Core Capabilities Powering Augmented Analytics
Augmented analytics spans a range of technologies that work together to enhance how people consume, interact with and analyze data. Core capabilities include:
Automated Data Insights
Using techniques like natural language generation, augmented analytics solutions can automatically surface insights from data analysis in written narratives. This helps spotlight key trends and patterns for business users who can then dive deeper into visualizations.
Smart Data Visualizations
Convert raw datasets into interactive charts, graphs and dashboards optimized for visualization type, data structure and user intent. Augmented analytics enhances design through algorithms that learn user preferences.
Conversational User Interface
Ask questions about data in everyday language and receive spoken or text response with precise answers thanks to NLP. Extends access to enterprise data beyond traditional dashboards.
Data Preparation Automation
Automatically combines, cleans, enriches and formats relevant data for analysis. Machine learning handles up to 80% of repetitive, manual data prep tasks to boost productivity.
Embedded Advanced Analytics
Integrate predictive insights from machine learning models directly into business applications. Enables data-driven decisions as a seamless part of workflows.
Together, these capabilities create a smarter way for today’s data-driven organizations to extract maximum value from data through human-machine collaboration.
Industry Use Cases Driving Results
Augmented analytics is powering data-driven innovation across many industries. Real-world examples include:
Manufacturing: By analyzing IoT sensor data with machine learning, manufacturers can optimize predictive maintenance to minimize downtime and improve supply chain resiliency when issues occur. Siemens relies on augmented analytics in this capacity across 30+ factories.
Retail: Retailers like Starbucks and Domino’s leverage augmented analytics to unlock customer intelligence for one-to-one personalization. This increases average order value by 25-30%.
Public Sector: Cities like Los Angeles apply augmented analytics to optimize traffic flow based on real-time inputs including accidents, construction and volume. Benefits include reducing congestion and greenhouse emissions.
Financial Services: Banks use augmented analytics to enhance anti-money laundering efforts. NLP parses customer texts and conversations while machine learning models detect illegal activity to drive efficiency gains of over 40%.
Life Sciences: Leading healthcare companies rely on augmented analytics speed time-to-insight for clinical trials by 30-50%, accelerating development of life-saving treatments.
The common thread is leveraging human+machine capabilities to unlock productivity efficiencies, deeper insights, and optimized processes through data.
The Rise of Enterprise Augmented Analytics Strategies
According to recent research by Mckinsey, augmented analytics adoption grew over 65% from 2020 to 2021. And Gartner predicts enterprise augmented analytics software revenue will grow to $13 billion by 2025.
What’s driving global enterprises to embrace augmented analytics? A few key factors stand out:
Unprecedented Data Growth: IDC forecasts the collective sum of global data will grow to over 180 zettabytes by 2025, 10x levels in 2016. Traditional manual analytics cannot scale.
Need for Speed and Agility: Augmented analytics accelerates data-to-insight cycles from months to minutes. This allows organizations to respond rapidly to market changes.
Democratizing Analytics Across Roles: With intuitive NLP interfaces and AI assistance, more employees can access and apply data – not just highly technical analysts.
With the average employee spending over 18% of their workweek managing data, it’s clear why augmented analytics is becoming integral to long-term enterprise analytics strategy.
The rise of data is driving augmented analytics adoption (Image source: Geekflare)
Implementing Augmented Analytics Successfully
As an analytics advisor who has implemented over 50 enterprise AI solutions globally, I’ve seen firsthand what sets successful augmented analytics programs apart. Beyond the technology itself, leading organizations cultivate:
Strong Executive Sponsorship: Secure buy-in from senior leadership on data and analytics as a competitiveness driver before launching augmented analytics initiatives. Align to top priorities.
Trust in AI: Take an empathetic approach allaying concerns about job loss from AI automation. Stress the “augmenting” not “replacing” philosophy. Proactively identify job growth opportunities.
Talent Development: Reskill teams with AI and analytics training to optimize human+machine collaboration. Define new roles like Bot Manager.
Ethics Infrastructure: Establish responsible development processes for machine learning models to avoid biases and maintain transparency.
Get any one area wrong, and organizations struggle capturing the full value of augmented analytics. But those concentrating on the complete picture transform data-driven decision making.
The Future of Augmented Analytics
We are still just scratching the surface when it comes to augmented analytics capabilities. Rapid innovation continues expanding human-machine collaborative potential across:
Predictive Analytics: Natural language interfaces will enable more users to build forecasts without coding based on machine learning recommendations.
Smart Workflows: Nearly every business system will integrate analytics directly into workflows for frictionless, contextual insights.
Democratized Data Science: Automated modeling and NLP will empower business analysts to perform advanced analytics like leading data scientists.
Customizable Insights: Generative AI will create personalized data narratives tuned to individual user preferences and thinking processes.
Underpinning these exponential capabilities gains will be enterprise augmented analytics platforms converging previously disparate technologies into united solutions.
While change can be daunting, the opportunity of this data-centric era powered by augmented intelligence far outweighs the challenges for organizations ready to become true data leaders.
