Realtime Synced Datasets Documentation

This documentation explains how to create and use real-time synced datasets in Abacus.AI.

Real-time datasets allow you to sync data from external sources like Microsoft Teams, Confluence, Jira, and SharePoint.

Table of Contents

• Overview
• Realtime Synced Datasets
  • How Realtime Synced Datasets Work
  • Using Realtime Synced Datasets with Jira
  • Using Realtime Synced Datasets with Teams
• Supported Connectors
• Creating a Realtime Synced Dataset
• Connector-Specific Details
  • Teams Transcripts
  • Confluence
  • Jira
  • SharePoint
• Troubleshooting and FAQ

Overview

Realtime synced datasets allow you to connect to external data sources and sync data periodically. These datasets are ideal for use cases requiring up-to-date information, such as chatbots, predictive analytics, and real-time monitoring.

Realtime Synced Datasets

Realtime synced datasets are a feature in Abacus.AI that allows for continuous, automatic updates of data from external sources into the platform. This ensures that the data in your Abacus.AI projects is always up-to-date with the latest information from your connected systems.

Note: You must use Jira Admin credentials to set up a realtime synced dataset for Jira.

How Realtime Synced Datasets Work

1. Connection to External Sources: Abacus.AI can establish connections with various external data sources, including project management abilities like Jira and communication platforms like Microsoft Teams.
2. Automatic Synchronization: Once connected, the platform continuously monitors these sources for changes and updates. When new data is available, it's automatically synced to your Abacus.AI project.
3. Data Freshness: This ensures that your models and analyses are always working with the most current data, which is crucial for accurate predictions and insights.
4. Reduced Manual Work: It eliminates the need for manual data uploads or periodic batch updates, saving time and reducing the risk of human error.

Using Realtime Synced Datasets with Jira

1. Issue Tracking: Abacus.AI can sync data about Jira issues in real-time. This includes information like issue status, assignees, priority, and custom fields.
2. Project Management: Data about Jira projects, sprints, and epics can be continuously updated in Abacus.AI.
3. Use Cases: This data can be used for various predictive modeling tasks such as: - Predicting issue resolution times - Forecasting project completion dates - Identifying potential bottlenecks in workflows - Analyzing team performance and capacity

Using Realtime Synced Datasets with Teams

1. Communication Data: Abacus.AI can sync data from Teams conversations, channel activities, and user interactions.
2. Collaboration Metrics: Information about team structures, meeting frequencies, and document sharing can be continuously updated.
3. Use Cases: This data can be utilized for: - Analyzing communication patterns and team dynamics - Predicting employee engagement levels - Identifying successful collaboration practices - Optimizing team structures and workflows

Supported Connectors

The following connectors support real-time synced datasets:

• Microsoft Teams Transcripts: Sync meeting transcripts, channel posts, and chat messages.
• Confluence: Sync all data from Confluence spaces.
• Jira: Sync all Jira issues across the site/projects (up to 120 days).
• SharePoint: Sync specific files and folders selected through the application browser.

Creating a Realtime Synced Dataset

To create a real-time synced dataset:

1. Go to the Datasets tab in your project and click Create Dataset.
2. Select Realtime Synced Dataset as the Type of Data.

3. Choose the appropriate connector and configure the data import options.

4. Once the dataset is created, use it to train models or build chatbots.

Connector-Specific Details

Teams Transcripts

• Data Options: Choose from meeting transcripts, channel posts, or chat messages.
• Permissions: Data access is scoped to the user’s permissions in Microsoft Teams.

Confluence

• Data Scope: Pull all data from Confluence spaces without scoping.
• Metadata: Includes page URLs as metadata in the dataset.

Jira

• Data Scope: Pull all Jira issues across the site/projects (up to 120 days).
• Additional Options: Include comments, watchers, and custom fields.

SharePoint

• Data Selection: Use the application browser to select specific files and folders for syncing.
• Authentication: Only supports the app-based authentication method for real-time datasets.

Troubleshooting and FAQ

Common Issues

• Why can’t I see my connector in the list?

  • Ensure that the connector has been verified and supports real-time datasets.

• What happens if permissions change in the source system?

  • Abacus.AI automatically respects updated permissions, ensuring secure data access.

• Where are my old Jira tickets?

  • To avoid issues with large ingestion, the Jira connector only pulls data from the past 120 days.

For further assistance, contact Abacus.AI support.