Connect Power BI

Connecting Power BI to Tallyfy Analytics

Section titled “Connecting Power BI to Tallyfy Analytics”

Connect Power BI to Tallyfy Analytics through Amazon Athena¹ and build dashboards from your workflow data. The setup takes about 15 minutes.

With this connection, you can build interactive dashboards showing bottlenecks and team performance, create metrics tailored to your KPIs, schedule automated reports, and merge Tallyfy data with other business systems like your CRM or ERP.

Connection architecture

Section titled “Connection architecture”

Power BI connects to your Tallyfy data through Amazon Athena via an ODBC driver.

What to notice:

• Use Access Key/Secret authentication - IAM credentials cause errors
• All connections must use the us-west-2 region

Authentication methods

Section titled “Authentication methods”

When Tallyfy activates your Analytics subscription, you receive two credential sets:

Authentication Method	Description	Use for
IAM Username/Password	Web console credentials	AWS web console only
Access Key/Secret	Token-based authentication	Power BI (recommended)

Use Access Key authentication

IAM credentials trigger the error “The security token included in the request is invalid” when used with Power BI. Always use your Access Key and Secret Access Key instead.

Connection setup

Section titled “Connection setup”

Requirements

Section titled “Requirements”

• Power BI Desktop installed
• Tallyfy Analytics subscription activated
• Amazon Athena ODBC driver installed
• Access Key credentials provided by Tallyfy

Step 1: Install the Amazon Athena ODBC driver

Section titled “Step 1: Install the Amazon Athena ODBC driver”

1. Download the Amazon Athena ODBC driver from the AWS documentation page ↗
2. Run the installer and follow the on-screen instructions
3. Complete the installation before proceeding

Step 2: Configure the ODBC connection

Section titled “Step 2: Configure the ODBC connection”

1. On Windows, open the ODBC Data Source Administrator (64-bit)
2. Go to the System DSN tab
3. Click Add to create a new data source
4. Select the Amazon Athena ODBC Driver and click Finish
5. Configure the connection using the settings below
6. Click Test to verify, then OK to save

Setting	Value
Data Source Name	Tallyfy Analytics
Description	Tallyfy workflow data
AWS Region	us-west-2
S3 Output Location	As provided by Tallyfy
Authentication Option	IAM Profile
Access Key	Your Tallyfy-provided Access Key
Secret Key	Your Tallyfy-provided Secret Access Key
Workgroup	Your organization ID (as provided by Tallyfy)
Catalog	AwsDataCatalog

Step 3: Connect Power BI to Athena

Section titled “Step 3: Connect Power BI to Athena”

1. Launch Power BI Desktop
2. Click Get Data in the ribbon menu
3. Select More… to view all connectors
4. Choose ODBC from the Database section and click Connect
5. From the DSN dropdown, select the Tallyfy Analytics connection you created
6. Click OK to establish the connection
7. In the Navigator window, expand AwsDataCatalog
8. Select your database (typically your organization name)
9. Choose the tables you want to analyze
10. Click Load to import the data into Power BI

Working with Tallyfy data

Section titled “Working with Tallyfy data”

You’re connected. Your Tallyfy Analytics data is organized as:

• Per-process Parquet files - Each process run is exported as a single flat table. Rows are differentiated by the type column: kick_off_task (kick-off form data), process_task (task-level data), process_task_data (form field answers within tasks), and process_task_comment (comments). Columns include process metadata (blueprint_name, process_status, process_owner), task details (process_task_name, completed_on, due_by, user_assigned), and form field data (form_field_type, question_in_form_field, answer_in_form_field).
• Members table - User data including email, full_name, job_title, is_active, timezone, and login activity timestamps.

Time metrics show elapsed time

Completion times show elapsed calendar time, not actual working time. A “3-day” task might be 30 minutes of work interrupted by a weekend. For accurate effort metrics, implement time tracking fields as described in how to track time spent on tasks.

Calculating accurate process completion times with NaN handling

The process_last_modified field updates whenever a process is edited, archived, or modified - not just when it’s completed. For accurate completion time calculations, use the maximum completed_on timestamp from all tasks in the process instead.

Important: Date fields may contain the string “NaN” when the original database value was NULL. Always filter these out to prevent errors.

DAX Formula (Calculated Column) - Process Completion Timestamp:

Process_Actual_Completion =
CALCULATE(
    MAXX(
        FILTER(
            'Query1',
            'Query1'[process_id] = EARLIER('Query1'[process_id])
            && 'Query1'[type] = "process_task"
            && NOT(ISBLANK('Query1'[completed_on]))
            && 'Query1'[completed_on] <> "nan"
        ),
        DATEVALUE('Query1'[completed_on]) + TIMEVALUE('Query1'[completed_on])
    )
)

DAX Formula (Measure) - Average Process Completion Time:

Average_Process_Completion_Time =
VAR ProcessCompletions =
ADDCOLUMNS (
    SUMMARIZE (
        FILTER (
            'Query1',
            'Query1'[process_status] = "complete"
            && 'Query1'[type] = "process_task"
        ),
        'Query1'[process_id]
    ),
    "First_Assigned_Date",
    CALCULATE (
        MINX (
            FILTER (
                'Query1',
                NOT(ISBLANK('Query1'[assigned_or_shown_on]))
                && 'Query1'[assigned_or_shown_on] <> "nan"
            ),
            DATEVALUE('Query1'[assigned_or_shown_on]) + TIMEVALUE('Query1'[assigned_or_shown_on])
        )
    ),
    "Last_Task_Completed_Date",
    CALCULATE (
        MAXX (
            FILTER (
                'Query1',
                NOT(ISBLANK('Query1'[completed_on]))
                && 'Query1'[completed_on] <> "nan"
            ),
            DATEVALUE('Query1'[completed_on]) + TIMEVALUE('Query1'[completed_on])
        )
    )
)
RETURN
AVERAGEX (
    FILTER (
        ProcessCompletions,
        NOT(ISBLANK([First_Assigned_Date]))
        && NOT(ISBLANK([Last_Task_Completed_Date]))
    ),
    DATEDIFF([First_Assigned_Date], [Last_Task_Completed_Date], DAY)
)

Key Points:

• Timestamp Precision: Using DATEVALUE() + TIMEVALUE() preserves exact completion times down to the second. Using DATEVALUE() alone would only give day-level precision, losing hours, minutes, and seconds - resulting in fractional days for sub-day durations (e.g., 2.3 days instead of just 2 days)
• Groups by process_id ONLY (not by assigned_or_shown_on) to avoid duplicate rows
• Filters out both ISBLANK and “nan” string values
• Calculates when the last task was completed, representing the true process completion timestamp
• Works correctly even if processes are archived/unarchived later

Why NaN Appears: NULL database values → Pandas conversion → Parquet export → string “NaN”

Configuration notes

Section titled “Configuration notes”

• You must connect to the us-west-2 region - other regions won’t work
• Your workgroup must match your organization ID exactly
• The catalog name is always AwsDataCatalog (exact capitalization matters)
• Use Direct Query mode² for datasets over 1GB
• Set up refresh scheduling if you need reports updated automatically

Video tutorial

Section titled “Video tutorial”

Troubleshooting

Section titled “Troubleshooting”

• Verify you’re using the Access Key and Secret Access Key - not IAM credentials
• The ODBC driver must be the 64-bit version (32-bit won’t work with Power BI Desktop)
• Confirm the region is us-west-2
• Check that your Tallyfy Analytics subscription is active
• Restart Power BI Desktop after configuring a new ODBC data source

Still stuck? Contact Tallyfy support with the exact error message.

Related articles

Section titled “Related articles”

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Footnotes

Section titled “Footnotes”

1. A serverless SQL query service that analyzes data in S3 using standard SQL syntax ↩

2. Queries data directly from source instead of importing, ideal for large datasets or real-time needs ↩

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