Why Augmented Analytics is the Future of Business Intelligence
With countless users generating massive volumes of digital records every single day, more powerful and robust analytics and AI systems are needed to store it, and make sense of it.
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.
With augmented analytics, the next wave of BI tools and analytics will feel different 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 provide a more accurate way of understanding 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, automatically generate insights in an easy and straightforward manner. Evaluating data analytics this way allows you to get the most 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 intelligently prepare data and analyze key insights automatically. Let’s say 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 important. Augmented analytics helps put into perspective the reasons behind such a decrease – is it because marketing isn’t effective, or is it because it is an industry-wide trend?
Augmented analytics takes into account everything from comparing relevant benchmarks, analyzing the geographical spread as well as giving a commentary around it. Just having knowledge of 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 into actionable plans. Augmented analytics can help automatically deliver insights and even flag certain threshold breaches.
Benefitting from Augmented Analytics
Currently, drawing insights from data remains a huge challenge for businesses, and that is why it is considered so important for almost all businesses to invest in augmented analytics – it speeds up time to value, makes 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 on the basis of user behaviors, the use cases for analytics are broad.
Key Capabilities of Augmented Analytics
The first big challenge that it solves is by reducing the process that data analysts need to do repetitively every time they receive new data sets to work with. 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-generate 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. When it comes to data preparation, users can easily create and personalize based on their skill-level using Insight Advisor – accelerating and automating the process. This also includes association recommendations – to combine chart suggestions, different data sources and find associations and inter-relations between columns.
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 completely missed otherwise; thereby enhancing human intellect and transforming the way you use analytics. Highlighting the most relevant hidden insights is an extremely powerful capability.
For example, augmented intelligence can help users manage the selection state (context) at step of the exploratory process, and it understands data values that are both associated with or unrelated to that context, resulting in powerful context-aware and relevant suggestions.
Enabling a citizen data scientist:
Augmented analytics can relieve a company’s dependence on data scientists by democratizing data analytics and automating insight generation through the use of ML/AI to convert insights into actionable steps – making analytics accessible to everyone.
According to Gartner, augmented analytics is the future of data analytics because it moves us closer than ever to that vision of “democratized analytics” because it will be cheaper, easier, and better.
The Future of Augmented Analytics
Augmented analytics is capable of visualizing, communicating and analyzing data as well as proposing actions. In the near future, augmented analytics will ascend to have an inherently social component. It will link analysis once insights have been identified, and connect team members within the company to those findings. Going forward, we will see augmented analytics systems become a more powerful productivity tool and an efficiency amplifier.