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What does “from data to value” mean?
Data has no automatic business value simply because an organization collects it. In Schmarzo’s framing, its value depends on whether it can help people make better decisions that advance a meaningful outcome. That shifts the starting question from “What data do we have?” to “What outcome matters, and what decisions can influence it?”
This is a practical way to connect business priorities with analytics work: make the desired result explicit, identify how decisions affect it, and select data and analysis that can improve those decisions. Schmarzo summarized the distinction between exploration and action this way: “Decisions are actionable. Questions may not be.” (Leaders of Analytics interview, October 24, 2022)
How to work from an outcome back to data
The following sequence synthesizes ideas Schmarzo discusses in that interview. It is a practical interpretation, not a claim about the unavailable original page’s structure.
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- Define the outcome and who it matters to. State what the organization wants to change and identify the stakeholders who benefit from that change or bear its costs. Avoid treating a broad ambition such as “be more data-driven” as an outcome.
- Choose measures that make progress visible. Agree on KPIs or other metrics that indicate whether the outcome is improving. Schmarzo stresses that an organization needs to define how it creates value and measure the effectiveness of that creation: “If you don’t do that, you will never be value driven.” (Leaders of Analytics interview, October 24, 2022)
- Identify the decisions that can move those measures. Be specific about who decides what, when, and with what possible actions. A useful decision is one that can be changed in practice—not merely a topic to investigate.
- Explore how data and analytics could improve those decisions. Work with the people involved to form and test ideas about relevant data, analysis, and possible actions. Treat failed approaches as learning, rather than assuming a dataset or model must be useful because it exists.
- Match data management to the use case. Determine what data quality, access, and timeliness the decision requires. Build or improve data-management capabilities to meet that need, rather than measuring success only by technical outputs.
- Put results into the operating process and refine the work. Bring useful analysis back to the people making the decision. Review whether the measures, decisions, and supporting data still fit the intended outcome, and adjust collaboratively.
Why decisions are the bridge between business and analytics
Business goals can be too broad to guide an analytics task, while data questions can remain interesting without leading to a change in action. A decision connects the two: it identifies a point where someone can act and where better information might matter. It also gives the team a way to discuss whether the analysis is useful: did it help improve the decision or its consequences?
Questions still have a role in exploration. The distinction is not that questions are unimportant, but that a project needs to translate inquiry into an actionable choice if it is to pursue a defined business outcome.
How to tell signal from noise
Data relevance depends on the decision being considered. Schmarzo uses point-of-sale data to illustrate the idea: information that may help analyze customer acquisition is not necessarily the information needed to understand clerk satisfaction or productivity. The same record can be useful for one purpose and noise for another.
That is why teams should identify the outcome and decision before deciding that a dataset is valuable. As Schmarzo puts it, “If you can’t tell me what’s valuable, I can’t distinguish signal from noise in the data”. (Leaders of Analytics interview, October 24, 2022)
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What data management should deliver
Data management matters when it enables the intended use: people need suitable data they can access in time to support the decision. Schmarzo describes the aim as “Not outputs, but outcomes.” (Leaders of Analytics interview, October 24, 2022) In practice, a technically successful data deliverable is not enough if the people responsible for the decision cannot use it or if it arrives too late for the process.
Who needs to work together?
Value-oriented analytics is collaborative rather than a handoff from business to a technical team. Schmarzo’s interview emphasizes the contributions of business stakeholders, analysts, data scientists, and frontline staff. Stakeholders help define value and decisions; analysts and data scientists help investigate how evidence might improve them; frontline staff contribute knowledge of how work actually happens and what information or features may be meaningful in that setting.
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The interview also discusses humility, learning, economics, analytics literacy, and design thinking as relevant capabilities. These are not substitutes for clear measures or decision ownership; they help teams understand the problem, communicate across roles, and adapt as they learn.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Is “Value-Nauts” a separate organization?
The similar name also appears in Sumitomo Chemical’s corporate context. Its Annual Report 2024 describes a “Value-nauts” team established in January 2023 for data-utilization-led business transformation and value creation. That is a distinct corporate team, not evidence that the title refers to the same group as Schmarzo’s value-driven approach. (Sumitomo Chemical, Annual Report 2024)
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Where to read more
The exact-title result associated with Data Science Central now redirects to TechTarget and does not expose the original article, so its publication date, format, and full contents cannot be confirmed from the available page. (Data Science Central / TechTarget)
For related context, a surfaced excerpt from Bill Schmarzo’s Big Data MBA: Driving Business Strategies with Data Science describes a value-engineering framework linking business initiatives, stakeholders, decisions, analytics, data, and architecture. It is related further reading, not verification that the book is the exact work named in the title. (Scribd excerpt)
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