AI and machine learning are transforming industries and improving how we use technology at an amazing pace, whether it’s accelerating drug discovery and therapeutics, allowing us to speak to our home appliances, or using facial recognition on our handheld devices. When it comes to business intelligence and data analytics, AI is also driving a new wave of innovation called augmented analytics that makes it easier for individuals of all technical skill levels to analyze volumes of data to accelerate more valuable business insights.
Augmented analytics uses machine learning, artificial intelligence, and natural language processing (NLP) techniques – such as natural language generation (NLG) and natural language query (NLQ) – to improve data preparation, the discovery of insights in data, and the operationalization and sharing of insights. Augmented analytics is modernizing the analytics experience by reducing manual data analysis, providing instant or near real-time insights, and making analytics accessible to more people. Organizations can leapfrog their analytics maturity, going beyond knowing what has happened in their business to learn why metrics change, how to use insights to impact business outcomes, and how to upskill the team members along the way. Let’s break down the essential components of augmented analytics.
Automated Generation of Insights
Faced with the ever-growing volume and complexity of enterprise data, automated generation of insights is essential in discovering important findings, patterns, and relationships across datasets. In fact, automation solves a big problem with “big data” that many organizations do not even know they have. Consider a dataset of just twenty columns or variables. In order to analyze up to four variables at a time to find the combinations that are correlated to a target metric, there are more than 6,000 combinations that you would have to visualize or evaluate.
With an automated process, it becomes easier to analyze all possible combinations of data instead of forming individual hypotheses and testing them by creating SQL query after SQL query to look across the data. In addition, you would be able to discover unknown data points you may not have thought of otherwise. Then, the system would be able to proactively push insights to you that you’re most interested in because it learns what data and metrics are important to you and your business. This augmentation represents the future of how analysts will get answers easier, iterate on insights discovery much faster, and uncover the reasons why metrics change, beyond simply monitoring high-level KPIs with dashboards.
A key part of modernizing the analytics experience and raising the adoption of analytics is natural language. A search and conversational interface where one can ask questions to get the information they need is becoming a preferred way to interact with data for ad hoc exploration, and the technology is only getting smarter, more anticipatory, and more forgiving over time. Coupled with the automated generation of insights, users can analyze data via search to not only visualize data but also get deep answers around why KPIs change and granular recommendations found in data, such as identifying customers who are most likely to respond to marketing offers. Natural language also plays a part in the narratives and data stories that are presented alongside data visualizations for automatically generated insights. Such narratives describe findings of interest, helping users understand the insight without having to interpret the visualization alone.
The shift to augmented analytics goes hand in hand with the shift to the cloud. The cloud enables massive storage of enterprise data, the availability of computing resources, and the elasticity necessary to handle the highly variable nature of analytic workloads as a greater number of people are involved in ad hoc data analysis. Unlike previous generations of business intelligence that solely relied on SQL queries for visualization, augmented analytics demands a distributed-capable architecture that leverages powerful data queries and complex machine learning processing.
In order to become an insights-driven organization in the year ahead, organizations should consider how to revamp their business intelligence strategies. Augmented analytics is powering a new wave of business intelligence through the use of AI-driven automation and natural language. With augmented analytics, organizations can accelerate their analytics maturity, speed the journey from complex data to better decision making, and free up valuable time for data specialists to work on high-value initiatives.
About the Author
Ajay Khanna, CEO & Founder of Tellius, a company disrupting business analytics with Search and AI, is a tech entrepreneur who has a passion for building disruptive enterprise products with an awesome user experience. Prior to starting Tellius, Ajay was CTO & Founding member of Celcite, a fast growing telecom analytics and solutions company, that was acquired by Amdocs. Ajay has over 25 years of extensive experience working in various technical, business. and consulting roles. He holds degree in Electronics and Communications Engineering.
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