I recently revisited Avinash Kaushik’s brilliant article, “Winning With Data: Say No to Insights, Yes to Out-of-Sights,” and felt inspired by his challenge to aim higher in our data-driven work. The concept of “Out-of-Sights”—insights that are Novel, Actionable, Credible, and Relative (N-A-C-R)—is not just an aspiration but a call to fundamentally reimagine how we approach analytics and decision-making.

While it’s easy to recognize the goal of delivering “Out-of-Sights,” achieving this standard consistently is another matter entirely. How do we use data, statistics, and models to not only learn from data but deliver insights that truly drive action and influence outcomes?

Knowing where we want to land is one thing; getting there is another. The path is often fraught with challenges, from technical limitations to competing priorities. But today, we have a powerful ally reshaping the way we work: Generative AI (GenAI). This transformative technology is ushering in a golden age for data practitioners, enabling us to overcome traditional constraints and focus on what truly matters—delivering insights (pardon me, “Out-of-Sights”) that make a difference.

GenAI: A Productivity and Creativity Catalyst

Generative AI (GenAI) is revolutionizing the way analysts and data scientists work by automating and streamlining many of the most labor-intensive tasks—such as data wrangling, cleansing, and transformation. These tasks, once significant logistical hurdles, are now dramatically accelerated, enabling us to focus more on solving meaningful problems rather than wrestling with data preparation. Where we might have spent days getting data “model-ready” in the past, we can now achieve the same results in hours, freeing up valuable time for exploration and discovery.

The true power of GenAI, however, lies not just in improving productivity but in expanding the analytical toolkit available to solve business problems. Traditional constraints—whether due to time, resources, or technical expertise—often limited our ability to experiment with complex or novel methods. GenAI is breaking down these barriers, making it faster and more feasible to apply advanced approaches like probabilistic programming, simulations, or other cutting-edge techniques to problems that once felt out of reach.

By accelerating workflows and unlocking new possibilities, GenAI is empowering data practitioners to explore solutions that are not only faster but also more innovative—paving the way for insights that truly make an impact.

Shifting Focus to the “Novel”

The newfound efficiency brought by GenAI is unlocking opportunities to focus on the key innovation catalyst: the “Novel” in N-A-C-R insights.

One of the biggest reasons organizations struggle to move beyond the “water is wet” analysis described by Avinash (e.g., obvious or redundant conclusions) is the traditional analytics process itself. It’s often constrained by the sheer amount of time and energy required just to produce basic descriptive insights. These types of insights, while necessary, often fail to close the “intelligence value gap”—the chasm between what an organization currently knows and what it needs to know to make strategic decisions.

By alleviating some of the burden of operational and logistical tasks, GenAI frees up time for analysts to focus on higher-value activities: asking the right questions, interrogating the data more deeply, and—most importantly—exploring hypotheses that might have once been dismissed as too time-consuming or complex.

It’s in this exploratory space that we can begin uncovering truly Novel insights—insights that not only surprise but fundamentally reshape the way decisions are made. With more time to push past surface-level analysis, data practitioners can deliver findings that inspire action and drive competitive advantage.

Breaking the Cycle of “Water is Wet” Analysis

Achieving “Out-of-Sights” requires more than technical proficiency—it demands a Decision Sciences mindset. At its core, this mindset shifts the focus from descriptive analysis (“what happened”) to prescriptive insights that answer, “what should we do?” While descriptive insights can highlight patterns and trends, they often fall short of driving decisions that lead to clear, measurable outcomes.

The role of a Decision Scientist is to make insights usable by delivering increasingly predictive and prescriptive recommendations. This requires focusing not just on the science of inputs (data and analysis) but on the science of outputs (recommendations and decisions)¹. It involves interpreting analyzed data within a decision-making framework—comparing alternatives, exploring outcomes, and determining the best course of action.

Decision Scientists leverage data within expected-value frameworks and scenario analyses to guide strategic decision-making. They are not merely answering questions with data; they are laser-focused on optimizing incrementality and lift, ensuring their insights deliver tangible improvements, such as increased marketing ROI or other business impacts. This approach demands a clear understanding of goals, the ability to anticipate decision-making processes, and the skill to weigh conflicting evidence to craft actionable recommendations.

What truly sets Decision Scientists apart is their unique blend of skills and characteristics:²

1. Strong Business Acumen: a deep understanding of business dynamics enables Decision Scientists to contextualize their analyses and align insights with organizational objectives.
2. Robust Analytical Mind: the ability to break down complex problems, frame them as actionable questions, and deliver clear, strategic solutions.
3. Extensive Data Science Proficiency: technical expertise in tools like Python, R, and SQL, as well as advanced modelling and predictive analytics, to uncover meaningful insights from data.
4. Creative Problem-Solving: the ability to think outside the box, innovate, and explore the broader implications of insights in dynamic, real-world contexts.

This unique combination of technical proficiency, business understanding, and creative thinking enables Decision Scientists to go beyond merely reporting the past. Instead, they focus on influencing the future by providing insights that are novel, actionable, credible, and relative—the key attributes of “Out-of-Sights.”

The Importance of Impact Paths

A fundamental prerequisite of “Out-of-Sights” is that they support decision-making. While this may seem self-evident, it is a critical point that often goes unspoken—and, as a result, is frequently overlooked in practice. Too often, insights are produced that fail to inform or influence meaningful decisions, leaving organizations stuck in the realm of analysis rather than action. This is where Decision Sciences steps in, and where the concept of impact paths becomes indispensable.

To ensure that insights lead to action, Decision Scientists establish clear impact paths—structured frameworks that connect insights to measurable outcomes. An impact path answers critical questions such as:

• What decision does this insight inform, and why does it matter?
• How does this insight align with business goals and priorities?
• What actions should follow, and who is responsible for them?
• What is the anticipated impact, and how will it be measured?

Without these clear pathways, even the most novel and compelling insights risk being ignored, misunderstood, or deprioritized. Impact paths act as a bridge, closing the gap between analysis and action by ensuring that insights are tied directly to decisions and their expected outcomes.

By grounding their analytical work in impact paths, Decision Scientists move beyond simply delivering data-driven findings. They enable organizations to act with confidence, ensuring that every insight not only informs but also drives meaningful, measurable change. In doing so, they elevate analytics from a supporting role to a critical enabler of strategic decision-making.

In Closing: Turning Constraints into Opportunities

The efficiency and creativity unlocked by GenAI allow us to redirect our energy toward solving meaningful problems, focusing on what truly matters: delivering insights that are Novel, Actionable, Credible, and Relative (N-A-C-R). By embracing a Decision Sciences mindset, we move beyond descriptive analytics to provide prescriptive insights that influence the future, not just report the past. Clear impact paths ensure that every insight connects to measurable outcomes, bridging the gap between analysis and action.

This shift is not just about improving analytics—it’s about elevating decision-making. Every “Out-of-Sights” we deliver narrows the intelligence value gap, transforming data into decision-altering revelations. By embracing this mindset, we enable ourselves to become not just better analysts but also more effective decision-makers.

Out-of-Sights are achievable—they’re not an elusive standard reserved for the lucky few, or the occasional jackpot. They require the right mindset, tools, and focus. By leveraging GenAI and adopting a Decision Sciences approach, we can break free from the cycle of obvious conclusions and deliver the kind of insights that matter: insights that surprise, inspire action, and deliver measurable results.

I’ll be bringing this mindset to my work tomorrow. How about you?

1. Since Data Scientists Don’t Do Business Decisions — Businesses Need Decision Scientists ↩︎
2. Data Science vs. Decision Science: What’s the Difference? ↩︎