ILC DataMyne(c)

  • We transform unstructured program data into a structured, interactive dashboard by extracting key insights and enriching participant information. This enables dynamic querying and visualization, providing actionable insights for strategic planning and analysis.

TRANSFORMING UNSTRUCTURED PROGRAM DATA INTO

AN INTERACTIVE, SEARCHABLE DASHBOARD

Collect the Corpus of Text Data

We begin by gathering a large set of unstructured, text-based program documents, including:

  • Funded proposals
  • Meeting agendas
  • Meeting notes
  • Participant rosters

This forms the initial corpus of qualitative data to be analyzed.

Extract Keywords from the Funded Proposal

The funded proposal is analyzed to identify strategic priorities and core concepts.

  • These become a list of keywords used to guide the coding process.
  • The keywords represent themes, goals, or actions outlined in the original proposal
Enrich and Expand Raw Participant Data (DataMyne Step 1)

We expand sparse participant info with online research.

  • Add attributes like department. role/title, and college/unit.
  • Infer and add gender using publicly available images and data.

This step ensures we capture a more complete and analyzable profile for each individual

Code the Program Documents in Excel

We use the keywords to code agenda items and meeting notes line by line.

Coded data is entered into a structured Excel spreadsheet, capturing:

  • Date of meeting
  • Type of document
  • Coded themes (based on keywords)
  • Participants associated with each agenda item or notes
Track Participation (DataMyne Step 2)

We link participants to specific meeting events.

  • This adds another layer to the dataset, enabling us to track who was involved in what, and when.
  • The resulting dataset is multi-dimensional, combining themes, timelines, and human engagement.
Structure Data for Migration (DataMyne Step 3)

Now that the dataset has been enriched and coded:

  1. We clean and organize it into CSV files, with clear attribute definitions.
  1. Each row represents a unit (e.g.. a participant in a meeting), and columns represent attributes such as:
  • Name. organization, department
  • Gender (inferred)
  • Meeting attendance
  • Coded topic/themes
Migrate to MySQL for Advanced Querying (DataMyne Step 4)

The structured CSVs are imported into a MySQL database, allowing:

  • Searchable queries across participant attributes and meeting data
  • Dynamic reports on engagement by gender, department, theme, etc.
  • This enables cross-cutting insights across multiple sites, institutions, and funding streams.
Visualize in Tableau

Finally, the MySQL data is visualized using custom Tableau dashboards:

  • Users can explore interactive visual summaries of participation, thematic focus, and program alignment
  • Dashboards support reflection, planning, and reporting, turning qualitative data into actionable insight.

ILC. "DataMyne©." Last modified October 17, 2025. http://www.ilearningcenter.education

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