As the volume and variety of data continue to grow, so does the potential for this data to improve efficiency, create better customer experiences, and drive the bottom line.
Doing so, however, requires businesses to look for new ways to analyze and make use of that data. Legacy approaches that require desktop tools, complex data visualization software, or coding skills prevents the majority of business users from making the most of their company’s data.
One approach that is gaining popularity is augmented analytics – a process that uses machine learning and artificial intelligence to help data professionals and business users alike make sense of data faster and more easily.
Augmented analytics is a type of analytics that uses Artificial Intelligence (AI) to assist or automatically prepare data, analyze it, identify trends and patterns, and visualize these for end users. This makes it easier for business users, most of whom are not analytics engineers, data analysts, or data scientists, to make better decisions based on data. Augmented analytics can be used by businesses of all sizes and in all industries to improve their decision-making processes. Augmented analytics is different from traditional analytics in that it does not require users to have significant experience working with data. This makes it ideal for businesses who want to bring self-service analytics to their organizations and empower business users to conduct more analysis on their own, without requiring extensive training or technical proficiency. Augmented analytics is changing the way businesses use data to make decisions, and when implemented effectively, will continue to revolutionize the way businesses operate.
Augmented analytics platforms make it possible for all kinds of users to easily ask data questions, identify patterns and correlations, predict future outcomes, and share these insights with others. This type of platform can help users to make better data-driven decisions by providing insights that would otherwise be difficult to obtain or missed entirely. Augmented analytics platforms are particularly well-suited for data-rich industries such as financial services, healthcare, manufacturing, technology, and retail.
Murali Vridhachalam, Head of Enterprise Data and Analytics at Gilead Sciences, in an episode of The Data Chief said, “[with] the convergence of data, AI, and cloud…we have a lot of enterprise data on the cloud, and we will apply AI and machine learning on top of it. It yields special use cases [that were] never before possible because data was always in silos.”
In these industries, the sheer volume of data makes it difficult for human analysts to parse all the data available to identify all of the relevant factors that could impact a decision. Augmented analytics platforms can sift through this data quickly and effectively to surface the most important insights, detect anomalies, and highlight trends. As a result, businesses that make use of augmented analytics are able to gain a competitive edge by making better-informed decisions.
Augmented analytics can be used for a variety of tasks, automating data discovery, including predictive maintenance, fraud detection, customer segmentation, supply chain optimization, and more. By automating the process of data discovery, augmented analytics can help data teams save time and increase their efficiency, while simultaneously increasing the impact of data within their organization.
As Cristian Felix, VP of product at Accern, put it, “data and AI are not here to replace humans, but to augment and support them to make better decisions.”
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.
HP leveraged their modern data stack to democratize access to business insights. By leveraging AI, ThoughtSpot goes beyond answering known questions, detecting trends, and identifying anomalies and patterns, to suggesting new questions users wouldn't think to ask.
Augmented analytics can help businesses better understand their customers’ behavior by providing a robust, 360 degree view of customer data. It can identify patterns and trends that would be difficult to find using traditional methods.
In the case of Clarity, a business travel company, ThoughtSpot’s augmented analytics helped business users see patterns in customer behavior that they didn’t know to look for. Darren Williams, Head of Management Information and Data shared, “We noticed that one of our customers’ travel habits altered dramatically. Due to a change in role they started traveling from Heathrow to Belfast 2-3 times a week. Since the system spotted this new behavior quickly and flagged it up we started booking their trips 8+ days in advance and were able to generate 15 percent in savings.”
This information can be used to improve customer service, target marketing efforts, and develop new products and services.
Augmented analytics can help companies not only understand what is happening today in their business, but what is likely going to happen next. By using AI to identify patterns in data or incorporating machine learning models, augmented analytics can provide more accurate predictions than traditional methods. In addition, augmented analytics can help to automate the process of making predictions, making it faster and easier to generate reliable results. As a result, augmented analytics has the potential to revolutionize the field of data analytics and improve predictions across a wide range of applications.
In order for businesses to benefit from augmented analytics, they must have high-quality data. This can be a challenge for businesses that do not have extensive experience working with data. Data quality is essential for augmented analytics because it ensures that the AI and ML algorithms have accurate information to work with. Poor data quality can lead to inaccurate results and suboptimal decision-making, which in turn can hinder user trust. To ensure high-quality data, businesses should adopt the right data governance framework to ensure their data is organized and ready for analysis. Fortunately, many tools in the modern data stack work hand in hand to address data quality concerns. With clean and well-organized data, businesses can reap the benefits of augmented analytics.
Augmented analytics can be complex, so users need a platform with a familiar interface to drive real adoption. Augmented analytics can be a great asset for any organization, but it's important to understand that if your users cannot use your tools, it doesn’t matter how robust or complex analysis it can handle.
In order to get the most out of an augmented analytics platform, users will need to understand the basics of data. This doesn’t mean that users should become technical experts. Instead, they will need to understand the fundamentals of data. By pairing an augmented analytics platform with data literacy programs, users of augmented analytics platforms will be able to get the most out of their investment and reap the benefits of this rapidly growing field.
With all of these benefits, it’s no wonder that augmented analytics is on the rise. If you’re looking for an AI-Powered Analytics solution to help you make better data-driven decisions, be sure to check out ThoughtSpot. Our intuitive search and AI-powered analytics can help you get the most out of your data with little effort. Sign up for a free trial today and see what live analytics can do for you.