About this revised edition
Revised September 23, 2026 for clarity and source accuracy. Analytics categories were clarified; an unverified market forecast and broad company claims were replaced with a sourced Google Flu Trends example and practical evaluation guidance.
Analytics earns its place by improving a specific decision: what to replenish, which service problem to investigate or where to direct limited capacity. Assess a dashboard or model through the action it informs and the evidence behind that action.
Start by naming the decision, its owner and the point at which it must be made. Then ask what information would change the choice. This keeps an analytics project connected to an operating need and makes it easier to judge whether the work is useful.
Match the analysis to the question
IBM's overview of analytics distinguishes descriptive, diagnostic, predictive and prescriptive approaches. The distinction helps a team select the right method and recognize what the output can establish.
- Descriptive analysis summarizes what happened. For example, a service team might compare completed requests and backlog age across periods. Agree the definitions and reporting boundary before comparing results.
- Diagnostic analysis investigates possible explanations. A team might examine whether delays cluster around a particular handoff. A relationship in the data is a reason to investigate; it does not by itself establish a cause.
- Predictive analysis estimates a future outcome. A demand model can support planning, provided its uncertainty and performance are understood at the horizon where a decision is needed.
- Prescriptive analysis evaluates possible actions. A replenishment model might compare order quantities subject to storage, service and supplier constraints. Its recommendation depends on those objectives and assumptions.
These approaches can be combined. More complex analysis is warranted when it answers a material question that simpler analysis cannot resolve reliably.
Reliable decisions need more than a model
Check how data was created before interpreting it. Missing records, changing definitions and inconsistent identifiers can alter the apparent story. A report of late deliveries, for example, is difficult to compare if one team measures against the customer's requested date and another against a revised promise date.
Document the source, definition, owner and update frequency of each important measure. Identify what is absent from the data and whether the available records represent the people, products or operating conditions affected by the decision.
For a predictive model, compare performance with a simple baseline on data that was not used to fit it. Review the errors that matter to the business: overestimating demand and underestimating it may create different costs. Establish an owner for monitoring and a response when performance changes.
A specific historical lesson: Google Flu Trends
In an October 2014 account of Google Flu Trends, Google acknowledged that the model overestimated U.S. flu levels during the 2012/2013 season relative to the CDC's reports. Google described subsequent retraining and a new model for the 2014/2015 season that would incorporate CDC data as the season progressed.
The lesson for a business team is to plan for evaluation and revision after deployment. A model's earlier performance does not settle whether it remains useful as conditions change. This is a particular historical project, not evidence of a company-wide inability to use analytics.
Make implementation an operating responsibility
A useful first increment connects one recurring decision with a small set of agreed measures. Give the decision owner time to review the output, question it and record the action taken. Where employees need new information or skills, include training and support in the scope.
Agree who resolves data-quality issues, approves changes to definitions and maintains access. For sensitive information, involve the relevant privacy and security owners when defining collection, use and permissions. These responsibilities belong in the delivery plan alongside technical work.
Review adoption and the operating outcome together. If users ignore a report, examine whether it arrives in time, is understandable and answers their actual question. If a measure improves, examine other changes that may have contributed before attributing the result to analytics.
Decide what evidence is enough
Before expanding an analytics project, agree what the pilot must demonstrate. That may include consistent metric definitions, usable outputs, acceptable model performance and an accountable support owner. Record remaining uncertainty and the conditions that would trigger a review.
Assuras's technology solutions approach begins with the user task and the decision to be supported. For a related operating example, read Smart Supply Chains. When the underlying question is which initiative deserves investment, explore management consulting.
