With countless users generating massive volumes of digital records every day, more powerful and robust analytics and AI systems are needed to store it and make sense of it. We hear everywhere that business teams are hungry for analytics. They crave accurate forecasts and predictions to allow them to make more reliable business decisions. The complication is that data has become so complex that users have to wait for it to be presented and analyzed most of the time. They waste time waiting, and when the report arrives, it does not give any crucial insight they need, and the insights become too delayed to act upon.
Augmented analytics – with its potential to merge traditional data analytics with technologies such as machine learning (ML) or artificial intelligence (AI) and the subtle integration of NLP – can help with data preparation, insight discovery, sharing, deployment, and augment how users explore and analyze data in analytics and BI platforms. The next wave of BI tools and analytics will feel different with augmented analytics as it will continue to change the user experience across the entire BI process. Here’s how:
Data discovery, ingestion, analysis, predictions, and interactions between platforms will become more streamlined.
It will allow for easy share-ability and dissemination of results across integrated functions such as in-app messaging, chatbots, etc. Automate and democratize the whole data analytics/ BI process by driving insight-based decision making, providing action-oriented experiences, and reducing costs. It will also offer a more accurate understanding of what drives business performance and serve up insights that human users couldn’t have possibly imagined.
Augmented Analytics in Action
According to Gartner, augmented analytics marks the next level of disruption in the data and analytics landscape. A combination of data science, AI, and augmented analytics makes analytics accessible for more people within an organization, enabling them to ask relevant questions and automatically generate insights quickly and straightforwardly. Evaluating data analytics this way allows you to get the possible value out of AI as well. Augmented analytics systems recommend metrics for your business, and this can then be analyzed accordingly.
To explain augmented analytics in action, on the data preparation side, augmented analytics has the power to prepare data and analyze key insights automatically intelligently. If you gather a data point that indicates that revenue is down by 20% year over year, a deeper dive to uncover the true meaning behind it is essential. Augmented analytics helps put into perspective the reasons behind such a decrease – is it because marketing isn’t effective, or is it an industry-wide trend?
Augmented analytics considers everything from comparing relevant benchmarks, analyzing the geographical spread, and giving a commentary around it. Just knowing declining revenue doesn’t make the information valuable to your organization.
Drawing out the reason for the decline is what actionable insights provide – and communicating those insights with the organization can help convert them into actionable plans. Augmented analytics can help automatically deliver insights and even flag certain threshold breaches.
Advantages from Augmented Analytics
Currently, drawing insights from data remains a huge challenge for businesses. That is why almost all companies must invest in augmented analytics – it speeds up a time to value, makes the search easier, visualization faster, and data literacy more accessible across the organization.
From large enterprises looking to reduce their analytics load on their teams to a bank identifying the right age group to target for wealth management services, or from an e-commerce company detecting out-of-stock events automatically to a digital publishing house adapting to the order/relevance of news in their magazine based on user behaviors, the use cases for analytics are broad.
Key attributes of Augmented Analytics
Data preparation
The first big challenge that it solves is reducing the process that data analysts need to do repetitively every time they receive new data sets. Augmented analytics helps decrease the time it takes to clean data through the ETL process. It allows for more time to think about the implications of the data, find patterns and relationships, auto-generated code, create visualizations, and propose recommendations from the insights it derives. It automates not just the process of data preparation but also visualization and analysis.
For example, Insight Advisor in QlikSense is an intelligent assistant that enhances just about everything you do across the analytics lifecycle. Users can easily create and personalize based on their skill level using Insight Advisor – accelerating and automating the process when it comes to data preparation. This includes association recommendations – combining chart suggestions and different data sources and finding associations and inter-relations between columns.
Contextually-aware insights:
Analytics can now take into account intent and behaviors, in turn creating contextual insights. Based on questions, augmented analytics presents new ways of looking at data and identifies patterns and insights companies might have missed entirely otherwise, thereby enhancing human intellect and transforming the way you use analytics. Highlighting the most relevant hidden insights is a compelling capability.
For example, augmented intelligence and business intelligence consulting can help users manage the selection state (context) at the exploratory process step. It understands data values associated with or unrelated to that context, resulting in powerful context-aware and relevant suggestions.
Enablement of citizen data scientists:
Augmented analytics can relieve a company’s dependence on data scientists by democratizing data analytics and automating insight generation through ML/AI to convert insights into actionable steps – making analytics accessible to everyone.
As per Gartner, augmented analytics is the future of data analytics. It moves businesses closer than ever to that vision of “democratized analytics” because it will be easier, economical, and better.
Final Thoughts
Augmented analytics is apt for visualizing, communicating, and analyzing data as well as proposing actions. Shortly, augmented analytics will ascend to having an inherently social component. It will link analysis once insights have been identified and connect team members within the company to those findings. We will see augmented analytics systems become a more powerful productivity tool and an efficiency amplifier in the future.
