Augmented Analytics: BI Meets AI (Today and Tomorrow)
Driving the Analytics of 2030, not 2020
The future of analytics isnât just about better dashboards or faster reports â itâs about rethinking how we harness data to drive competitive advantage. In my full article, Driving the Analytics of 2030, Not 2020, I explored how organizations need to shift their mindset and strategy to keep pace with the evolving analytics landscape. This section walk through Augmented Analytics, uncovering key insights and practical strategies for forward-thinking data leaders.
Augmented Analytics is the infusion of AI and machine learning into the analytics process itself. Instead of just providing tools to make charts, augmented analytics features can automate insights discovery. Gartner heralded augmented analytics as âthe future of analyticsâ, because it helps business people overwhelmed by data to find what truly matters. In essence, AI helps at each step: preparing the data, analyzing it for patterns, and even presenting findings in natural language.
Read more: [Gartner: Augmented Analytics is the Future of Analytics](<https://go.thoughtspot.com/analyst-report-gartner-augmented-analytics-li.html?utm_source=linkedin&utm_medium=paidsocial&utm_campaign=dec-19-gartner-augmented-lsn#:~:text=Analytics is at a critical,driven decisions)
For example, modern BI tools might allow you to ask questions in plain English (âWhat were our top 3 products by profit last quarter?â) and get an instant answer with a chart â thatâs NLP in analytics. Or the system might proactively alert you: âHey, this metric is unusual today and here are likely reasons.â One quote put it well: âAugmented analytics uses AI/ML to augment human intelligence, presenting the insights most important to them and driving data-driven decisions.â
Read more: [Gartner: Augmented Analytics is the Future of Analytics](<https://go.thoughtspot.com/analyst-report-gartner-augmented-analytics-li.html?utm_source=linkedin&utm_medium=paidsocial&utm_campaign=dec-19-gartner-augmented-lsn#:~:text=Analytics is at a critical,driven decisions)
By 2030, augmented analytics will be everywhere. Some concrete things to expect.
Automated Insight Generation
You open your analytics app and it already has a few headlines for you: âSales in Region East are 5% above trend due to spike in Product Z sales to first-time customers.â This is the AI doing the first pass of analysis â scanning millions of data points and summarizing the âso whatâ for you. Itâs like having a junior analyst always on duty, but one that works at machine speed.
Natural Language & Chat Interfaces
Instead of slicing data via mouse clicks, you might chat with your data: âAnalyticsBot, compare our Q1 customer acquisition cost across regions and tell me if anything stands out.â The bot could reply with text and charts, and you could drill down with follow-up questions. OpenAIâs GPT models are already being integrated into BI tools for this purpose. By 2030, talking to your data will be normal â even fun.
Decision Support Integration
Augmented analytics doesnât stop at insight; it helps with decisions. This means integrating analytics into business processes. For instance, when making a pricing decision, an AI agent might pop-up in the pricing software saying, âBased on historical data and current demand, a price of $X is optimal for this deal (with 90% confidence).â Itâs not making the decision, but itâs supporting it with on-the-fly analytics. Think of it as an AI consigliere whispering data-driven counsel in every managerâs ear.
Broader Adoption Beyond Analysts
The real promise is that anyone can leverage advanced analytics, even if they arenât a data scientist. Augmented analytics, combined with easy UIs, means a marketing specialist can do customer segmentation with clustering algorithms without knowing the math, or an HR manager can forecast attrition without coding a model â the tools handle the heavy lifting. By reducing the skills barrier, organizations get more brains using data. In fact, the culture of data-driven decision-making truly flourishes when people at all levels can play wit.
Read more: [The Future of Data Analytics: Key Trends Shaping Innovation](<https://www.quadratichq.com/blog/the-future-of-data-analytics-ai-enhanced-insights-at-your-fingertips#:~:text=The success of data analytics,of organizations across every industry)
One emerging trend is the idea of the Analytics Catalog or Marketplace inside companies â by 2030, large enterprises might have internal âapp storesâ of data products. Employees could browse a library of AI-powered analysis modules (e.g., a âchurn predictorâ or âinventory optimizerâ) and plug them into their workflow. This modular approach, paired with augmented analytics, could massively accelerate analytics adoption.
The path to 2030âs analytics landscape isnât about incremental improvements â it requires bold rethinking and strategic transformation. In the next article, weâll dive into AI Agents and No-Code Automation.