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Crossing the analytics chasm means moving beyond reports that describe past performance to predictive insights and prescriptive actions that change business decisions. Bill Schmarzo’s framework treats this as an economic and organizational challenge as much as a technical one: start with a business outcome, prioritize a manageable set of use cases, and build the data and analytics capabilities needed to act on them.
What the analytics chasm means
Traditional business reporting answers questions such as what happened, where performance changed, or which products sold. Predictive analytics estimates what is likely to happen; prescriptive analytics helps determine what action to take. The transition is not complete when a team produces a sophisticated model or dashboard. It matters when analysis informs a business decision or operational action.
Schmarzo describes a broader capability shift alongside that change in purpose:
| Dimension | Retrospective monitoring | Predictive and prescriptive analytics |
|---|---|---|
| Question | What happened? | What is likely to happen, and what should we do? |
| Level of analysis | Aggregated results | More granular histories, potentially at the level of individual people or devices |
| Data inputs | Often restricted to tabular data | Broader internal and external data, including structured and unstructured sources |
| Timing | Batch reporting after events | Timely analysis that can inform operational decisions |
| Intended result | Monitoring and explanation | Action informed by customer, product, service, or operational insight |
These are distinctions in Schmarzo’s framework, not a universal maturity scale. More granular data, broader access, or faster processing can enable useful analysis, but none creates business value by itself.
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How to move from dashboards to decisions
A practical way to cross the chasm is to work from business priorities toward the analytics required—not from a technology purchase toward a hoped-for use. Use cases give business stakeholders and data teams a shared unit of work: a defined decision or outcome, the data needed to support it, and a way to judge whether implementation is worthwhile.
- Start with a material business initiative. Identify a financial, customer, or operational outcome that matters, and clarify the drivers behind it. Avoid starting with a tool or an open-ended request to “use AI” or “do big data.”
- Generate and assess candidate use cases. Describe the business decision each candidate would improve. Validate the need with the stakeholders who own that decision, then assess potential value and implementation feasibility.
- Prioritize a manageable number. Compare candidates on business value and feasibility, and focus on the strongest opportunities. Schmarzo warns against pursuing too many use cases at once: spreading teams across an unmanageable portfolio makes it harder to establish value or deliver working outcomes.
- Assemble relevant data at useful granularity. Identify what each leading use case needs, including appropriate internal or external sources and structured or unstructured data. Choose a level of detail and update cadence that serve the decision; more data is not automatically better.
- Align business, data science, and technology teams. Agree on the decision the analysis is meant to support, the outcome stakeholders care about, and the practical constraints on applying an insight. Analytics is more likely to influence action when the people building it work with the people who will use it.
- Advance incrementally and validate. Test whether the data and approach are feasible and whether the result is relevant to the business decision. Treat a proof of concept as an experiment with risks and limits—not as a guaranteed solution or a promise of production impact.
Choose use cases by value and feasibility
Schmarzo’s “Big Data Game Board” approach emphasizes collaborative selection and assessment rather than accepting every proposed project. Business value asks whether a use case can materially improve an important outcome. Implementation feasibility asks whether the organization can realistically deliver it with the available data, capabilities, and operating context.
| Assessment | Questions to ask | How to use the answer |
|---|---|---|
| Business value | Which financial, customer, or operational outcome could change? Is there a consequential decision to improve? | Favor work connected to a material business initiative rather than an interesting analysis without a clear decision. |
| Implementation feasibility | Can the relevant data be accessed and analyzed at the needed level and cadence? Can stakeholders act on the result? | Surface data, delivery, and adoption risks early; adjust scope or sequence rather than promising an outcome the team cannot support. |
A use case that looks valuable but cannot be implemented may need a narrower scope or enabling work first. A feasible project with little connection to a consequential decision may not deserve priority. Assessing both dimensions helps teams make these trade-offs explicitly.
What changes—and what does not
Crossing the chasm does not mean replacing every dashboard with a predictive model, collecting every available data source, or making every decision in real time. It means applying analysis where it can improve a specific decision or action. Some questions still call for reliable retrospective reporting; others may justify prediction or prescription. The useful capability is matching the approach to the business need.
Schmarzo’s material also makes clear that technology experiments should not carry exaggerated promises. A proof of concept can help establish feasibility, but it is not evidence by itself that the organization has created business value. Keep expected outcomes, implementation risks, and the route from analysis to action explicit as a use case progresses.
Further reading
For a related treatment of the economics behind this approach, Packt’s chapter on Bill Schmarzo’s book on data and analytics economics describes becoming value-driven and applying data and analytics economics use case by use case.
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About the named work
The exact original page for “Crossing the Big Data, Data Science and Analytics Chasm” is not established here. The European Parliamentary Research Service cites a related Bill Schmarzo article, “Crossing the big data analytics chasm,” dated September 25, 2018; that citation does not prove it is the same work or establish the canonical page. Schmarzo’s related “The Big Data Game Board™,” published by KDnuggets on November 19, 2018, discusses moving from retrospective reporting toward predictive insights and prescriptive actions, along with collaborative use-case selection.
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