{
  "format": "dataset-manifest/1",
  "slug": "company-culture-and-employee-engagement-app",
  "version": 3,
  "seed": "company-culture-and-employee-engagement-app:1",
  "spec": {
    "format": "dataset/1",
    "slug": "company-culture-and-employee-engagement-app",
    "name": "Employee Engagement Insights",
    "tagline": "Employee engagement metrics, recognition comments, wellness challenges, and feedback submissions from a workplace.",
    "description": "Employees participate in initiatives that enhance workplace culture and foster engagement through a variety of activities. The company prioritizes employee recognition, wellness challenges, and feedback submissions as core components of its strategy to create a positive work environment. As team members partake in recognition events, the data reflects a rich collection of comments highlighting achievements and contributions. Concurrently, wellness challenges promote health and well-being, generating a clustered dataset of entries that showcases employee participation and enthusiasm. However, feedback submissions vary in frequency, indicating certain areas may require more attention and improvement.\n\nThe tables in this dataset are interrelated to provide a comprehensive view of employee engagement. Each row in the 'employees' table represents an individual worker, capturing their unique identifiers and attributes. 'Recognition' entries are linked to employees through their IDs, showing the acknowledgment that each person receives. The 'wellness_challenges' table tracks different health initiatives and their participation rates, while 'feedback_submissions' captures the sentiments and suggestions from employees, directly associated with their IDs as well. Finally, 'engagement_metrics' aggregates data points from all other tables, providing insights into overall employee engagement levels through various metrics derived from participation and feedback.\n\nData from these tables answers key questions about employee experiences and company culture. For example, you can analyze which types of recognition are most appreciated, the popularity of specific wellness challenges, or trends in employee feedback over time. This synthetic dataset is ideal for practicing data analysis skills, as it offers a rich variety of metrics and relational data points to explore, allowing developers to test their understanding of database querying and manipulation.",
    "domain": "hr",
    "shape": "event-log",
    "useCases": [
      "analytics-demo",
      "etl-pipelines"
    ],
    "keywords": [
      "employee engagement",
      "workplace culture",
      "wellness challenges",
      "recognition",
      "feedback",
      "sentiment analysis"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "employees": {
        "description": "This table contains records of employees within the organization, linking to their engagement activities and feedback submissions.",
        "rows": 100,
        "columns": {
          "employee_id": {
            "kind": "pk",
            "description": "Unique identifier for each employee."
          },
          "full_name": {
            "kind": "fullName",
            "description": "The full name of the employee."
          },
          "department": {
            "kind": "enum",
            "values": [
              "HR",
              "Engineering",
              "Marketing",
              "Sales",
              "Support"
            ],
            "description": "Department where the employee works.",
            "weights": [
              5,
              4,
              3,
              2,
              1
            ]
          },
          "hire_date": {
            "kind": "date",
            "from": "2015-01-01",
            "to": "2023-01-01",
            "description": "The date when the employee was hired."
          }
        }
      },
      "recognition": {
        "description": "This table captures instances of employee recognition, showcasing who recognized whom and the nature of the recognition.",
        "rows": {
          "per": "employees",
          "via": "employee_id",
          "min": 1,
          "max": 10,
          "skew": "long-tail"
        },
        "columns": {
          "recognition_id": {
            "kind": "pk",
            "description": "Unique identifier for each recognition event."
          },
          "employee_id": {
            "kind": "fk",
            "ref": "employees.employee_id",
            "description": "The employee being recognized."
          },
          "recognizer_id": {
            "kind": "fk",
            "ref": "employees.employee_id",
            "nullable": true,
            "description": "The employee who gave the recognition."
          },
          "message": {
            "kind": "sentence",
            "min": 10,
            "max": 200,
            "description": "The message of recognition."
          },
          "date": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "dayProfile": "business",
            "description": "Date and time of the recognition."
          }
        }
      },
      "wellness_challenges": {
        "description": "This table records participation in wellness challenges by employees, detailing which challenge they participated in.",
        "rows": {
          "per": "employees",
          "via": "employee_id",
          "min": 1,
          "max": 5,
          "skew": "long-tail"
        },
        "columns": {
          "challenge_id": {
            "kind": "pk",
            "description": "Unique identifier for each wellness challenge."
          },
          "employee_id": {
            "kind": "fk",
            "ref": "employees.employee_id",
            "description": "The employee participating in the challenge."
          },
          "challenge_name": {
            "kind": "enum",
            "values": [
              "Step Challenge",
              "Hydration Challenge",
              "Mindfulness Challenge"
            ],
            "description": "The name of the wellness challenge.",
            "weights": [
              6,
              2,
              1
            ]
          },
          "start_date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Start date of the wellness challenge."
          },
          "end_date": {
            "kind": "date",
            "from": "2023-01-02",
            "to": "2023-12-31",
            "description": "End date of the wellness challenge."
          }
        }
      },
      "feedback_submissions": {
        "description": "This table contains feedback submissions from employees regarding their workplace experiences and suggestions for improvement.",
        "rows": {
          "per": "employees",
          "via": "employee_id",
          "min": 1,
          "max": 3,
          "skew": "heavy-tail"
        },
        "columns": {
          "feedback_id": {
            "kind": "pk",
            "description": "Unique identifier for each feedback submission."
          },
          "employee_id": {
            "kind": "fk",
            "ref": "employees.employee_id",
            "description": "The employee providing feedback."
          },
          "feedback_type": {
            "kind": "enum",
            "values": [
              "Positive",
              "Neutral",
              "Negative"
            ],
            "description": "Type of feedback being submitted.",
            "weights": [
              7,
              2,
              1
            ]
          },
          "feedback_text": {
            "kind": "paragraph",
            "min": 20,
            "max": 500,
            "description": "The text of the feedback provided."
          },
          "submission_date": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "dayProfile": "business",
            "description": "Date and time of the feedback submission."
          }
        }
      },
      "engagement_metrics": {
        "description": "This table aggregates engagement metrics over time, linking to the employees and their participation in activities.",
        "rows": {
          "per": "employees",
          "via": "employee_id",
          "min": 1,
          "max": 12,
          "skew": "long-tail"
        },
        "columns": {
          "metric_id": {
            "kind": "pk",
            "description": "Unique identifier for each engagement metric entry."
          },
          "employee_id": {
            "kind": "fk",
            "ref": "employees.employee_id",
            "description": "The employee whose metrics are recorded."
          },
          "recognition_count": {
            "kind": "int",
            "min": 0,
            "max": 100,
            "description": "Number of recognition received by the employee.",
            "distribution": "long-tail"
          },
          "wellness_participation": {
            "kind": "int",
            "min": 0,
            "max": 10,
            "description": "Number of wellness challenges participated in by the employee.",
            "distribution": "long-tail"
          },
          "feedback_count": {
            "kind": "int",
            "min": 0,
            "max": 10,
            "description": "Number of feedback submissions made by the employee.",
            "distribution": "long-tail"
          },
          "date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Date corresponding to the engagement metrics."
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "categorical",
      "timestamped",
      "high-cardinality"
    ],
    "analysisQuestions": [
      "How frequently do employees participate in wellness challenges?",
      "What types of recognition messages are most common?",
      "Is there a correlation between recognition received and feedback submissions?",
      "Which departments have the highest engagement in wellness activities?",
      "What trends can be identified in employee feedback over time?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Employees and Recognition",
        "sql": "SELECT e.full_name, r.message FROM employees e JOIN recognition r ON e.employee_id = r.employee_id;",
        "dialect": "postgres"
      },
      {
        "title": "Aggregate Recognition Count by Department",
        "sql": "SELECT e.department, COUNT(r.recognition_id) AS recognition_count FROM employees e LEFT JOIN recognition r ON e.employee_id = r.employee_id GROUP BY e.department;",
        "dialect": "postgres"
      },
      {
        "title": "Wellness Participation by Employee",
        "sql": "SELECT e.full_name, COUNT(w.challenge_id) AS challenge_count FROM employees e LEFT JOIN wellness_challenges w ON e.employee_id = w.employee_id GROUP BY e.full_name;",
        "dialect": "postgres"
      },
      {
        "title": "Feedback Trends Over Time",
        "sql": "SELECT f.submission_date, COUNT(f.feedback_id) AS feedback_count FROM feedback_submissions f GROUP BY f.submission_date ORDER BY f.submission_date;",
        "dialect": "postgres"
      },
      {
        "title": "Engagement Metrics Analysis",
        "sql": "SELECT e.full_name, AVG(em.recognition_count) AS avg_recognition FROM employees e JOIN engagement_metrics em ON e.employee_id = em.employee_id GROUP BY e.full_name;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "HR Analytics Dashboard",
        "body": "HR analysts can utilize this dataset to create dashboards that visualize employee engagement metrics over time. By exploring the relationships between recognition, wellness participation, and feedback, analysts can identify trends and areas for improvement in workplace culture."
      },
      {
        "title": "Employee Engagement Research",
        "body": "Researchers focusing on workplace engagement can analyze this data to publish findings on the impact of recognition and wellness activities on employee satisfaction. The insights derived from this dataset can inform future studies and workplace initiatives."
      },
      {
        "title": "ETL Pipeline Development",
        "body": "Data engineers can use this synthetic dataset to build and test ETL pipelines that extract engagement data, transform it into usable formats, and load it into data warehouses for further analysis. This practice is essential for maintaining accurate and insightful reporting."
      },
      {
        "title": "Sentiment Analysis Projects",
        "body": "Data scientists can leverage the feedback submissions table to conduct sentiment analysis on employee feedback. By applying natural language processing techniques, they can uncover underlying sentiments that may impact overall employee engagement."
      }
    ]
  },
  "counts": {
    "employees": 100,
    "engagement_metrics": 442,
    "feedback_submissions": 136,
    "recognition": 352,
    "wellness_challenges": 228
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