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Augmented Analytics
Augmented analytics represents the next logical evolution of your organization’s business intelligence (BI) strategy. It fundamentally changes your relationship with data by integrating artificial intelligence, machine learning (ML), and natural language processing (NLP) directly into the data analysis workflow. Unlike traditional BI, which provides dashboards and reports for humans to interpret, augmented analytics automates the entire insight-generation process. It moves your teams beyond just observing data. This technology automates data preparation, proactively discovers significant patterns, and explains those insights in plain English, often recommending specific actions.
This shift is critical because it directly addresses the primary bottleneck in traditional data analysis: human capacity. In the past, your BI and data science teams spent the majority of their time on laborious, manual tasks. They had to cleanse and prepare the data, build complex query models, hunt for correlations, and finally translate their statistical findings into a narrative that business leaders could understand. This process is slow, expensive, and inherently biased by the questions the analyst thinks to ask. Augmented analytics, by contrast, automates these time-consuming steps, allowing your skilled analysts to focus on higher-value strategic work.
How Augmented Analytics Redefines the Workflow
The true power of an augmented platform lies in its ability to automate the most difficult parts of the data lifecycle. This automation typically occurs in three key phases. First, it addresses data preparation. Your teams know that up to 80% of any analytics project is spent on “data janitor” work: cleansing, normalizing, joining tables, and feature engineering. Augmented tools use ML algorithms to learn your data structures. Consequently, they can automate most of this preparation, flagging anomalies and suggesting data quality improvements, which dramatically accelerates project timelines.
Second, the platform performs automated insight discovery. Instead of a business user staring at a dashboard and trying to spot a trend, the AI engine proactively sifts through billions of data points. It tests thousands of variable combinations to uncover statistically significant correlations, anomalies, and key business drivers. For example, the system might automatically generate an insight like, “Sales in the northeast region dropped 7% last quarter.” This is a helpful, but standard, BI finding. Augmented analytics, however, goes deeper.
This leads to the third phase: natural language generation (NLG). The system follows up on that initial finding by automatically determining the cause of the drop. It might add, “This drop was driven by a 45% decrease in sales from new customers, which correlates with our main competitor launching a new product in that same market.” The platform delivers this entire finding as a simple, narrative paragraph. This use of NLP also allows non-technical users to query complex databases by simply asking questions in plain English, such as, “What were our top 5 most profitable products in Europe, and what was their primary marketing channel?” This capability truly democratizes data access, moving it beyond your specialized IT and BI teams.
Your Role: Shifting from Gatekeeper to Enabler
As a technology leader, your role in this new paradigm shifts from being a gatekeeper of data to an enabler of insight. Augmented analytics empowers your business-side colleagues—in marketing, finance, and operations—to become “citizen data scientists.” They can self-serve their own reports and discover answers without filing a ticket with your department. This frees your expert analysts from the queue of routine report requests. However, this democratization also introduces new governance challenges that you must manage.
Your primary responsibility is to ensure the quality and integrity of the data feeding these AI models. The principle of “garbage in, garbage out” is amplified; an AI making automated recommendations from insufficient data can cause significant damage. Therefore, your focus on robust data governance, master data management, and secure data pipelines becomes more critical than ever. In addition, you must address the “black box” problem. An AI may provide a recommendation, but if it cannot explain how it reached its conclusion, your business leaders will not trust it. Your teams must be responsible for vetting, selecting, and configuring tools that provide high transparency and explainability.
A Practical Shift in Focus
Ultimately, augmented analytics is not about replacing your analysts. It is about augmenting them. This technology elevates their work from building reports to interpreting and validating AI-driven insights. They can then apply their unique human context to those findings and partner with business units to drive strategy. Your leadership will be essential in managing this cultural shift and upskilling your teams. By implementing these tools, you are building an organization that does not just have data, but can act on it rapidly and intelligently, providing a durable competitive advantage.