2018-2019

Web Application, UX, Product Design, Feature Design

Connection.io

Data intelligence tool designed for analysts investigating crimes or monitoring potential criminal activity. This web application supports data-driven insights to help analysts detect, investigate, and respond to threats effectively.

Bottle
Bottle
Bottle

Previous Version Problems

The initial version of Connection.io presented several challenges that hindered its usability and effectiveness for analysts: Light-colored Interface: The work environment is typically dim, making light interfaces uncomfortable for prolonged use and leading to eye strain. Complex and Overloaded Dashboard: The dashboard was cluttered with excessive information, complicating the analyst’s task of spotting potential threats within a strict KPI of 3 minutes. Simplifying the interface was critical for faster response times. Missing Essential Features: Key functionalities, such as note-taking and merged result views, were absent, limiting analysts’ ability to organize information and track insights effectively. Complicated Query Building: The query-building process was cumbersome, with multiple steps and a non-intuitive flow, making it challenging for users to build complex queries quickly.

Solution

The redesign of Connection.io focused on transforming it into a user-friendly, efficient, and visually ergonomic tool tailored to the analysts’ needs: Dark Mode Interface: To reduce eye strain and improve comfort in low-light work environments, the platform was redesigned with a dark theme. This adjustment provides a more suitable visual experience for users who work long hours in dim conditions. Streamlined Dashboard: The dashboard was restructured to display only the most relevant data and metrics, reducing clutter and enhancing focus. By emphasizing high-priority information, the redesigned dashboard enables analysts to quickly assess threats and meet their KPI. Enhanced Features: New functionalities were introduced, including note-taking and the ability to view merged result sets. These features allow analysts to document findings directly within the platform and consolidate data for easier access and analysis. Simplified Query Building: The query-building interface was redesigned to be more intuitive, reducing the number of steps and improving the overall flow. Analysts can now build complex queries more efficiently, saving time and reducing frustration. Overall, these changes have created a focused, analyst-centered platform that improves productivity, decision-making speed, and user comfort.

Pivoted to Cement Delivery

Query Builder

The Query Builder was redesigned to streamline the process of creating and refining complex data queries. The interface features an intuitive map-based visualization, allowing analysts to easily define parameters such as date range, time frame, and geographical boundaries. This new design reduces the learning curve, enabling users to quickly construct powerful, targeted queries without unnecessary complexity.

Result Set - Dashbord

The Result Set Dashboard consolidates critical data insights into a single, user-friendly interface, allowing analysts to quickly assess and interpret key metrics. This dashboard includes a range of visualizations, such as top services, active email accounts, call activity trends, and message types, providing a comprehensive overview of communication patterns.

Merge Result Set

The Merge Result Set feature introduces a Venn diagram interface that allows analysts to visually combine and compare different data sets. By selecting overlapping areas, users can isolate specific subsets of data, revealing connections and patterns that would otherwise be difficult to detect. This tool simplifies complex data merging, making it easy to generate new insights.

New Cut Result Set

The New Cut Result Set functionality provides a structured view for slicing and categorizing data according to custom parameters. With this feature, users can filter, tag, and organize data points into defined groups, facilitating more granular analysis. This streamlined approach to data categorization ensures that analysts can quickly isolate relevant information for further investigation.