As a young man I worked as an electrician for two years, wiring buildings and installing systems. It was hard, manual work, but at the end of each project, I experienced a sense of deep gratification: a tangible result of my efforts. A building, fully wired and functional, with lights glowing and systems humming. The connection between my labor and the finished product was undeniable and rewarding.

Fast forward to my career in data science, where the connection between effort and outcome often feels far more abstract. I work hard on analyses, build models, craft insights, and provide stakeholders with the answers they seek. But all too often, I don’t see the end result. What decisions were made based on my work? What impact did those decisions have on the business? Did they truly add value?

Should there not be a clear line of sight?

The Decision Black Hole

Many data scientists find themselves working in a proverbial vacuum. Insights are delivered, but they often disappear into the ether of business operations, never to be heard from again. We might be told we’ve done a good job, but this validation is abstract, detached from the ultimate outcomes we’ve worked so hard to influence.

Keeping up-to-date with the latest data science topics and methods certainly keeps our minds sharp and our “brains in gear,” and there is satisfaction in mastering the craft itself. However, something still feels missing—a sense that we’re not reaching our full potential. Without a clear understanding of how our work translates into real-world decisions and measurable outcomes, it’s difficult to feel the same sense of fulfillment and purpose that comes from seeing the “tangible” results of our labor, an outcome precisely identified and understood. This lack of transparency creates a fundamental disconnect.

What is an Impact Path in Data Science?

An impact path is a framework for goal-driven analytical decision-making ensuring that data science efforts lead to precisely defined business outcomes. It bridges the gap between insights and action by defining how data-driven analysis progresses from discovery to implementation — directly supporting decision-making — and delivering measurable outcomes aligned with organizational goals.

Without a structured impact path, data science risks becoming disconnected from meaningful decision-making, leaving valuable insights underutilized.

An effective impact path includes the following key elements (slightly adapted from the amazing work of Victor S. Y. Lo and Dessislava A. Pachamanovaelements):¹

1. Define the Goal: start with a clear objective. What are you ultimately trying to achieve? Without a well-defined goal, analytics projects risk being unfocused and ineffective.
2. Identify Key Outcomes: determine what specific results will signal progress toward your goal (#1). These outcomes are directly influenced by actions you take (see next bullet)
3. Determine Associated Actions (Decision Variables): outline the actions, or decisions, a stakeholder can take to influence the outcomes (#2) that will move the needle on the overarching goal (#1).
4. Model Relationships: use data to build a clear understanding of how actions (#3) influence outcomes (#2) using predictive models, causal analysis, or other insights to guide decisions.
5. Account for Constraints: consider any limitations—such as budget, resources, or business policies—that restrict decision-making.
6. Solve for the Best Outcome: analyze “what-if” scenarios from models (#4) and identify the best decision or action (#3), or combinations of actions, to achieve your goals (#1) within the given constraints (#5).
7. Optimize: the process is iterative. Goals, outcomes, and actions may need to be revisited as new data and insights emerge.

Critical to success of the entire impact path is how effectively we map the first three elements — which in practice is helpful to do in reverse order — and requires clear alignment with stakeholders at the start of a project. Here are some basis examples to illustrate the proper set-up of an impact path²:

Importantly, notice that all goals are connected to clear decisions to be made and that the intermediate outcomes — to be analyzed and optimized through model development and scenario analysis — move the needle on the ultimate goal or objective.

By establishing a clear and traceable impact path, businesses can ensure that every analysis is actionable and aligned with their objectives. This structured approach not only prevents insights from disappearing into the “decision black hole” but also empowers teams to deliver measurable outcomes and drive real business success.

On the surface this may seem straightforward. But it is astounding how often these steps are overlooked, or assumed to be so obvious that explicit mappings are not required. This is when the proverbial goalposts start to move, resources are wasted, and projects get detailed and/or fail outright.

With an impact path, data science becomes more than an analytical exercise—it becomes a strategic advantage that directly connects effort to results.

Advancing Through Analytics: From Descriptive to Prescriptive

To help ensure that insights lead to precisely identified business outcomes, it’s essential to progress through the stages of analytics:

1. Descriptive Analytics: this initial stage focuses on summarizing historical data to understand what has happened. While valuable for hindsight, it doesn’t provide guidance for future actions.
2. Predictive Analytics: building upon descriptive insights, predictive analytics forecasts future trends and outcomes based on historical data patterns. This forward-looking approach aids in anticipating potential scenarios but stops short of recommending specific actions.
3. Prescriptive Analytics: the most advanced stage, prescriptive analytics, not only predicts future outcomes but also suggests actionable strategies to achieve desired results. By analyzing various decision options and their potential impacts, it provides a clear course of action.

Transitioning through these stages enables data professionals to move from merely reporting past events to actively shaping future decisions. This progression is crucial for closing the “Intelligence Value Gap,” where reliance on historical data must evolve into actionable insights that drive business success.

By embracing predictive and prescriptive analytics, organizations can transform data into a strategic asset, ensuring that the efforts of data scientists lead to visible, impactful outcomes.

Why Visibility Matters

The absence of a clear feedback loop in data science work doesn’t just diminish job satisfaction—it undermines the potential impact of the insights themselves. If insights aren’t directly tied to decision-making or measured against clear objectives, how can we know their true value or worth?

This lack of visibility also creates risks for businesses:

1. Wasted Effort: analyses may be completed and insights delivered, but without implementation or measurement, they provide no real benefit.
2. Missed Opportunities: without tracking outcomes, organizations may fail to recognize what works and what doesn’t, limiting the potential for learning and improvement.
3. Workplace Unfulfillment: for data scientists, the absence of “tangible” results can lead to frustration, disengagement, and burnout.

The Case For Decision Sciences

The answer to closing the gap between effort and impact lies in decision sciences—a discipline focused on driving clear, measurable actions through data. Decision sciences emphasize tying every analysis to specific decisions, quantifying outcomes through metrics like incrementality and lift, and using advanced methods like simulations to model potential scenarios.

In addition, this approach restores the “tangible” connection between effort and outcome, creating a more fulfilling and impactful practice. Data scientists evolve into Decision Scientists—key contributors to measurable business success, driving not just insights but actions that transform organizations.

From Numbers to Notable Outcomes

Just as an electrician takes pride in seeing a building come alive—lights shining, systems humming—data scientists should feel the same spark of fulfillment when their work illuminates decisions and powers measurable success. Decision sciences provide the wiring needed to connect effort to outcome, transforming insights into actions that generate undeniable value.

By embracing decision sciences, we can move beyond abstract validation from stakeholders—where success is acknowledged but lacks “tangible” impact—and into a domain where success is defined by direct and measurable results. This shift requires a deliberate focus on clear objectives connected to decision-making scenarios, actionable insights to support the decision-making process, and measurable outcomes to validate if the decision and resulting action(s) was correct, ensuring that data not only illuminates the way but also becomes the engine driving strategic decisions and business growth.

The Leadership Imperative

Ultimately, the evolution from data scientist to decision scientist isn’t just about mastering new techniques or tools—it’s about becoming a pivotal contributor to transformative results. For this shift to succeed, leadership must play a key role by fostering a culture that values action over abstraction, prioritizes measurable outcomes, and empowers teams to connect their efforts to the organization’s strategic goals. In this role, we don’t just explain the past or predict the future; we shape the present by empowering organizations to act with clarity, confidence, and purpose.

1. From Meaningful Data Science to Impactful Decisions: The Importance of Being Causally Prescriptive^* ↩︎
2. From Meaningful Data Science to Impactful Decisions: The Importance of Being Causally Prescriptive^* ↩︎