The field of business intelligence is on the cusp of its next major disruption, and it's being driven by the power of artificial intelligence. This new frontier is called augmented analytics. While BI has traditionally relied on analysts to manually prepare data, explore it for insights, and build visualizations, augmented analytics uses machine learning and natural language processing (NLP) to automate and accelerate every step of this workflow. It’s a paradigm shift that promises to make deep data insights accessible to a broader range of business users, not just data specialists, and to free up analysts to focus on higher-level strategic thinking.

What is Augmented Analytics?

Augmented analytics refers to the use of AI/ML techniques to assist with data preparation, insight discovery, and insight explanation. Instead of a user needing to manually sift through data, an augmented system can proactively surface important findings. This works in several key ways:

• Augmented Data Preparation: Data preparation is notoriously time-consuming, often consuming up to 80% of an analyst's time. Augmented analytics can automate tasks like data cleansing, detecting schema changes, joining datasets, and recommending enrichments, dramatically reducing the manual effort required to get data ready for analysis.
• Augmented Insight Discovery: This is where the magic truly happens. An augmented BI tool can automatically analyze a dataset and identify the most statistically significant correlations, trends, outliers, and key drivers. For example, it might automatically discover that a sales dip in a particular region is strongly correlated with a competitor's recent marketing campaign, an insight that might have taken a human analyst hours or days to find.
• Augmented Insight Explanation: Beyond just finding insights, augmented analytics can explain them in plain English. Using Natural Language Generation (NLG), the system can create automated narratives that describe what is happening in a chart or dashboard, making the information immediately understandable to a non-technical business user.

The "Citizen Data Scientist"

One of the most profound impacts of augmented analytics is its role in fostering the rise of the "citizen data scientist." This refers to a business user with deep domain knowledge but who is not a trained statistician or data scientist. Augmented analytics empowers these users by automating the more complex statistical aspects of data analysis. It allows a marketing manager, for instance, to quickly understand the key drivers of customer churn without needing to build a complex model themselves. This democratizes the power of data science, distributing it throughout the organization and embedding it within the lines of business where it can have the most immediate impact.

The Changing Role of the Data Analyst

Some may fear that augmented analytics will make data analysts obsolete. In reality, it elevates their role. By automating the tedious and time-consuming tasks of data preparation and initial exploration, it frees analysts from the drudgery of "data janitor" work. They can spend less time wrangling data and more time on high-value activities: interpreting the AI-generated insights in the context of the business, applying their domain expertise, communicating findings through compelling data stories, and focusing on complex, strategic business problems. The analyst's role shifts from being a report builder to a strategic data advisor.

The Path Forward

Augmented analytics is more than just a buzzword; it's a fundamental change in how we interact with data. As AI and ML models become more sophisticated and more deeply integrated into BI platforms, the process of gaining insights will become more conversational, more proactive, and more accessible than ever before. Organizations that embrace this next frontier of business intelligence will be able to make smarter, faster decisions and unlock a level of insight that was previously unimaginable.