Feature Requests

We have updated the date format across the Omnisient platform to 01 Jan 2024 to enhance readability, improve localization, and ensure visual clarity. This format is more user-friendly, aligning with international standards, reducing confusion across regions, and providing a consistent user experience. The use of an abbreviated month name also enhances the visual hierarchy, making dates easier to interpret at a glance.

Financial Inclusion is a project template that incorporates a large retailer's data for use by a client to match customer data based on both the customer's identity ("Customer") and a specific date ("Date"). You need to have an active subscription to this - please speak to the sales team ( sales@omnisient.com )

The Own Data Preparation project feature in the Omnisient Platform empowers users to manage their data more effectively. https://help.omnisient.com/en/articles/8034106-mastering-the-own-data-preparation-project-feature-in-omnisient-platform

You will now receive an email if you're dataset has a high rate of errors and has been quarantined.

Python, a widely used programming language for Data Scientists, offers built-in mathematical libraries and functions, simplifying complex calculations and data analysis tasks. With Python integrated into Omnisient, data scientists can seamlessly work on anonymized data without the hassle of moving it around. Additionally, it facilitates the automation of tasks and processes, streamlining workflow for enhanced productivity and efficiency.

At Omnisient, we prioritize meticulous tracking and auditing of user actions within our platform. We recognize the significance of a comprehensive Update Activity log in ensuring transparency and security. Currently, there are gaps in recording crucial actions like adding users, approving projects, approving selections, and sharing datasets. These gaps can hinder accurate monitoring and analysis of user activity. To address this, we're dedicated to enhancing the Update Activity log. This upgrade will include all essential entries for the specified areas, providing a robust tool for monitoring and auditing user actions. Enhancements at a Glance: Enhanced Recording: Actions like adding users, project approvals, selection approvals, and dataset sharing will be meticulously recorded in the Update Activity log. Accessible History: Users will have access to a comprehensive history of these actions, facilitating detailed tracking and auditing. Our commitment to improving the Update Activity log reflects our dedication to a secure and transparent user experience. This enhancement ensures that all relevant entries for critical areas are documented, enabling accurate tracking and auditing. We appreciate your trust in our platform and look forward to delivering this valuable upgrade.

In our ongoing efforts to enhance your platform experience, we're introducing a valuable improvement to the Overlap feature. Currently, successful matching in Overlap hinges on having at least one common Personally Identifiable Information (PII) column between client datasets. The absence of such columns can lead to frustrating results—zero matches. To address this, we've implemented a proactive warning system. When initiating an overlap booking, our system now checks for common PII columns between datasets. If none are found, a warning message is promptly displayed. This early alert saves you time and frustration. Here's how it works: Booking an Overlap: When you begin an overlap booking, our system conducts a quick check. Warning Message: If no common PII columns are detected, a warning message appears on the platform. It notifies you that the overlap might yield no results due to the absence of common PII columns. Review and Adjust: Upon receiving the warning, you can review your dataset selections and make necessary adjustments to ensure the presence of at least one common PII column. This enhancement streamlines your Overlap experience, enabling you to make informed decisions and optimize outcomes. We value your time and aim to provide a more user-friendly experience. Thank you for choosing our platform!

experience on our platform. With this update, we've made your journey more efficient and user-friendly. Here's what you can look forward to: Streamlined Access: Our redesigned left-hand side menu provides easy access to all your screens and projects, conveniently located in one place. Search Functionality: We've added a powerful "search" feature that lets you quickly locate specific pages, projects, or articles. Just enter your query, and our system will do the rest, ensuring you can navigate through our platform effortlessly. Our commitment is to enhance your user experience and provide you with the tools you need to navigate our platform seamlessly. Give our new navigation system a try, and enjoy the convenience of easy access and efficient search capabilities.

The booking of an ACE Report is a request to the system to compare your dataset to the ACE “universe” of data which contains credit bureau data, deeds information, CIPC & Home Affairs information, and other marketing data, and to prepare a report with various pages of analyses and insights of your dataset(s) against this universe. Please visit help.omnisient.com for more details on this project.

Prerequisites You need to have uploaded a dataset, and have created a collaboration between you and another partner. Navigate to the menu and locate the "Projects" section. This is where you'll find helpful templates and analysis tools. Look for the "Overlap Template" and select it. This template is specifically designed to assist you in selecting datasets for overlap analysis. Once you've accessed the template, you'll be guided through the steps of the Overlap Project. Follow these steps carefully to ensure a successful overlap analysis. In the template, you'll find a list of your uploaded datasets. Take your time to review the options and select the dataset you wish to include in the overlap analysis. You now need to select your Customer Identifier. Next, you'll need to indicate the shared dataset that you want to compare with the selected dataset. This comparison will help identify any overlapping data points. After confirming your selections, the system will automatically schedule the overlap between the chosen datasets. This ensures that the analysis is performed according to your preferences and within the specified timeframe.