Don't Be So Serious

I often see people using the terms dashboards and insights interchangeably. They are not the same.

Dashboards answer the “what” questions: “Revenue is up (or down) by X% this month,” “Daily Active Users is Y,” etc. Dashboards provide a way to display information (not raw data). In other words, dashboards visualize structured and aggregated data that has been processed to highlight specific results — transforming raw data into information.

Insights, on the other hand, answer the “why” questions: “Why did revenue drop?” “Why did Monthly Active Users increase?” Insights reveal the underlying causes and effects behind the observed trends. For example, if you run a retention campaign but churn remains high, insights will explain why the campaign had limited success. When information reveals a negative trend, insights help pinpoint root causes and areas for improvement. When the trend is positive, insights help identify what is working and where there’s room to grow.

These are distinct and critical differences. The confusion between dashboards and insights often leads to poor decision-making. Many decisions are made based on raw or visualized information without understanding the why behind the what. This is where analysis bridges the gap.

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Analysis and Bias

There is a challenge when it comes to analyzing information and deriving insights: bias. Most analyses today are correlation-based. This often leads to biased or misleading insights. The choice of variables and interpretations is influenced by the person running the analysis.

Correlation-based methods are not ideal. However, they are sometimes the only available option. They serve as a starting point for further exploration. We must aim to produce consistent, unbiased analysis. To achieve this, we should shift toward Experimental Data Science. This approach is designed to normalize and reduce bias in analysis.

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What Is Experimental Data Science?

Experimental Data Science involves using model outputs to derive hypotheses, which are then validated through controlled experiments. The process looks like this:

1. Use model results to propose a hypothesis.
2. Design a low-cost experiment to validate the hypothesis.
3. If the hypothesis is confirmed, increase investment; if not, revise and re-test.
4. Through iteration, you move closer to identifying causal relationships (not just correlations).

This iterative, hypothesis-driven process helps eliminate bias and leads to a more accurate understanding of which KPIs and business levers truly drive results.

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The Role of Automation

We can’t discuss dashboards, insights, and analysis without addressing automation.

Today, most organizations have automated dashboards via tools like Looker, Sigma, Power BI, and Tableau. These dashboards are fed by data pipelines typically developed by IT professionals and data engineers. In some cases, the dashboards are populated by AI models analyzing data at scale. Teams then examine these dashboards to interpret results — but rarely does this process include Experimental Data Science, and even more rarely is the full loop automated.

Automation is essential to reducing bias in insights — and this is where AI becomes critical.

The data pipeline (from collection to cleaning, storage, and modeling) should be fully automated. While analysis still involves human judgment, humans should be assisted by AI models that:

• Break down complex datasets,
• Add market or contextual awareness,
• Present multiple analytical perspectives.

Ideally, a GPT-like model powers this process, where human analysts collaborate with the AI to define actions. The model then runs automated experiments, surfaces results, and adjusts the course as needed — creating a feedback loop of experimentation and learning.

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Toward a Fully Automated Analytics Cycle

In the ideal setup, the entire analytics life cycle is automated:

1. Data ingestion and storage
2. Data cleaning and modeling
3. Dashboard visualization
4. Insight generation with AI-assisted analysis
5. Hypothesis testing via automated experiments

This approach can be applied across business domains, including:

• Audience segmentation and marketing optimization
• User engagement and retention
• Churn reduction and conversion improvement
• Market intelligence
• Cybersecurity and behavioral analysis
• Content greenlighting
• Consumer affinity modeling

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Business Value: Speed and Optimization

Two key outcomes of this methodology:

1. Revenue Acceleration: Speed to insight and speed to market are dramatically increased.
2. Cost Efficiency: Automation reduces labor-intensive tasks and optimizes decision-making processes — especially when combined with AI strategies like multi-armed bandit algorithms that dynamically allocate resources to the best-performing strategies.