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Your Record to Report - Journal Entry Data Template

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Record to Report - Journal Entry Attributes (20) Record to Report - Journal Entry Activities (12) Extraction Guides (2)

Your Record to Report - Journal Entry Data Template

This template provides a comprehensive guide to collecting the right data for analyzing your Record to Report - Journal Entry process. It outlines essential attributes to gather, key activities to track, and practical guidance on extracting this information from BlackLine. Use this resource to ensure you capture all necessary data for effective process mining.

Recommended attributes to collect
Key activities to track
Extraction guidance

New to event logs? Learn how to create a process mining event log.

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Record to Report - Journal Entry Attributes

These are the recommended data fields to include in your event log for comprehensive analysis of your Record to Report - Journal Entry process.

5 Required 6 Recommended 9 Optional

	Name	Description
	Activity ActivityName	The name of the specific business event that occurred at a point in time in the journal entry process.
Description This attribute describes a single step or task performed within the journal entry lifecycle, such as 'Journal Entry Created', 'Journal Entry Approved', or 'Journal Entry Posted'. Each activity represents a distinct event in the process. Analyzing the sequence of these activities allows for the visualization and understanding of the process flow. It is fundamental for discovering process variations, measuring transition times between steps, and identifying rework loops, such as when a journal entry is rejected and resubmitted. Why it matters It defines the steps in the process, which is essential for visualizing the process map, analyzing flow, and identifying deviations or bottlenecks. Where to get This is typically derived from event logs, status change records, or audit trails within BlackLine. It may require mapping system status codes to user-friendly activity names. Examples Journal Entry CreatedJournal Entry SubmittedJournal Entry ApprovedJournal Entry Posted
	Journal Entry ID JournalEntryId	The unique identifier for a single journal entry, serving as the primary case identifier for tracking its lifecycle.
Description The Journal Entry ID uniquely identifies each set of financial transactions recorded in the general ledger. It acts as the central key linking all related activities, such as creation, review, approval, and posting. In process mining, this ID is essential for correlating all events belonging to a single journal entry case. Analyzing the process flow based on this identifier allows for the reconstruction of the end-to-end journey, from initiation to final reconciliation, enabling detailed analysis of cycle times, bottlenecks, and process variations. Why it matters This is the fundamental case identifier, making it possible to trace the complete lifecycle of a single journal entry and analyze its performance. Where to get This is a primary key in BlackLine's journal entry tables or data exports. Consult BlackLine documentation for the specific field name. Examples JE2024-001234JE2024-001235JE2024-001236
	Start Time EventTime	The precise timestamp indicating when a specific activity or event occurred.
Description The Event Time records the date and time that a business event happened. It is the chronological backbone of the process, establishing the sequence and duration of all activities. This timestamp is critical for all time-based process mining analyses. It is used to calculate cycle times, wait times, and durations between activities. It enables the identification of bottlenecks, the analysis of performance over time, and the assessment of adherence to service level agreements. Why it matters This timestamp is the foundation for all performance metrics, including cycle time and bottleneck analysis, enabling a chronological reconstruction of the process. Where to get This information is captured in the timestamp fields of event logs or transaction records in BlackLine. Examples 2023-10-26T10:00:00Z2023-10-26T11:30:00Z2023-10-27T14:05:00Z
	Last Data Update LastDataUpdate	The timestamp indicating the last time the data was refreshed or extracted from the source system.
Description This attribute marks the date and time of the most recent data pull. It provides a reference point for the freshness of the data being analyzed, ensuring that users are aware of the data's currency. This is a critical piece of metadata for any process mining dashboard or analysis. It informs users about the timeliness of the insights and helps manage expectations about whether the very latest transactions are included in the view. Why it matters It ensures transparency about data freshness, allowing users to understand how current the process analysis is. Where to get This timestamp is generated and added to the dataset by the data extraction tool or ETL process at the time of execution. Examples 2023-11-01T02:00:00Z2023-11-02T02:00:00Z
	Source System SourceSystem	The system of record from which the journal entry data was extracted.
Description This attribute identifies the originating system for the event data. In this context, it will typically be 'BlackLine', but it could also include data from an upstream ERP system if the process starts there. Identifying the source system is important for data governance and for understanding the context of the process, especially in environments where data from multiple systems is combined. It helps in troubleshooting data ingestion issues and verifying data lineage. Why it matters It provides crucial context for data origin, ensuring traceability and helping to manage data integration from different platforms. Where to get This is typically a static value added during the data extraction and transformation process to label the origin of the dataset. Examples BlackLineSAP S/4HANAOracle NetSuite
	Company Code CompanyCode	The unique identifier for the legal entity or company for which the journal entry is being made.
Description The Company Code represents a specific legal entity within a corporate group. It is a fundamental organizational unit in financial accounting, ensuring that transactions are booked to the correct entity. In process mining, Company Code is a powerful dimension for filtering and comparison. It enables analysis of process performance across different entities, helping to identify regional or entity-specific bottlenecks, variations in compliance, and opportunities to standardize processes globally. It directly supports the 'Approval To Posting Delay Analysis' dashboard. Why it matters It enables performance comparison and process standardization efforts across different legal entities or business units. Where to get This is a key field on the journal entry header in BlackLine, often synchronized from the underlying ERP system. Examples 1000US01DE012500
	Journal Entry Amount JournalEntryAmount	The total monetary value of the journal entry, typically the sum of the debits.
Description This attribute represents the financial value of the journal entry. It can be used to understand the financial impact of process inefficiencies, such as delays in posting high-value entries. Analyzing the process based on the entry's value allows for materiality-based analysis. For example, you can prioritize process improvements for high-value entries, investigate if they follow a different approval path, or determine if their cycle times are longer. This adds a crucial business context to the process flow. Why it matters It provides financial context, enabling analysis based on materiality, such as prioritizing high-value entries or identifying value-based process deviations. Where to get This is a calculated or standard field on the journal entry header in BlackLine, representing the total debit or credit amount. Examples 15000.00250.75125000.50500.00
	Journal Entry Status JournalEntryStatus	The current status of the journal entry in its lifecycle, such as 'In Progress', 'Approved', or 'Posted'.
Description This attribute indicates the current state of a journal entry case at the time of data extraction. It provides a snapshot of where the entry is in the overall process. While process mining reconstructs the flow from activities, having the final status is useful for filtering and creating high-level summary dashboards. It helps in quickly identifying all entries that are currently pending approval or are stuck in a particular state, which is valuable for backlog analysis. Why it matters It provides a snapshot of the current state of entries, which is useful for backlog analysis and understanding work-in-progress. Where to get This is a standard status field on the journal entry in BlackLine. Examples In ProgressPending ApprovalApprovedPostedRejected
	Journal Entry Type JournalEntryType	The classification of the journal entry, such as standard, recurring, or reversing.
Description Journal Entry Type categorizes entries based on their nature or purpose. Common types include standard manual entries, automated system-generated entries, recurring entries for month-end accruals, or reversing entries that automatically reverse in the next period. This attribute is critical for segmentation and comparative analysis. It allows you to compare process performance, such as cycle times or rejection rates, across different types of entries. This helps identify if certain types of entries are more problematic or inefficient than others, leading to targeted process improvements. Why it matters It allows for segmented analysis to understand if different types of journal entries follow different paths or have different performance characteristics. Where to get This is a standard field on the journal entry header in BlackLine. Consult BlackLine documentation for the exact field name. Examples StandardRecurringReversingAccrual
	Rejection Reason RejectionReason	The reason provided by a reviewer when a journal entry is rejected.
Description When a journal entry is sent back for corrections, the approver typically provides a reason for the rejection. This attribute captures that reason, which could be a predefined code or free-form text. This is a critical attribute for root cause analysis of rework. By analyzing the most common rejection reasons, a company can identify systemic issues, such as inadequate training, unclear policies, or problems with supporting documentation. This directly supports the 'Journal Entry Rejection & Rework Analysis' dashboard and helps reduce the overall rejection rate. Why it matters It is essential for root cause analysis of process rework, helping to identify why journal entries are rejected and enabling targeted improvements. Where to get This data is typically captured in a comment or reason code field when a user performs the 'Reject' action in BlackLine. Examples Incorrect Account CodeMissing Supporting DocumentationExceeds ThresholdDuplicate Entry
	User UserName	The name or ID of the user who performed the activity.
Description This attribute identifies the individual responsible for executing a specific process step, such as the person who submitted, reviewed, or approved a journal entry. It can be a name or a unique user ID. Analyzing activities by user is essential for understanding workload distribution, individual performance, and identifying training opportunities. It helps answer questions like 'Is work distributed evenly across the team?' or 'Which users have the longest approval times?'. It is also crucial for compliance and audit trail analysis. Why it matters It enables workload analysis, performance comparison across team members, and helps identify resource-related bottlenecks or training needs. Where to get This information is available in BlackLine's audit logs, often stored as 'User ID' or 'Changed By' fields associated with each event. Examples j.doea.smithr.joness.patel
	Account Reconciled AccountReconciled	The general ledger account number that is being reconciled by the journal entry.
Description This attribute identifies the specific account in the chart of accounts that is affected or being reconciled. Journal entries are often part of the account reconciliation process, and this links the entry to that specific account. This attribute directly supports the 'Journal Entry Reconciliation Cycle Time' dashboard. By analyzing the process per account, it is possible to identify if reconciliations for certain accounts consistently take longer, indicating complexity or data issues related to those specific accounts. Why it matters It links the journal entry process to the specific accounts being reconciled, allowing for targeted analysis of reconciliation bottlenecks. Where to get This information is part of the journal entry line item details in BlackLine. It may need to be aggregated to the case level if an entry affects multiple accounts. Examples 101000210500400100550200
	Approval To Posting Lag ApprovalToPostingLag	The calculated duration between the final approval and the actual posting of the journal entry.
Description This metric measures the delay that occurs after a journal entry has been fully approved but before it is posted to the general ledger. It is calculated as the time difference between the 'Journal Entry Approved' activity and the 'Journal Entry Posted' activity. This is a critical KPI for identifying post-approval bottlenecks, which can significantly delay the financial close process. The 'Approval To Posting Delay Analysis' dashboard uses this metric to investigate why approved entries are not being posted promptly, helping to streamline the final steps of the process. Why it matters It specifically measures post-approval efficiency, helping to identify and eliminate delays that impact the speed of the financial close. Where to get This metric is calculated within the process mining tool by finding the duration between the 'Journal Entry Approved' and 'Journal Entry Posted' events for each case. Examples P0DT1H10MP1DT2H0MP0DT0H5M
	Case Cycle Time CaseCycleTime	The total duration of the journal entry case from creation to final posting verification.
Description This attribute is a calculated metric that represents the total elapsed time for a journal entry to move through its entire lifecycle. It is calculated as the difference between the timestamp of the first activity (e.g., 'Journal Entry Created') and the last activity (e.g., 'Posting Verified' or 'Journal Entry Reconciled'). This is a primary KPI for measuring overall process efficiency. It is used in almost every dashboard to provide a high-level view of performance, identify long-running cases, and track efficiency improvements over time. It directly supports the 'Average Journal Entry Cycle Time' KPI. Why it matters This is a fundamental KPI for measuring end-to-end process efficiency and tracking performance against targets. Where to get This metric is calculated within the process mining tool by subtracting the minimum StartTime from the maximum StartTime for each JournalEntryId. Examples P2DT4H30MP0DT8H15MP5DT12H0M
	Currency Currency	The currency code for the amount specified in the journal entry.
Description This attribute specifies the currency of the Journal Entry Amount, such as USD, EUR, or GBP. It is essential for correctly interpreting the financial value of the transactions. For multinational organizations, analyzing by currency can provide useful context. It allows for filtering the process analysis to specific currencies and is a necessary field for any dashboard that aggregates financial amounts. Why it matters It provides necessary context for the financial amounts, ensuring accurate interpretation and enabling currency-specific analysis. Where to get This is a standard field on the journal entry header in BlackLine, usually named 'Document Currency' or similar. Examples USDEURGBPJPY
	Department Department	The business department or cost center associated with the journal entry.
Description This attribute specifies the department, cost center, or functional area that initiated the journal entry or to which the costs are being allocated. It provides an additional layer of organizational context. Analyzing the process by department helps to understand how different parts of the business utilize the journal entry process. It can reveal variations in efficiency, compliance, or rework rates between departments, highlighting areas that may need additional support or process standardization. Why it matters This enables process performance to be compared across different business departments, revealing variations and opportunities for standardization. Where to get This information is usually part of the journal entry line item data or header data in BlackLine, often linked to the GL account. Examples FinanceMarketingSalesOperations
	Document Attached IsDocumentAttached	A flag indicating whether supporting documentation was attached to the journal entry.
Description This boolean attribute indicates if one or more supporting documents, such as invoices or calculations, have been attached to the journal entry. This is often a prerequisite for approval. This flag is crucial for the 'Journal Entry Documentation Compliance' dashboard and the 'Documentation Attachment Rate' KPI. It helps analyze whether missing documentation is a common cause for rejections or delays and helps enforce policies requiring attachments before submission for review. Why it matters It directly measures compliance with documentation policies and helps diagnose a common root cause for approval delays and rejections. Where to get This would likely be a derived field, calculated by checking for the existence of an attachment record associated with the JournalEntryId before the submission activity occurs. Examples truefalse
	Is Automated IsAutomated	A flag indicating if the journal entry was created or posted by an automated process.
Description This boolean attribute distinguishes between journal entries that are created and processed manually by users and those generated automatically by the system, such as recurring entries or system integrations. Analyzing this attribute helps in evaluating the effectiveness of automation. It allows for a direct comparison of cycle times, error rates, and costs between automated and manual processes. This is key to building a business case for further automation and measuring the ROI of existing initiatives. Why it matters It enables a direct comparison between manual and automated processes, which is crucial for measuring the impact and ROI of automation initiatives. Where to get This can be derived from the 'Journal Entry Type' (e.g., 'Recurring') or from the 'User' field if system users (e.g., 'SYSTEM' or 'BATCH') are used for automated postings. Examples truefalse
	Is Rework IsRework	A calculated flag that is true if the journal entry has been rejected at least once.
Description This boolean flag is calculated to identify journal entries that have undergone rework. It is typically set to true if a case contains a 'Journal Entry Rejected' activity followed by a 'Corrected and Resubmitted' activity. This attribute simplifies the analysis of rework by allowing users to easily filter for all cases that have experienced rejection. It is fundamental for calculating the 'Journal Entry Rejection Rate' KPI and for deep dives in the 'Journal Entry Rejection & Rework Analysis' dashboard to understand the cost and time impact of quality issues. Why it matters It easily identifies cases with rework, simplifying the calculation of rejection rates and the analysis of the impact of quality issues. Where to get This flag is calculated in the process mining tool by checking the sequence of activities for each case. Examples truefalse
	Posting Date PostingDate	The date on which the journal entry is officially posted to the General Ledger.
Description The Posting Date is the effective date of the transaction in the financial records. It is a critical date for accounting, as it determines the fiscal period in which the transaction is recognized. While the event timestamp tracks when the posting activity occurred, the Posting Date itself is a key data attribute. It is used in financial analysis to ensure entries are posted in the correct period and can be used to analyze delays between transaction date, approval date, and posting date. Why it matters This date is critical for financial reporting accuracy and helps analyze whether entries are being posted in the correct accounting period. Where to get This is a standard date field on the journal entry header in BlackLine. Examples 2023-10-312023-11-012023-10-30

Required Recommended Optional

Record to Report - Journal Entry Activities

These are the key process steps and milestones to capture in your event log for accurate discovery and analysis of your journal entry process.

6 Recommended 6 Optional

	Activity	Description
	Journal Entry Approved	This activity signifies that the journal entry has passed all required review and approval steps. It is captured when the final authorized user approves the entry, triggering a status change to 'Approved'.
Why it matters This is a major milestone that concludes the approval process. The time from 'Journal Entry Submitted' to this event is the 'Approval Cycle Time', a critical KPI for measuring workflow efficiency. Where to get This event is inferred from a status change to 'Approved' in the journal entry record. The final approver's user ID and the approval timestamp are typically logged in BlackLine's audit history. Capture Inferred from the timestamp when the journal entry status is updated to 'Approved'. Event type inferred
	Journal Entry Created	This activity marks the initiation of a journal entry case. It is captured when a user creates a new journal entry record in BlackLine, which generates a unique Journal Entry ID and logs the creation timestamp and user.
Why it matters This is the primary start event for the process. Analyzing the time from this activity to others reveals the total process cycle time and helps identify delays at the very beginning of the workflow. Where to get This event is explicitly logged in BlackLine's journal entry module. It is captured from the creation timestamp and user details associated with each Journal Entry ID in the main journal entry table or its audit history. Capture Captured from the 'Create Date' timestamp associated with the journal entry record. Event type explicit
	Journal Entry Posted	Represents the point at which the approved journal entry is officially recorded in the general ledger. This is often an explicit action in BlackLine, which then updates the entry's status to 'Posted'.
Why it matters This is a critical milestone, often considered the operational conclusion of the process. The lag time from 'Journal Entry Approved' to this activity is a key indicator of post-approval efficiency. Where to get This is typically inferred from a status change to 'Posted'. The event may also be explicitly logged in a posting history table or audit trail with a specific posting date and timestamp. Capture Inferred from the timestamp when the journal entry status is updated to 'Posted'. The posting date field is also relevant. Event type inferred
	Journal Entry Reconciled	Marks the completion of the lifecycle where the journal entry is included in a finalized and certified account reconciliation. In BlackLine, this corresponds to the certification of the reconciliation that contains this journal.
Why it matters This activity serves as the final endpoint for the end-to-end process. The time from 'Journal Entry Posted' to 'Reconciled' measures the efficiency of the account reconciliation part of the financial close. Where to get This is an inferred event. It is derived by identifying when the account reconciliation containing the specific journal entry has its status changed to 'Certified' within BlackLine's Account Reconciliation module. Capture Inferred by linking the journal entry to its corresponding account reconciliation and capturing the certification date of that reconciliation. Event type inferred
	Journal Entry Rejected	This activity marks the rejection of a journal entry by a reviewer or approver. It is captured when a user takes the 'Reject' action, which updates the entry's status to 'Rejected' and is recorded in the audit log.
Why it matters This is a critical activity for identifying rework, quality issues, and training needs. Analyzing rejection frequency, reasons, and the subsequent rework loop is key to process improvement and reducing cycle times. Where to get This is typically inferred from a status change to 'Rejected' or 'Needs Correction' in the journal entry data. The timestamp and user who performed the rejection are logged in BlackLine's history. Capture Inferred from the timestamp when the status of the journal entry is updated to 'Rejected'. Event type inferred
	Journal Entry Submitted	This activity occurs when the preparer formally submits the journal entry for the review and approval workflow. This is typically captured as a status change, for example from 'In Preparation' to 'Submitted', along with a timestamp.
Why it matters This marks the end of the preparation phase and the beginning of the approval cycle. It is a key milestone for measuring the duration of both the preparation and approval stages. Where to get This is likely inferred from a status change in the journal entry record in BlackLine. The timestamp of the status changing to 'Submitted for Approval' or a similar value marks the event time. Capture Inferred from the timestamp when the journal entry status field is updated to 'Submitted' or 'Pending Approval'. Event type inferred
	Approval Rescinded	This activity represents an approver revoking a previously granted approval, returning the journal entry to a prior state. This is captured by a status change from 'Approved' back to an earlier status like 'In Preparation'.
Why it matters This rare but important activity highlights process exceptions and potential issues that are discovered post-approval but pre-posting. It can indicate data errors or policy changes affecting the entry. Where to get This would be an inferred event, identified by a status change from 'Approved' to a non-posted, editable status. This action would be recorded in BlackLine's detailed audit logs. Capture Identified by a status change from 'Approved' back to a preceding status like 'In Preparation' or 'Submitted'. Event type inferred
	Entry Corrected and Resubmitted	Occurs after a journal entry has been rejected and the preparer has made the necessary corrections and submitted it again. This is captured by a status change from 'Rejected' back to 'Submitted for Approval'.
Why it matters This activity helps quantify the time and effort spent on rework. The duration between 'Journal Entry Rejected' and this event represents the rework time, a key metric for efficiency analysis. Where to get This is inferred by observing a sequence of status changes in the BlackLine data, specifically a transition from a 'Rejected' status back to a 'Submitted' or 'Pending Approval' status for the same Journal Entry ID. Capture Derived from a status change from 'Rejected' to 'Submitted' or 'Pending Approval'. Event type inferred
	Journal Entry Reversal Processed	This activity captures the creation and posting of a new journal entry that reverses a previously posted one. This is typically initiated in response to an error discovered after the original entry was posted.
Why it matters This event is a strong indicator of upstream data quality problems. Tracking the rate of reversals helps measure the 'Post-Posting Adjustment Rate' and identifies areas needing improved accuracy. Where to get This is often an explicit event where a user initiates a 'Reverse' action in BlackLine on a posted journal. The system creates a new reversing entry, often linked to the original Journal Entry ID. Capture Captured when a 'Reverse' action is logged, or when a new journal is created with a reversal flag and a link to the original journal ID. Event type explicit
	Journal Entry Reviewed	Represents a formal review step in a multi-level approval process, completed by a designated reviewer. It is typically captured as a status change or a specific log entry indicating the reviewer's action.
Why it matters For organizations with distinct review and approval steps, this activity helps isolate bottlenecks within the approval chain. It shows how long entries wait for initial review versus final approval. Where to get In BlackLine workflows, this can be an explicit event in the approval history log or inferred from a status change to 'Reviewed' or 'Pending Final Approval'. The user and timestamp are recorded. Capture Inferred from the timestamp when the journal entry status changes to reflect completion of the review stage. Event type inferred
	Posting Verified	Represents the confirmation that a posted journal entry has been successfully received and recorded in the target ERP system. This might be an automated system handshake or a manual confirmation step.
Why it matters This activity provides finality to the posting step, ensuring data integrity between systems. The duration from 'Posted' to 'Verified' can highlight integration issues or delays in system synchronization. Where to get This may be an explicit event from a system integration log or inferred from a status change to a value like 'Posted and Verified'. If manual, it would be a user-driven status update. Capture Inferred from a status update to 'Verified' or from a confirmation flag received from the target ERP system. Event type inferred
	Supporting Documentation Attached	Represents the action of a user attaching one or more supporting documents to the journal entry. This event is typically captured in an audit log or a related attachments table linked to the Journal Entry ID.
Why it matters Tracking this activity is crucial for monitoring compliance and efficiency. It helps analyze if delays in the approval process are caused by missing documentation and supports the 'Documentation Attachment Rate' KPI. Where to get This is usually an explicit event in BlackLine, recorded in an audit trail or attachments log. The data would include the Journal Entry ID, the user who attached the file, and a timestamp. Capture Event is logged in the system's audit trail or a dedicated attachment history table whenever a file is uploaded against a journal entry. Event type explicit

Recommended Optional

Extraction Guides

How to get your data from BlackLine

Steps

Log in to your BlackLine instance with a user account that has the necessary permissions to access financial reporting.
Navigate to the reporting module. This is typically found under a menu like 'Reports' > 'Standard Reports'.
Locate and select the 'Journal Entry & Services Report' or a similarly named comprehensive journal entry report from the list of available standard reports.
Configure the report parameters. Specify the desired 'Date Range' for the extraction, which should correspond to the period you wish to analyze. A period of 3 to 6 months is recommended to start.
Apply necessary filters to narrow down the data. At a minimum, filter by the relevant 'Company Code(s)'. You may also filter by 'Journal Status' to include all relevant statuses from creation to posting.
Select all available columns for the output to ensure the report contains all necessary date, user, and status fields for event log generation. Key fields include creation date, submission date, approval date, posting date, and user roles.
Run the report directly in the BlackLine UI. Once the report has finished generating, use the 'Export' or 'Download' function to save the data as a CSV file. Name it 'blackline_report.csv'.
Set up a Python environment with the pandas library installed. If you do not have it, install it using the command: pip install pandas.
Place the provided Python script in the same directory as your downloaded 'blackline_report.csv' file.
Execute the Python script. The script will read the raw CSV report, transform the data by creating a distinct row for each business activity, and generate a new file named 'event_log.csv' in the required format.
Perform validation checks on the 'event_log.csv' file to ensure data quality and completeness before uploading.
Upload the final 'event_log.csv' file to ProcessMind for analysis.

Configuration

Report Name: Journal Entry & Services Report. The exact name may vary based on your BlackLine configuration.
Date Range: It is recommended to extract 3-6 months of data for an initial analysis. The date filter in the report typically applies to the journal creation date or posting date. Clarify which date field is used for filtering to ensure the correct dataset is extracted.
Key Filters:
- Company Code: Always specify the company codes you want to analyze to limit the data volume and focus the analysis.
- Journal Status: Select all statuses (e.g., In Preparation, Submitted, Approved, Rejected, Posted) to capture the complete end-to-end process.
- Journal Type: Filter for specific journal types (e.g., Standard, Recurring) if needed for the analysis scope.
Prerequisites: The user account running the report must have permissions to view journal entry data and execute standard reports within BlackLine. This typically requires a role such as 'Accountant' or 'Reporting User'.
Performance Considerations: Exporting a large dataset covering a long time period or numerous company codes can be resource-intensive and may take a significant amount of time to generate in the BlackLine UI. Consider running the report in smaller batches if you encounter performance issues or timeouts.

a Sample Query config

import pandas as pd
import numpy as np
import datetime

# --- Configuration ---
# Define the input file name from BlackLine
input_filename = 'blackline_report.csv'
# Define the output file for ProcessMind
output_filename = 'event_log.csv'

# --- Column Mapping ---
# Map the BlackLine report column names to the required event log attribute names.
# Adjust the keys in this dictionary to match the exact column headers in your CSV export.
column_mapping = {
    'Journal ID': 'JournalEntryId',
    'Journal Type': 'JournalEntryType',
    'Company': 'CompanyCode',
    'Amount': 'JournalEntryAmount',
    'Current Status': 'JournalEntryStatus',
    'Rejection Reason': 'RejectionReason',
    'Preparer': 'PreparerUserName',
    'Submitter': 'SubmitterUserName',
    'Reviewer': 'ReviewerUserName',
    'Approver': 'ApproverUserName',
    'Poster': 'PosterUserName',
    'Verifier': 'VerifierUserName',
    'Reverser': 'ReverserUserName',
    'Reconciler': 'ReconcilerUserName',
    'Creation Date': 'CreationTimestamp',
    'Attachment Date': 'AttachmentTimestamp',
    'Submission Date': 'SubmissionTimestamp',
    'Review Date': 'ReviewTimestamp',
    'Rejection Date': 'RejectionTimestamp',
    'Resubmission Date': 'ResubmissionTimestamp',
    'Approval Date': 'ApprovalTimestamp',
    'Rescind Date': 'RescindTimestamp',
    'Posting Date': 'PostingTimestamp',
    'Verification Date': 'VerificationTimestamp',
    'Reversal Date': 'ReversalTimestamp',
    'Reconciliation Date': 'ReconciliationTimestamp'
}

# --- Data Loading ---
try:
    df_raw = pd.read_csv(input_filename)
except FileNotFoundError:
    print(f"Error: The file {input_filename} was not found. Please ensure it's in the same directory as the script.")
    exit()

# Rename columns based on the mapping
df = df_raw.rename(columns=column_mapping)

# Ensure all required timestamp columns exist, fill with NaT if not
for col in column_mapping.values():
    if 'Timestamp' in col and col not in df.columns:
        df[col] = pd.NaT

# Convert date columns to datetime objects, coercing errors to NaT (Not a Time)
date_cols = [col for col in df.columns if 'Timestamp' in col]
for col in date_cols:
    df[col] = pd.to_datetime(df[col], errors='coerce')

all_events = []
last_data_update = datetime.datetime.now()

# --- Activity Extraction ---

# 1. Journal Entry Created
event_created = df[df['CreationTimestamp'].notna()].copy()
event_created['ActivityName'] = 'Journal Entry Created'
event_created['EventTime'] = event_created['CreationTimestamp']
event_created['UserName'] = event_created.get('PreparerUserName')
all_events.append(event_created)

# 2. Supporting Documentation Attached
if 'AttachmentTimestamp' in df.columns:
    event_attached = df[df['AttachmentTimestamp'].notna()].copy()
    event_attached['ActivityName'] = 'Supporting Documentation Attached'
    event_attached['EventTime'] = event_attached['AttachmentTimestamp']
    # Assuming the submitter or preparer attached the document
    event_attached['UserName'] = event_attached.get('SubmitterUserName', event_attached.get('PreparerUserName'))
    all_events.append(event_attached)

# 3. Journal Entry Submitted
event_submitted = df[df['SubmissionTimestamp'].notna()].copy()
event_submitted['ActivityName'] = 'Journal Entry Submitted'
event_submitted['EventTime'] = event_submitted['SubmissionTimestamp']
event_submitted['UserName'] = event_submitted.get('SubmitterUserName')
all_events.append(event_submitted)

# 4. Journal Entry Reviewed
if 'ReviewTimestamp' in df.columns:
    event_reviewed = df[df['ReviewTimestamp'].notna()].copy()
    event_reviewed['ActivityName'] = 'Journal Entry Reviewed'
    event_reviewed['EventTime'] = event_reviewed['ReviewTimestamp']
    event_reviewed['UserName'] = event_reviewed.get('ReviewerUserName')
    all_events.append(event_reviewed)

# 5. Journal Entry Rejected
if 'RejectionTimestamp' in df.columns:
    event_rejected = df[df['RejectionTimestamp'].notna()].copy()
    event_rejected['ActivityName'] = 'Journal Entry Rejected'
    event_rejected['EventTime'] = event_rejected['RejectionTimestamp']
    event_rejected['UserName'] = event_rejected.get('ReviewerUserName', event_rejected.get('ApproverUserName'))
    all_events.append(event_rejected)

# 6. Entry Corrected and Resubmitted
if 'ResubmissionTimestamp' in df.columns:
    event_resubmitted = df[df['ResubmissionTimestamp'].notna()].copy()
    event_resubmitted = event_resubmitted[event_resubmitted['ResubmissionTimestamp'] > event_resubmitted.get('RejectionTimestamp', pd.NaT)]
    event_resubmitted['ActivityName'] = 'Entry Corrected and Resubmitted'
    event_resubmitted['EventTime'] = event_resubmitted['ResubmissionTimestamp']
    event_resubmitted['UserName'] = event_resubmitted.get('SubmitterUserName')
    all_events.append(event_resubmitted)

# 7. Journal Entry Approved
event_approved = df[df['ApprovalTimestamp'].notna()].copy()
event_approved['ActivityName'] = 'Journal Entry Approved'
event_approved['EventTime'] = event_approved['ApprovalTimestamp']
event_approved['UserName'] = event_approved.get('ApproverUserName')
all_events.append(event_approved)

# 8. Approval Rescinded
if 'RescindTimestamp' in df.columns:
    event_rescinded = df[df['RescindTimestamp'].notna()].copy()
    event_rescinded['ActivityName'] = 'Approval Rescinded'
    event_rescinded['EventTime'] = event_rescinded['RescindTimestamp']
    event_rescinded['UserName'] = event_rescinded.get('ApproverUserName')
    all_events.append(event_rescinded)

# 9. Journal Entry Posted
event_posted = df[df['PostingTimestamp'].notna()].copy()
event_posted['ActivityName'] = 'Journal Entry Posted'
event_posted['EventTime'] = event_posted['PostingTimestamp']
event_posted['UserName'] = event_posted.get('PosterUserName')
all_events.append(event_posted)

# 10. Posting Verified
if 'VerificationTimestamp' in df.columns:
    event_verified = df[df['VerificationTimestamp'].notna()].copy()
    event_verified['ActivityName'] = 'Posting Verified'
    event_verified['EventTime'] = event_verified['VerificationTimestamp']
    event_verified['UserName'] = event_verified.get('VerifierUserName')
    all_events.append(event_verified)

# 11. Journal Entry Reversal Processed
if 'ReversalTimestamp' in df.columns:
    event_reversal = df[df['ReversalTimestamp'].notna()].copy()
    event_reversal['ActivityName'] = 'Journal Entry Reversal Processed'
    event_reversal['EventTime'] = event_reversal['ReversalTimestamp']
    event_reversal['UserName'] = event_reversal.get('ReverserUserName')
    all_events.append(event_reversal)

# 12. Journal Entry Reconciled
if 'ReconciliationTimestamp' in df.columns:
    event_reconciled = df[df['ReconciliationTimestamp'].notna()].copy()
    event_reconciled['ActivityName'] = 'Journal Entry Reconciled'
    event_reconciled['EventTime'] = event_reconciled['ReconciliationTimestamp']
    event_reconciled['UserName'] = event_reconciled.get('ReconcilerUserName')
    all_events.append(event_reconciled)


# --- Final Assembly ---
if all_events:
    event_log = pd.concat(all_events, ignore_index=True)

    # Add required constant value attributes
    event_log['SourceSystem'] = 'BlackLine'
    event_log['LastDataUpdate'] = last_data_update

    # Select and order columns for the final output
    final_columns = [
        'JournalEntryId',
        'ActivityName',
        'EventTime',
        'SourceSystem',
        'LastDataUpdate',
        'UserName',
        'JournalEntryType',
        'CompanyCode',
        'JournalEntryAmount',
        'JournalEntryStatus',
        'RejectionReason'
    ]

    # Filter for only the columns that exist in the dataframe to avoid errors
    existing_columns = [col for col in final_columns if col in event_log.columns]
    event_log = event_log[existing_columns]

    # Sort the event log
    event_log = event_log.sort_values(by=['JournalEntryId', 'EventTime'])

    # Save to CSV
    event_log.to_csv(output_filename, index=False, date_format='%Y-%m-%d %H:%M:%S')
    print(f"Successfully created event log: {output_filename}")
else:
    print("No events were generated. Please check your input file and column mappings.")

Steps

Prerequisites and Setup: Ensure you have Python 3.6 or newer installed. Install the necessary libraries using pip: pip install requests pandas. Obtain API credentials (Client ID, Client Secret, Refresh Token) and your organization's BlackLine Base URL from your BlackLine administrator.
Configure Script Variables: Open the Python script and locate the configuration section. Enter your Client ID, Client Secret, Refresh Token, and the Base URL. Define the extraction period by setting the START_DATE and END_DATE variables in 'YYYY-MM-DD' format.
Authentication: The script begins by calling the get_access_token function. This function sends your credentials to the BlackLine authentication endpoint (/api/oauth/token) and retrieves a temporary access token required for all subsequent API requests.
Fetch Initial Journal List: The script makes a paginated request to the /api/v2/journals endpoint, filtering by the specified creation date range. It loops through the pages, collecting the primary details for all journal entries that fall within the scope.
Retrieve Detailed Event History: For each Journal Entry ID collected in the previous step, the script makes two additional API calls. First, it queries the journal's history endpoint (e.g., /api/v2/journals/{id}/history) to get a detailed audit trail of status changes, approvals, and rejections. Second, it calls the attachments endpoint (e.g., /api/v2/journals/{id}/attachments) to identify when supporting documents were added.
Transform Data into an Event Log: The script processes the JSON responses from the API. It iterates through each journal and its associated history and attachment records. It maps the raw data into a structured event log format, creating a distinct row for each of the 12 required activities, such as 'Journal Entry Created', 'Journal Entry Submitted', and 'Journal Entry Approved'.
Map Status Changes to Activities: Key activities are generated by interpreting the data from the history endpoint. For example, a history item indicating a status change to 'Submitted' becomes the 'Journal Entry Submitted' activity. The script contains logic to map different history event types to the standardized activity names required by ProcessMind.
Construct DataFrames: The script uses the pandas library to build DataFrames. It first creates a DataFrame for the 'Created' event from the main journal list. It then creates another DataFrame containing all other activities derived from the history and attachment logs. These are then combined into a single, comprehensive event log.
Add Metadata and Standardize Columns: After combining the data, the script adds required metadata columns, including SourceSystem (hardcoded as 'BlackLine') and LastDataUpdate (set to the script's execution time). It also renames columns to match the required schema, for instance, mapping the API's id field to JournalEntryId.
Export to CSV: Finally, the script exports the completed event log DataFrame to a CSV file named blackline_journal_entry_event_log.csv. This file is formatted and ready for direct upload into ProcessMind.

Configuration

API Credentials: You must have a valid Client ID, Client Secret, and Refresh Token for the BlackLine REST API. These should be stored securely and not hardcoded directly in production scripts.
Base URL: The script requires your organization's specific BlackLine API base URL, for example, https://api.blackline.com.
Date Range: The script is configured with START_DATE and END_DATE variables. For an initial analysis, a date range covering the last 3 to 6 months of activity is recommended to provide sufficient data without being excessively large.
Pagination Limit: The LIMIT variable controls how many records are fetched per API call. The default is typically 100. Adjusting this may have performance implications, so it is best to leave it at the recommended value unless advised otherwise by BlackLine support.
Required Permissions: The API user associated with the credentials must have sufficient permissions to read journal entries, journal history, and attachments across the company codes you intend to analyze.
Python Environment: A functioning Python 3.6+ environment with the requests and pandas libraries installed is a prerequisite for running the script.

a Sample Query python

import requests
import pandas as pd
from datetime import datetime
import time

class=class="hl-string">"hl-comment"># --- Configuration ---
class=class="hl-string">"hl-comment"># Replace with your BlackLine API credentials and base URL
CLIENT_ID = class="hl-string">'[Your Client ID]'
CLIENT_SECRET = class="hl-string">'[Your Client Secret]'
REFRESH_TOKEN = class="hl-string">'[Your Refresh Token]'
BASE_URL = class="hl-string">'[Your BlackLine API Base URL, e.g., https://api.blackline.com]'

class=class="hl-string">"hl-comment"># --- Extraction Parameters ---
START_DATE = class="hl-string">'2023-01-01' class=class="hl-string">"hl-comment"># Format: YYYY-MM-DD
END_DATE = class="hl-string">'2023-12-31'   class=class="hl-string">"hl-comment"># Format: YYYY-MM-DD
LIMIT = 100 class=class="hl-string">"hl-comment"># Records per page

def get_access_token():
    class="hl-string">"""Authenticates with BlackLine to get an access token."""
    auth_url = fclass="hl-string">"{BASE_URL}/api/oauth/token"
    payload = {
        class="hl-string">'grant_type': class="hl-string">'refresh_token',
        class="hl-string">'client_id': CLIENT_ID,
        class="hl-string">'client_secret': CLIENT_SECRET,
        class="hl-string">'refresh_token': REFRESH_TOKEN
    }
    response = requests.post(auth_url, data=payload)
    response.raise_for_status() class=class="hl-string">"hl-comment"># Raise an exception for bad status codes
    return response.json()[class="hl-string">'access_token']

def main():
    class="hl-string">"""Main function to extract, transform, and save journal entry event data."""
    print(class="hl-string">"Starting BlackLine Journal Entry extraction...")
    
    try:
        access_token = get_access_token()
        headers = {class="hl-string">'Authorization': fclass="hl-string">'Bearer {access_token}'}
    except requests.exceptions.RequestException as e:
        print(fclass="hl-string">"Authentication failed: {e}")
        return

    events = []
    last_data_update = datetime.utcnow().isoformat() + class="hl-string">'Z'

    class=class="hl-string">"hl-comment"># 1. Fetch all journals within the date range
    print(fclass="hl-string">"Fetching journals created between {START_DATE} and {END_DATE}...")
    journals = []
    offset = 0
    while True:
        journals_url = fclass="hl-string">"{BASE_URL}/api/v2/journals"
        params = {
            class="hl-string">'createdStartDate': START_DATE,
            class="hl-string">'createdEndDate': END_DATE,
            class="hl-string">'limit': LIMIT,
            class="hl-string">'offset': offset
        }
        try:
            response = requests.get(journals_url, headers=headers, params=params)
            response.raise_for_status()
            data = response.json()
            journals.extend(data.get(class="hl-string">'data', []))
            if class="hl-string">'nextLink' not in data or not data.get(class="hl-string">'data'):
                break
            offset += LIMIT
        except requests.exceptions.RequestException as e:
            print(fclass="hl-string">"Error fetching journals: {e}")
            break

    print(fclass="hl-string">"Found {len(journals)} journals. Now fetching detailed events for each...")

    class=class="hl-string">"hl-comment"># 2. For each journal, fetch history and attachments
    for journal in journals:
        journal_id = journal.get(class="hl-string">'id')
        if not journal_id:
            continue

        class=class="hl-string">"hl-comment"># --- Activity: Journal Entry Created ---
        events.append({
            class="hl-string">'JournalEntryId': journal_id,
            class="hl-string">'ActivityName': class="hl-string">'Journal Entry Created',
            class="hl-string">'EventTime': journal.get(class="hl-string">'createdDate'),
            class="hl-string">'UserName': journal.get(class="hl-string">'createdBy', {}).get(class="hl-string">'username'),
            class="hl-string">'JournalEntryType': journal.get(class="hl-string">'type'),
            class="hl-string">'CompanyCode': journal.get(class="hl-string">'companyCode'),
            class="hl-string">'JournalEntryAmount': journal.get(class="hl-string">'totalDebit'),
            class="hl-string">'JournalEntryStatus': journal.get(class="hl-string">'status'),
            class="hl-string">'RejectionReason': None
        })

        class=class="hl-string">"hl-comment"># Fetch history for other activities
        try:
            history_url = fclass="hl-string">"{BASE_URL}/api/v2/journals/{journal_id}/history"
            history_response = requests.get(history_url, headers=headers)
            history_response.raise_for_status()
            history_items = history_response.json().get(class="hl-string">'data', [])
            
            for item in history_items:
                activity_name = None
                rejection_reason = None
                
                class=class="hl-string">"hl-comment"># This mapping requires knowledge of your specific BlackLine history event descriptions
                class=class="hl-string">"hl-comment"># The following are common examples. Adjust them based on your BlackLine configuration.
                event_description = item.get(class="hl-string">'description', class="hl-string">'').lower()
                if class="hl-string">'submitted' in event_description:
                    activity_name = class="hl-string">'Journal Entry Submitted'
                elif class="hl-string">'reviewed' in event_description:
                    activity_name = class="hl-string">'Journal Entry Reviewed'
                elif class="hl-string">'rejected' in event_description:
                    activity_name = class="hl-string">'Journal Entry Rejected'
                    rejection_reason = item.get(class="hl-string">'comment') class=class="hl-string">"hl-comment"># Assuming comment contains the reason
                elif class="hl-string">'resubmitted' in event_description:
                    activity_name = class="hl-string">'Entry Corrected and Resubmitted'
                elif class="hl-string">'approved' in event_description and class="hl-string">'final' in event_description: class=class="hl-string">"hl-comment"># Example for final approval
                    activity_name = class="hl-string">'Journal Entry Approved'
                elif class="hl-string">'approval rescinded' in event_description:
                    activity_name = class="hl-string">'Approval Rescinded'
                elif class="hl-string">'posted' in event_description:
                    activity_name = class="hl-string">'Journal Entry Posted'
                elif class="hl-string">'posting verified' in event_description:
                     activity_name = class="hl-string">'Posting Verified'
                
                if activity_name:
                    events.append({
                        class="hl-string">'JournalEntryId': journal_id,
                        class="hl-string">'ActivityName': activity_name,
                        class="hl-string">'EventTime': item.get(class="hl-string">'eventDate'),
                        class="hl-string">'UserName': item.get(class="hl-string">'user', {}).get(class="hl-string">'username'),
                        class="hl-string">'JournalEntryType': journal.get(class="hl-string">'type'),
                        class="hl-string">'CompanyCode': journal.get(class="hl-string">'companyCode'),
                        class="hl-string">'JournalEntryAmount': journal.get(class="hl-string">'totalDebit'),
                        class="hl-string">'JournalEntryStatus': journal.get(class="hl-string">'status'),
                        class="hl-string">'RejectionReason': rejection_reason
                    })
        except requests.exceptions.RequestException as e:
            print(fclass="hl-string">"Could not fetch history for Journal ID {journal_id}: {e}")

        class=class="hl-string">"hl-comment"># Fetch attachments
        try:
            attachments_url = fclass="hl-string">"{BASE_URL}/api/v2/journals/{journal_id}/attachments"
            attachments_response = requests.get(attachments_url, headers=headers)
            attachments_response.raise_for_status()
            attachments = attachments_response.json().get(class="hl-string">'data', [])
            for attachment in attachments:
                events.append({
                    class="hl-string">'JournalEntryId': journal_id,
                    class="hl-string">'ActivityName': class="hl-string">'Supporting Documentation Attached',
                    class="hl-string">'EventTime': attachment.get(class="hl-string">'uploadedDate'),
                    class="hl-string">'UserName': attachment.get(class="hl-string">'uploadedBy', {}).get(class="hl-string">'username'),
                    class="hl-string">'JournalEntryType': journal.get(class="hl-string">'type'),
                    class="hl-string">'CompanyCode': journal.get(class="hl-string">'companyCode'),
                    class="hl-string">'JournalEntryAmount': journal.get(class="hl-string">'totalDebit'),
                    class="hl-string">'JournalEntryStatus': journal.get(class="hl-string">'status'),
                    class="hl-string">'RejectionReason': None
                })
        except requests.exceptions.RequestException as e:
            print(fclass="hl-string">"Could not fetch attachments for Journal ID {journal_id}: {e}")

        class=class="hl-string">"hl-comment"># --- Activities based on journal fields (may need customization) ---
        if journal.get(class="hl-string">'isReversed'):
            events.append({
                class="hl-string">'JournalEntryId': journal_id,
                class="hl-string">'ActivityName': class="hl-string">'Journal Entry Reversal Processed',
                class="hl-string">'EventTime': journal.get(class="hl-string">'[reversalDate]'), class=class="hl-string">"hl-comment"># Placeholder for reversal date field
                class="hl-string">'UserName': journal.get(class="hl-string">'[reversalUser]'), class=class="hl-string">"hl-comment"># Placeholder for user field
                class="hl-string">'JournalEntryType': journal.get(class="hl-string">'type'),
                class="hl-string">'CompanyCode': journal.get(class="hl-string">'companyCode'),
                class="hl-string">'JournalEntryAmount': journal.get(class="hl-string">'totalDebit'),
                class="hl-string">'JournalEntryStatus': class="hl-string">'Reversed',
                class="hl-string">'RejectionReason': None
            })

        if journal.get(class="hl-string">'reconciliationId'): class=class="hl-string">"hl-comment"># Assuming this indicates reconciliation
            events.append({
                class="hl-string">'JournalEntryId': journal_id,
                class="hl-string">'ActivityName': class="hl-string">'Journal Entry Reconciled',
                class="hl-string">'EventTime': journal.get(class="hl-string">'[reconciliationCertifiedDate]'), class=class="hl-string">"hl-comment"># Placeholder for certified date
                class="hl-string">'UserName': journal.get(class="hl-string">'[reconciliationCertifier]'), class=class="hl-string">"hl-comment"># Placeholder for certifier user
                class="hl-string">'JournalEntryType': journal.get(class="hl-string">'type'),
                class="hl-string">'CompanyCode': journal.get(class="hl-string">'companyCode'),
                class="hl-string">'JournalEntryAmount': journal.get(class="hl-string">'totalDebit'),
                class="hl-string">'JournalEntryStatus': journal.get(class="hl-string">'status'),
                class="hl-string">'RejectionReason': None
            })
        class=class="hl-string">"hl-comment"># Introduce a small delay to respect API rate limits
        time.sleep(0.1)

    if not events:
        print(class="hl-string">"No events were generated. Please check your date range and API access.")
        return

    class=class="hl-string">"hl-comment"># 3. Create DataFrame and save to CSV
    print(class="hl-string">"Transforming data and saving to CSV...")
    df = pd.DataFrame(events)

    class=class="hl-string">"hl-comment"># Add metadata columns
    df[class="hl-string">'SourceSystem'] = class="hl-string">'BlackLine'
    df[class="hl-string">'LastDataUpdate'] = last_data_update
    
    class=class="hl-string">"hl-comment"># Ensure required columns are present
    final_columns = [
        class="hl-string">'JournalEntryId', class="hl-string">'ActivityName', class="hl-string">'EventTime', class="hl-string">'SourceSystem', class="hl-string">'LastDataUpdate',
        class="hl-string">'UserName', class="hl-string">'JournalEntryType', class="hl-string">'CompanyCode', class="hl-string">'JournalEntryAmount', class="hl-string">'JournalEntryStatus', class="hl-string">'RejectionReason'
    ]
    for col in final_columns:
        if col not in df.columns:
            df[col] = None
            
    df = df[final_columns] class=class="hl-string">"hl-comment"># Order columns

    output_filename = class="hl-string">'blackline_journal_entry_event_log.csv'
    df.to_csv(output_filename, index=False)
    print(fclass="hl-string">"Extraction complete. Event log saved to {output_filename}")

if __name__ == class="hl-string">"__main__":
    main()

Your Record to Report - Journal Entry Data Template

Your Record to Report - Journal Entry Data Template

Record to Report - Journal Entry Attributes

Record to Report - Journal Entry Activities

Extraction Guides

BlackLine Journal Entry & Services Report Export

Steps

Configuration

a Sample Query config

Python Script Extraction via BlackLine REST API

Steps

Configuration

a Sample Query python

Ready to Get Started?

Optimize Record to Report Journal Entry Efficiency Today

Your Record to Report - Journal Entry Data Template

Your Record to Report - Journal Entry Data Template

Record to Report - Journal Entry Attributes

Record to Report - Journal Entry Activities

Extraction Guides

BlackLine Journal Entry & Services Report Export

Steps

Configuration

a Sample Query config

Python Script Extraction via BlackLine REST API

Steps

Configuration

a Sample Query python

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Ready to Get Started?

Optimize Record to Report Journal Entry Efficiency Today