Traditionally, data analytics has been left to the experts. But the growth of artificial intelligence has introduced a more accessible type of analytics called augmented analytics, where AI and machine learning are embedded into business intelligence (BI) tools to automate various aspects of data analysis.
Augmented Analytics Definition
Augmented analytics is the practice of integrating artificial intelligence and machine learning into business intelligence platforms and tools. It automates the data analysis process, enabling users with varying technical skills to generate insights from data.
The primary objective of augmented analytics is to accelerate data analysis and allow even the least tech-savvy users to generate insights on their own. As AI continues to transform the modern business landscape, augmented analytics is playing a critical role in making data-driven decision-making faster, more intuitive and more broadly accessible.
What Is Augmented Analytics?
Augmented analytics integrates artificial intelligence and machine learning into business intelligence and analytics tools. With the help of this technology, teams can automate tasks like data preparation, insight generation and data visualization. This makes data analysis much faster, while still leaving the actual decision-making to humans. As a result, augmented analytics fosters a more collaborative relationship between humans and automation tools — enhancing productivity without replacing workers altogether.
“A well-implemented augmented analytics system acts like a smart co-pilot, helping users throughout the workflow,” Clarence Rozario, Global Head of Zoho Analytics Business, told Built In. “From data modeling and cleansing to metric creation and analysis.”
By automating tasks traditionally handled by data analysts, augmented analytics enables users with less technical expertise to use BI tools to analyze data and present their findings, democratizing access to data analysis. And as the sheer volume of business data continues to grow, these platforms make it possible to generate deeper, more comprehensive analyses from larger amounts of data as well.
How Does Augmented Analytics Work?
Augmented analytics automates different steps of data analysis by applying several disciplines of artificial intelligence.
Data Preparation
Augmented analytics uses AI algorithms to compile and clean data. This involves processing data from a range of sources, checking data for inconsistencies and identifying attributes to organize data accordingly. Automating this step saves data analysts from having to spend hours combing through data for errors and other issues.
Data Discovery
Machine learning algorithms can assess massive amounts of data, detecting anomalies and isolating outliers. Using techniques like cluster analysis, they can group data points based on similar characteristics to reveal broader patterns. Users can then transform these connections into insights that guide business decisions.
Natural Language Interactions
Natural language processing lets users ask a platform to complete actions using naturally worded queries instead of having to write code. Large language models process and convert these queries into a format that a machine can understand. The platform can then verbally respond through natural language generation, facilitating more seamless human-computer interactions.
Data Visualization
Augmented analytics can automatically generate data visualizations like bar charts, pie charts, scatter plots and heat maps, making it easier to identify trends that might be missed with standard queries. These visualizations are not only more intuitive, but they’re also easier to share with colleagues, making it easier for teams to collaborate and make more informed decisions together.
Statistical Analysis
Perhaps the most valuable feature of augmented analytics is its ability to perform statistical analyses and generate insights automatically. For example, a platform can intuit the type of data and data analysis it should focus on simply based on the request, customizing the analysis to fit a user’s needs. It can even detect and proactively alert a user about anomalies or patterns that weren’t part of the query at all.
Augmented Analytics vs. Predictive Analytics
Predictive analytics is a type of statistical analysis that uses historical data to determine the probability of an event occurring in the future. It essentially answers the question, “What might happen next?” Augmented analytics, on the other hand, uses AI and machine learning to automate the entire data analysis process — predictive analytics is just one possible component of this process.
“Augmented analytics is often associated with ‘predictive analytics,’ which is not always the case,” Sean Zinsmeister, Director of Outbound Product Management for BI at Google Cloud, told Built In. “While types of analysis like forecasting usually fall under this category, the end goal of augmented is not necessarily a prediction.”
In other words: Predictive analytics is a piece of the puzzle, while augmented analytics is the whole picture. In fact, augmented analytics can support three other types of statistical analysis in addition to predictive analytics:
- Descriptive Analytics: Assesses historical data to track changes over time. It seeks to answer the question, “What happened?”
- Diagnostic Analytics: Evaluates data to determine the cause of an event. It answers the question, “Why did it happen?”
- Prescriptive Analytics: Uses data to predict what will happen next and determine the best course of action. It answers, “What should we do next?”
Use Cases of Augmented Analytics
Augmented analytics is making a variety of industries more efficient, ranging from manufacturing to healthcare.
Retail
Retailers can use augmented analytics to track consumer buying habits, gauging which products are popular and which aren’t. They can adjust the location of items within a store while monitoring which inventory is running low. With augmented analytics, retailers can also accurately forecast demand and stock up on products to maximize future sales.
Finance
Financial institutions can use augmented analytics to review account activity, flagging unusual purchases or larger transactions to protect against fraud. Banks can also use augmented analytics to closely evaluate loan applicants’ financial history for possible risks and approve loans for qualified candidates.
Manufacturing
Augmented analytics can compile real-time data on factory equipment, allowing manufacturers to more effectively monitor machine performance, pinpoint potential bottlenecks and conduct predictive maintenance. This way, manufacturers can take proactive measures to address major issues before they occur and avoid long downtimes.
Healthcare
Growing amounts of patient data can be difficult for healthcare professionals to sort through, but augmented analytics can quickly organize this data and produce insights. Healthcare providers can use these insights to more accurately diagnose diseases, personalize treatment plans and analyze patterns in patients’ visits to identify and address gaps in their care.
Marketing
Marketers can employ augmented analytics to keep tabs on the number of interactions a particular advertisement earns, including comments, clicks and views. This can help them gauge the overall effectiveness of a given marketing campaign and shift resources as needed to prioritize the most popular ads and channels.
IT Management
With augmented analytics, IT teams can maintain a bird’s-eye view of all digital assets, gaining real-time data on user activity, device performance and system outages. IT teams can then take measures to prevent outages, implement software patches or updates and detect and stop cyber attacks in their earliest stages.
Benefits of Augmented Analytics
With an emphasis on automation, augmented analytics can help teams and organizations improve their operations in several ways.
Faster Insights
Augmented analytics platforms can quickly process massive volumes of data with machine learning algorithms and provide specific information in response to natural language queries. Users can then act on those insights in real time.
Greater Efficiency
Routine tasks like cleaning data, removing outliers and creating data visualizations are all automated through augmented analytics. As a result, data analysts are free to concentrate on more strategic work, like interpreting insights and identifying business opportunities.
Broader and Deeper Analysis
Augmented analytics can go more in-depth than a human data analyst, not only revealing trends among large data sets but also providing context around these trends. This way, users can better explain what’s happening within the data and what it means for the company.
“Augmented analysis can automatically detect anomalies in my data, and then auto-drill into these to help me understand the root cause,” Zinsmeister said. “You can go from an alert ‘something has happened’ to ‘here’s why it happened’ in real time.”
Increased Access to Data
Because augmented analytics tools can understand and respond to naturally worded questions, users of all skill levels can tailor their data analyses and insights according to their specific goals and preferences. This democratizes data analysis and reduces businesses’ reliance on specialized expertise.
Improved Predictions
Using AI and machine learning algorithms, augmented analytics can learn through repeated interactions with business data. Keep feeding these algorithms historical and current data, and they’ll deliver more accurate predictions over time.
Challenges of Augmented Analytics
Despite its obvious upsides, augmented analytics comes with some limitations that users need to keep in mind.
Low Data Literacy Levels
For augmented analytics to be effective, users must be able to read data visualizations. If employees aren’t trained on how to interpret data patterns or consider AI recommendations, they may make inaccurate decisions.
Poor Data Quality
Training machine learning models on clean, accurate data is essential, as errors like missing values, duplicated entries and outdated information can seriously compromise the reliability of their insights. Data silos can also make it difficult for augmented analytics platforms to compile and clean company data. Even when platforms appear to be trained correctly, AI can still produce false or biased outputs.
“Although AI/ML models are improving rapidly, blindly trusting the outcomes can be risky. These systems are only as good as the data and assumptions they are trained on,” Rozario said. “It’s crucial to verify results and, where possible, customize or fine-tune models for your specific needs.”
Unrealistic Expectations
Augmented analytics can still struggle with scaling, particularly when organizations are dealing with complex data sets. These tools may also fail to consider industry context when analyzing data trends, so employees must be aware of these shortcomings.
Employee Misconceptions Around AI and Machine Learning
Introducing augmented analytics in the workplace may give employees the wrong idea, especially if they fear AI taking over their jobs. In reality, augmented analytics is designed to assist — not replace — human workers by automating repetitive, mundane tasks. This way, employees can focus on higher-value work, such as interpreting insights and strategizing.
Lack of Transparency Around AI
Users may not always be able to explain why AI models draw certain insights from data or come to the conclusions they do. This can make it harder to validate the findings of augmented analytics platforms, which can undermine trust in automation in general.
Best Practices for Using Augmented Analytics
To maximize the impact of augmented analytics on your organization, follow these guidelines when integrating automation technologies into your workflows.
1. Establish Clear Objectives
Assemble all relevant stakeholders to discuss why you need an augmented analytics platform in the first place. Define the problems you want to address and the goals for implementing this technology, so you can better gauge whether an initiative is successful down the road.
2. Choose the Right Platform and Analytics Tools
When selecting an augmented analytics platform or tool, consider whether it’s easy for your employees to use, supports appropriate AI and machine learning capabilities, fits within your budget and complies with all your privacy and security protocols. Update your current tech stack as needed to ensure it’s compatible with your preferred platform.
3. Prepare Your Data for Analysis
Clean your data. Fill in missing values, remove duplicates, de-silo data and resolve any other issues. Preparing high-quality data ahead of time speeds up the analysis process and ensures AI models can deliver more reliable and accurate results.
“‘Garbage in, garbage out’ is still foundational to success with augmented analytics,” Zinsmeister said. “If your data estate is not in order, or if data is not modeled to be analytics-ready, then it’s difficult to trust the results.”
4. Start With a Smaller Project
Begin by using augmented analytics to solve a particular problem or automate workflows within one department. Testing it within a single use case can help you work through any obstacles and improve the system before applying it on a larger scale.
5. Provide Education and Training
Equip employees with the training they need to confidently use augmented analytics tools. When people understand how the technology works and how to apply it, they’re not only more likely to adopt and integrate it into their workflows effectively, but they’re also more likely to trust it.
6. Encourage a Collaborative Approach
In addition to company-wide education, establish open lines of communication between departments and stakeholders. Different teams can share ideal use cases and performance updates, allowing your company to assess and adjust on the fly.
The Future of Augmented Analytics
The future of augmented analytics is inextricably tied to the future of AI — and the possibilities are rapidly expanding. One especially promising development is the rise of agentic AI and the deployment of AI agents to complete more advanced, multi-step tasks. This could usher in a new era of “agentic AI-powered analytics assistants,” according to Rozario.
“These tools, powered by generative AI and deep reasoning, can act as fully conversational analytics agents,” Rozario said. “By simply connecting to a data source, users could ask natural language questions, monitor KPIs, trigger workflows and automate complex analytical tasks — making analytics more dynamic and proactive than ever before.”
Frequently Asked Questions
What is augmented analytics?
Augmented analytics is when artificial intelligence is integrated into business intelligence and analytics tools to automate data analysis processes. This allows users with varying levels of technical expertise to generate data-driven insights that inform their decisions.
What is the difference between augmented analytics and business analytics?
Business analytics refers more generally to the process of using statistical methods and tools to analyze data and uncover trends. Augmented analytics builds on this approach by introducing AI and machine learning to automate the data analysis process.
What is the future of augmented analytics?
Augmented analytics is likely to be enhanced by advances in artificial intelligence and machine learning. For example, augmented analytics platforms could be equipped with AI agents that can further accelerate insight generation and perform more complicated tasks.
