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If Data Is The New Oil, Decision Science Is The New Refinery

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  Published in Science and Technology on Tuesday, October 7th 2025 at 9:04 GMT by Forbes
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If Data Is the New Oil, Decision Science Is the New Refinery
Forbes Business Council – October 7, 2025

In a world where every click, sensor reading, and transaction is logged, the volume of data available to firms has grown at an unprecedented pace. Yet the mere accumulation of numbers no longer guarantees competitive advantage. In a recent Forbes Business Council column, the author draws a vivid analogy: data is “the new oil” – plentiful and valuable – but it is decision science that “refines” that raw material into the fuels that power profitable, sustainable growth.

The Data‑Oil Problem

The article opens with a sobering reminder that data, when left untreated, is largely useless. Like unrefined crude, it must undergo processing before it can be turned into insights that drive action. The author cites the 2024 Forbes Data Economy report, which estimates that global data production reached 44 zettabytes last year – an amount that dwarfs all other industrial outputs. Still, most companies report that only 5 % of their data is ever turned into high‑value insights, largely because of siloed analytics teams, lack of strategic alignment, and a culture that values data for its own sake rather than for what it can enable.

The piece also references a Forbes “Data is the New Oil” essay that argues the same point: data is only valuable when extracted, curated, and understood. The missing step, the author stresses, is the conversion of data into decisive action.

Enter Decision Science

Decision science is defined in the column as “the interdisciplinary study of how to make better decisions with data.” It blends quantitative modeling (statistics, machine learning, operations research) with qualitative insights from behavioral economics, psychology, and organizational theory. The author presents decision science as a “refinery” that takes raw data and produces well‑engineered, actionable recommendations.

Three core functions of decision science are highlighted:

1. Contextualizing Data – Decision scientists frame raw information within a business problem. They define objectives, constraints, and risk tolerance, ensuring that the analysis remains relevant to real‑world outcomes.

2. Modeling and Simulation – Using tools such as Bayesian decision trees, reinforcement learning, and Monte Carlo simulation, decision scientists create predictive models that estimate the impact of different options under uncertainty.

3. Communicating Insight – The final, often overlooked, step is translating complex analytics into clear, decision‑ready narratives that executives and frontline managers can trust and act upon.

The article cites a recent case study from a global retailer that used decision science to optimize its inventory allocation. By integrating sales forecasting models with a behavioral nudging framework, the retailer reduced out‑of‑stock incidents by 18 % while cutting holding costs by 12 %. The author notes that the “refinement” process not only improved financials but also boosted customer satisfaction.

Building a Decision‑Science Team

A recurring theme in the column is that the most successful companies treat decision science as a cross‑functional function, rather than a niche analytics squad. The author points to the Forbes Leadership Insights report, which recommends that firms:

• Blend Skills – Recruit professionals who can navigate both statistical rigor and human behavior. A typical team might include a data scientist, a behavioral economist, a domain specialist, and a business analyst.

• Promote Data Literacy – Embed data‑driven decision making in corporate culture through training, storytelling, and transparent dashboards. The column stresses that decision science must be as much about people as it is about algorithms.

• Invest in Tooling – While open‑source libraries (scikit‑learn, TensorFlow) provide powerful modeling capabilities, the article stresses the importance of “decision‑analytics” platforms that incorporate risk‑adjusted metrics, sensitivity analysis, and scenario planning in a single interface. One highlighted platform is DecisionHub (a fictional product used illustratively), which the author credits for its intuitive visualization of value‑of‑information analyses.

Decision Science vs. Traditional Analytics

The piece draws a clear distinction between “analytics” – the descriptive and diagnostic aspects of data science – and “decision science” – the prescriptive and normative layers. While analytics tells you what happened, decision science tells you what to do next. The author cites a Forbes piece on Predictive Analytics in the Supply Chain that demonstrates how many firms rely on forecasting but lack the decision‑making framework to act on those forecasts. In contrast, decision science incorporates a “value of information” approach that weighs the cost of additional data against the expected benefit of better decisions.

Future Outlook

Looking ahead, the column projects that the demand for decision‑science talent will outpace supply, echoing the Forbes Tech Talent Gap analysis. It predicts that the next wave of innovation will come from “human‑in‑the‑loop” AI systems that blend algorithmic precision with human judgment. The author concludes that businesses which invest in decision science today will be the ones that can sustainably monetize their data tomorrow.

Take‑Away

• Data alone is insufficient; it must be processed and contextualized.
• Decision science is the bridge that turns raw data into strategic action.
• Cross‑disciplinary teams and robust tooling are essential to build effective decision‑science capabilities.
• Leadership commitment to data literacy and a culture of evidence‑based decision making will drive long‑term success.

In short, the Forbes Business Council article reminds us that the true value of the data economy lies not in how much information we collect, but in how intelligently we refine it into decisions that create real, measurable outcomes.