Structured Data
.png)
Structured data is information stored in a predefined schema with typed fields, relational keys, and consistent formatting so that software can query, filter, sort, and compute against it without ambiguity about what any value means.
What actually qualifies as structured data inside an AEC firm
Project records in a CRM with a construction cost field typed as currency, a delivery method field constrained to a controlled vocabulary, and a contract date stored as ISO 8601: that is structured data. A spreadsheet where one cell reads "$4.2M," the next reads "4,200,000," and a third reads "approx. four million" is not, regardless of its grid format. The defining characteristic is schema enforcement, not visual organization. Most AEC firms have structured data in pockets: Deltek Vision or Vantagepoint project records, Salesforce opportunity stages, BST Global personnel certifications. The problem is that these systems rarely share a common schema, so a "project type" field in your ERP may not map cleanly to the same concept in your CRM, and reconciling them requires either a data governance decision or a manual translation layer.
Where structured data shows up in a live pursuit
Go/no-go scorecards pull structured data when they query how many projects of a given type, above a given fee threshold, were completed in a given geography within the last five years. SF-330 Section F requires exactly that kind of answer, and the firms that answer it fastest and most accurately are the ones whose project records were entered consistently at closeout, not assembled under a two-week deadline. Shortlist pools of three to five firms tend to separate on relevance and specificity; a firm that can filter to "water treatment plants, design-build, over $20M construction cost, awarded 2019 or later" in thirty seconds has a material advantage over one reconstructing that list from email threads. Fee proposal calculations also depend on structured cost and labor data; if those fields were entered inconsistently, the numbers your estimator pulls may not mean what they appear to mean.
The misconception that costs firms in proposal quality
Most marketing teams assume their data is more structured than it actually is. A field exists in the system; therefore the data is structured. But a text field that someone uses to type "CMAR" in one record and "Construction Manager at Risk" in another is functionally unstructured: software cannot reliably group those two records without normalization. The real discipline is upstream, at project setup and closeout, enforcing controlled vocabularies and required fields before a pursuit creates urgency. Kantiv addresses this by treating project records, personnel data, and past proposal content as a unified knowledge layer with consistent structure, so that when a pursuit opens, the query against relevant experience returns verified results rather than a best-guess assembly of whatever was entered last.
Related terms