A data silo is when data is locked away inside one department, one tool, or one platform, and cannot easily flow or connect with other systems. The effect is fragmentation (scattered, disconnected data), duplication of effort (retyping, copying), and limited ability to get holistic insight across systems. Examples in practice could look like:

• The math learning platform has its own student performance data, but you cannot export it in a useful format to your overall student information system (SIS) or learning analytics dashboard.
• The English assessment tool does not talk to the curriculum planner, so teachers have to re-enter or manually reconcile data.
• Each vendor uses its own data model or proprietary file format, so moving from one to another means rewriting or manually transforming everything.

Open standards are agreements (often technical specifications) about how data should be structured, labeled, exchanged, and understood — so that different systems can interoperate. Examples in practice could look like:

• The diagnostic assessment tool can export student results in a well-known format (e.g. CSV, JSON, or education-industry standard like IMS LTI, QTI, or Caliper) that other systems can read.
• When you change vendors, you can migrate your records with minimal friction because the data format is understood.
• Systems from different vendors can “plug in” to each other (e.g., assessments, gradebooks, dashboards, curriculum systems) because they share a “data language.”
• Analytics or reporting tools can aggregate data across different learning tools without custom connectors or massive rework.

A metaphor for these differences:

• In a data silo, each garden has its own high walls; you can’t see or reach over to the neighbor’s garden.
• In open standards, the gardens use a shared gate and paths so you can walk between them, bring produce together, and see the full patchwork.