Data analysis and decision support
A focused review of the data a school or district already has, the questions leaders are trying to answer, and the gaps that keep those data from becoming useful decisions.
A focused consulting engagement that turns scattered needs, tools, data, or workflows into a clearer system schools can test, refine, and use.
Engagement workflow
Start with the questions the school is trying to answer instead of beginning with the dashboard or spreadsheet.
Review assessment, attendance, gradebook, survey, intervention, and local workflow data for usefulness and limitations.
Identify format issues, missingness, inconsistent labels, timing gaps, and places where data sources do not connect.
Recommend reporting views, meeting routines, and pilot questions that help teams act on what the data shows.
The goal is not another static report. The goal is a usable decision process: clearer priorities, cleaner evidence, practical workflows, and next steps that match the capacity of the school or district.
Source material
Supported by the local blog draft arguing that schools often have too much disconnected data and too few systems for using it well.
Backed by local student analysis work that combines assessment and school data into reports rather than treating each export separately.
Connects to the existing service-page example where planning, assessment, student tracking, and follow-up analysis become one improvement cycle.
Example output
The numbers below are illustrative, not client data. They are based on the same local project patterns: clean inputs, summarize patterns, suppress sensitive detail, and connect findings to next actions.
Sample audit
Modeled after a local project pattern of inspecting exports, cleaning fields, summarizing gaps, and turning the result into next-step recommendations.
Readiness by data source
Matched sources
4 of 6
Enough for a first pilot
Priority gap
Intervention history
Most inconsistent labels
Output
3 reporting views
Leader, PLC, support team
Most engagements should start small: one clear problem, one limited data or workflow scope, one set of users, and a short review cycle. That creates enough evidence to decide what should be refined, stopped, or expanded.
Next step
A focused first phase can clarify the problem, test the workflow, and show whether the support is useful before a larger rollout.