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Augmented analytics, powered by artificial intelligence, will change everything about the analytics and business intelligence processes, by simplifying, improving, or radically changing them. By integrating artificial intelligence and natural language processing elements with traditional BI processes, augmented analytics will transform the insurance end-customer experience by data curation, revealing new insights, and making relevant information easily accessible 24X7.
Data is a gold mine that powers the intelligent insurance enterprise. Analytics and business intelligence (BI) act as core enablers for mining both physical assets and digital business opportunities, thus improving accuracy, increasing efficiency, and augmenting the ability of employees to deliver business value. But though insurers continue to collect data, its real potential remains untapped. Traditional analytics and BI tools are primarily rule-based and prone to human bias, thus falling short of providing quick, relevant, and actionable insights to business users.
Augmented analytics has the potential to help insurers get the most out of data. It involves an intense mix of artificial intelligence (AI), often in the form of machine learning (ML) and natural language processing (NLP), and traditional analytics. Augmented analytics is a huge step forward from traditional analytics or BI tools because the AI technologies involved are continuously working at learning and enhancing results. Augmented analytics allows faster access to insights derived from massive amounts of structured and unstructured data, which helps unbridle insights from any kind of bias.
This article takes a deep dive into the new world of augmented analytics that is poised to unlock enormous opportunities for insurance enterprises and is powered by impartial contextual awareness and actionable insights.
Evolution Of Analytics and BI
Every decade has turned a new chapter in analytics and BI technologies. From rule-based platforms to visual-based data discovery platforms, the core drivers of the modern analytics and BI market have always been relevant information, speed of delivering insights, self-service, and ready-to-consume analysis.
Augmented analytics is characterized by the AI/ML-powered automation of the insight discovery, exploration, curation, and explanation process. It’s a defining feature of new-generationanalytics technology, allowing users to simultaneously apply a range of algorithms and collaborative learning to data, explain actionable findings and reduce the risk of missing important insights extracted from the data.
Ushering in the New Paradigm
Augmented analytics simplifies the traditional manual intensive analytics process by automating Data collection, curation, and Analysis to build insights.
The Emerging Power of Augmented Analytics
Augmented Analytics and Conversational AI are positioned to bring in next major Industry revolution. Gartner* report has also marked it as the next wave of disruption in the data and analytics space. By 2021 conversational analytics and NLP will boost analytics and BI adoption from 32 percent to more than 50 percent of an organization, while 50 percent of analytical queries will be generated via plain text-based search, Voice, NLP or will be automatically generated. AI will also enable automation of data science tasks and facilitate citizen data scientists to produce a higher volume of advanced analysis, than specialized data scientists.
*Source- Augmented Analytics Is the Future of Analytics – Gartner
Business Value of Augmented Analytics
Augmented analytics use ML algorithms to automate the data and analytics processes, significantly reducing the time-consuming exploration, explanation, prediction, and prescription analytics process as well as contextualizing the insights to user personas, as shown in below diagram:
The principle behind augmented intelligence is to complement human intelligence as force multiplier by speeding-up repetitive tasks and enable businesses to take faster and smarter decisions. As a result, allowing data scientists and analysts to focus on solving more complex queries and data science projects, offering critical business insights to the relevant stakeholders.
Augmented analytics should be viewed as an always-on, immersive system that guides key stakeholders from issues to visions and decisions in a tenacious environment of LOBs, things, teams, and locations. For Insurers, this translates to empowering business and Leadership stakeholders with the power of AI/ML and advanced analytics to improved decision making, increased efficiency, while reducing cost and fuel innovation.
Key use cases Augmented analytics is solving for various personas across the Insurance value chain: