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Data Management 2.0 starts with a business goal—not a request to clean data. The approach works backward from company ambitions and the metrics that define progress to analytics use cases, the data they require, and the results teams can measure. Its central test is practical: which data work can produce meaningful business value, and can the organization deliver it?
Why reframe data management?
Data quality and organization matter, but treating them as isolated technical chores can make it difficult to explain why the work deserves time and funding. Data Management 2.0 connects data work to decisions and outcomes. Stakeholders agree on what the business is trying to achieve; teams then identify how analytics might help and what data is needed to test that possibility.
This shifts analytics from a collection of disconnected technical projects toward an empirical process. Bill Schmarzo described the scientific mindset in a webinar as “an empirical method for gathering knowledge and insights to prove/disprove a specific hypothesis.” In practice, a use case should make a testable claim about how an action informed by data could affect a business ambition.
How the framework works
- Align on business ambitions. Stakeholders and shareholders establish the company’s priorities so teams share a clear direction.
- Define progress metrics. Business functions identify measures that indicate whether the company is moving toward those ambitions.
- Develop analytics use cases. Data and analytics experts propose ways analysis could influence the ambitions, rather than starting with a preferred technology or dataset.
- Specify data and measurement needs. For each use case, identify the required data assets and the tracking metrics needed to assess its results.
- Prioritize the candidates. Place potential use cases on a matrix and select work that combines substantial business impact with feasible execution.
The sequence is a way to connect strategy to execution: an ambition gives metrics their purpose, metrics help focus use cases, and each use case clarifies what data must be available and what outcome should be tracked.
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How to compare and prioritize use cases
The prioritization matrix is the framework’s explicit decision device. It should help a team compare candidate work on more than projected upside: a high-impact idea may still be a poor first choice if its data is unavailable or its outcome cannot be measured. Consider these dimensions together:
- Business impact: How directly could the use case affect a stated ambition?
- Implementation feasibility: Can the organization deliver it with the available data, systems, time, and skills?
- Required data assets: Which data must be accessible, usable, and relevant to the use case?
- Metric measurability: Can the team track the intended result and distinguish it from activity that does not demonstrate progress?
- Available expertise: Does the organization have the experience to define, build, and evaluate the use case?
- Ongoing maintenance: What continuing effort will be needed to keep the data and analytics useful?
These dimensions make trade-offs visible. A modest use case with clear measures and readily available data may be a more credible starting point than a more ambitious proposal that depends on uncertain assets or expertise. The matrix supports a reasoned choice; it does not substitute for agreement on business priorities or evidence about results.
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What smaller companies should consider
Smaller companies may know their ambitions and key performance indicators yet lack specialized experience in selecting analytics use cases, estimating impact, or judging feasibility. The framework identifies two ways to address that gap: hire data and analytics specialists or consult external experts. Either route should strengthen the company’s ability to connect proposed work to its own goals and measures.
Before committing, a smaller organization can make the decision more concrete by asking whether it can name the business ambition, identify a metric that signals progress, specify the data needed for a proposed use case, and explain how it would assess the result. If those links are unclear, further technical work alone will not resolve the prioritization problem.
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The framework offers a way to align people around measurable outputs and reduce execution risk by prioritizing work against impact and feasibility. It does not guarantee a particular return, prescribe a software product, or make data quality irrelevant. Rather, it gives data management a business rationale: invest in the data assets and tracking needed for use cases that are tied to explicit ambitions.
That distinction answers the title’s underlying questions. Many people may want clean data in the abstract; fewer may want to do the work or pay for it without a clear purpose. Data Management 2.0 makes that purpose the starting point, so the organization can decide which data work is worth doing and how to judge whether it helped.
Source: Bill Schmarzo, “Reframing Data Management: Data Management 2.0”.
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