According to Gartner, “Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation, and insight explanation to augment how people explore and analyze data in analytics and BI platforms. It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management, and deployment.”
Leading analyst Ventana Research in a recent benchmark report, declares, “the use of artificial intelligence (AI) and machine learning (ML), and what is promised in augmented analytics, is limited to one-quarter of organizations but growing and is planned to be addressed in an additional one-third (34%).”
TechTarget has called ours the “new era of BI,” marking the end of the self-service tools that have defined BI in recent times: “After the era of self-service analytics, it’s now the era of augmented analytics.”
Augmented analytics enables organizations to speed up their journey from data to decisions. As data volumes and complexity explode, it’s become an indispensable component of the data and analytics workstream. Machine learning—one of the foundational drivers for augmented analytics—has the potential to reveal tremendous opportunities and perspectives that traditional BI tools fail to uncover.
With augmented analytics, instead of requiring your data scientists to provide analytics for other employees, they can work on what they were hired to do; business experts analyze their data on their own unassisted.
While a data scientist can mine and analyze data for personal use (or analyze data from parameters provided by colleagues who don’t have advanced technical skills), front-line workers typically don’t have the skills needed to do this.
However, while they can use their domain expertise and industry experience, they still need to harness data to make data-driven decisions. However, the problem is they don’t always have the skills to harness that data, manipulate it as they see fit, and then derive insights from it.
Augmented analytics changes all that, delivering automated insights that drive people to make better decisions. Forrester Research defines augmented analytics as “insights infused with AI.” Forrester argues that data is only augmented when business intelligence‒which delivers data in consumable dashboards and reports‒is actionable and integrated into business applications for decision-making purposes.
When you simplify analytics through augmented analytics, you can enable anyone to come to the table informed. The effect on the overall organization is positive.
Gartner’s definition above states, “…It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management, and deployment.” This speaks to the core value of augmented analytics and its relationship with AI.
During data collection, all the way to data visualization, AI will prompt front-line workers and C-suite executives alike to make better decisions with their data. AI-driven insights, once deployed, provides a business-friendly mechanism to aid in the decision-making process.
When requirements consist of nothing more than a secure web browser and the right software, you can scale adoption for any person at any time. One-off requests become the exception and not the rule; businesspeople can then collaborate on common goals and shared information.
Pyramid Analytics CEO Omri Kohl writes, “When you … empower decision-making at all levels of your organization, you create an undeniable foundation for long-term business success.”
Machine learning helps businesspeople and organizations find opportunities that are not immediately apparent within the data. Machine learning can sense patterns running through the data that non-technical workers cannot discern on their own: they get prompted to see more insightful perspectives and make smarter decisions quicker.
Augmented analytics is a core aspect of the Decision Intelligence Platform. It combines data prep, business analytics, and data science with a no-code experience and a unique analytics engine connecting your data directly to the source. We apply AI guidance throughout the entire analytics pipeline to speed up insights, scale adoption, and simplify analytics.
Citizen data scientists can have easier access to cleansed and trusted data through the power of augmented capabilities built into our data preparation functionality. Our no-code platform features machine learning algorithms that reduce the need for intervention from IT.
Employees can utilize our Natural Language Query (NLQ) Chat Bot to interact with visualizations intuitively with plain English queries. The Chat Bot facilitates data exploration by encouraging spontaneity, helping users to explore the data in an organic and unrestricted way quickly.
Our Explain feature makes it simple for your team to access the insights they need to make intelligent decisions. An advanced machine learning algorithm automatically traverses all hierarchies and metrics, finding the most significant drivers and influencers behind the values in question. This gives everyone the power to dig directly into their source data with a single click, giving them faster insights into key drivers and influencers through automated analysis with little effort.
Find out more about augmented analytics and see how to empower everyone in your organization‒for any analytics need. The Gartner Critical Capabilities Report is a good place to start. Gartner identified Pyramid Analytics as the top vendor in the Augmented Analytics use case. In addition, we were the only vendor to be a top 5 vendor for all four major critical capabilities use cases.
Contact us for a demonstration to see how we integrate augmented analytics into our platform.