager respond to client queries. Using this technology, they were able to improve the satisfaction of their customers and create more opportunities for cross-selling and up-selling.
Despite the hype around augmented analytics, many businesses still rely on experience to make business decisions. A recent survey by the Business Application Research Center shows that 58% of companies still rely on their hunches and intuition to make business decisions. Another example of augmented analytics in action is in the banking industry. This industry had previously targeted older customers for its wealth management services, but with augmented analytics, they could analyze high volumes of data and ask deeper questions. Using augmented analytics, they were able to find out that 20 to 35-year-olds were the most likely to engage with their wealth management services.