Which step follows data collection in a typical analytics workflow?

Which step follows data collection in a typical analytics workflow?

• A. Publishing a report without validation
• B. Model building and validation
• C. Ignoring data quality
• D. Delaying insights indefinitely

Answer: (B)

After collecting data, the next step is to build a model and validate it. Modeling turns raw data into patterns, predictions, or quantified relationships that can drive decisions. Validation checks how well those patterns hold up on new, unseen data, helping ensure the results aren’t just a fluke from the current dataset. Techniques like splitting data into training and test sets or using cross-validation provide a realistic assessment of performance and guard against overfitting. This step is essential before sharing findings because it confirms that insights are reliable and generalizable beyond the initial data. Publishing a report without validation risks basing decisions on unstable or misleading results. Ignoring data quality would undermine any analysis from the start, and delaying insights indefinitely wastes value.

After collecting data, the next step is to build a model and validate it. Modeling turns raw data into patterns, predictions, or quantified relationships that can drive decisions. Validation checks how well those patterns hold up on new, unseen data, helping ensure the results aren’t just a fluke from the current dataset. Techniques like splitting data into training and test sets or using cross-validation provide a realistic assessment of performance and guard against overfitting. This step is essential before sharing findings because it confirms that insights are reliable and generalizable beyond the initial data. Publishing a report without validation risks basing decisions on unstable or misleading results. Ignoring data quality would undermine any analysis from the start, and delaying insights indefinitely wastes value.