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What “data to value” means in Schmarzo’s approach
Data has no business value simply because an organization collects or stores it. Its potential value comes from helping people make better decisions that contribute to an outcome the organization cares about. In an interview published October 24, 2022, Schmarzo describes starting with the value to be created and making that value explicit through measures such as KPIs and metrics. He puts the point plainly: “If you don’t do that, you will never be value driven.” Read the interview transcript.
This is a way to frame analytics work, not a claim that every initiative will produce a measurable financial return. The practical test is whether a project has a clear outcome, an identifiable decision it can influence, and a way to evaluate whether the decision or resulting outcome improves.
How to work from an outcome back to data
The following sequence translates Schmarzo’s ideas into a project-planning method. It is a synthesis of his interview, not a reproduction of the inaccessible exact-title page.
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- Define the outcome. State what should improve and who benefits or bears costs. “Use more data” is not an outcome; a specific improvement in a business process or stakeholder result is.
- Choose measures. Agree on the KPIs or metrics that can indicate whether the outcome is changing. Make clear what is being measured and how the measure relates to the intended value.
- Name the decisions. Identify the real choices people make that may affect those measures. Schmarzo’s distinction is that “Decisions are actionable. Questions may not be.” Questions help explore a problem, but a decision provides a route to action.
- Identify useful analytics and data. Work with the people who own or make the decisions to form hypotheses about what information could help. Check that the necessary data is available and suitable for the purpose; test the approach and learn from results, including when a hypothesis fails.
- Support the use case with data management. Set access, quality, and timeliness requirements according to what the decision needs. Schmarzo describes data management in terms of what it enables: “Not outputs, but outcomes.”
- Put the result into the operating process. Make the insight usable where the decision is made, observe what happens, and refine the measures, decision process, or data as needed with the people involved.
Why the decision is the bridge
A business question can be too broad to guide an analytics project. “What is happening with customers?” may invite exploration without clarifying what anyone should do. A decision-focused framing is narrower: which choice needs to be made, by whom, and what information could improve it? That structure connects business stakeholders to analysts and data scientists and gives a project a practical direction.
It also helps teams distinguish a useful finding from an interesting one. If an analysis cannot plausibly change a decision or help evaluate an outcome, its relevance to the stated value remains unclear. This does not mean exploratory work has no place; it means exploration should eventually connect to a decision or be recognized as exploration rather than represented as value delivered.
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- Wiley
- Language: english
- Book - storytelling with data: a data visualization guide for business professionals
Signal and noise depend on the decision
Schmarzo’s point-of-sale example illustrates why data relevance is contextual. Transaction records might help inform a customer-acquisition decision, while those same records may be noise for a decision about clerk satisfaction or productivity. The data did not change; the decision did. As he puts it, “If you can’t tell me what’s valuable, I can’t distinguish signal from noise in the data.”
Before requesting more data, specify the decision and the outcome it serves. Then ask whether a candidate field, event, or analysis could reasonably inform that decision. This keeps teams from treating volume, novelty, or technical availability as a substitute for relevance.
Who needs to be involved
Value-driven analytics is collaborative work. The interview emphasizes input across business stakeholders, analysts, data scientists, and frontline staff. People who make or support decisions can explain constraints and context that may not appear in a database. That knowledge can shape hypotheses, interpretation, and feature engineering. Analysts and data scientists contribute methods; business owners clarify intended outcomes; frontline workers help determine whether an insight fits the work as it is actually done.
Schmarzo also discusses humility, learning, economics, analytics literacy, and design thinking as useful parts of this work. These are not substitutes for clear measures or sound data. They help teams learn together, account for how value is created, and design an approach people can use.
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Keep the framework practical
For a proposed analytics initiative, discuss these questions before treating data collection or model development as the goal:
- Outcome relevance: What result is the work intended to improve, and for whom?
- Decision actionability: Which decision could change, and who has authority to act?
- Measurement: Which KPI or metric can show whether the outcome or decision is improving?
- Data fit: What quality, access, and timeliness does the use case require?
- Ownership and learning: Which stakeholders and frontline users will help test, interpret, and refine the approach?
- Reuse: Can the resulting data or analytics support other relevant decisions, or does it need to be adapted?
These are planning considerations, not a scored comparison or a guarantee of performance. Schmarzo’s interview sets out the concepts; it does not provide controlled comparative results for different approaches.
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What the title does—and does not—establish
The Data Science Central result for “Schmarzo and the Value-Nauts: The Journey from Data to Value” now redirects to TechTarget and does not expose the original article. Its full text, format, and publication date therefore cannot be established from the accessible page. The explanation above relies on Schmarzo’s separately accessible interview, rather than claiming to reproduce the missing work.
“Value-nauts” is also used for a distinct Sumitomo Chemical team established in January 2023. Sumitomo describes that team in its 2024 annual report as part of data-utilization-led business transformation and value creation. That corporate team should not be confused with the Schmarzo reference in the title.
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