An enterprise workspace that brings data management, quality analysis, and AI-assisted exploration into one clear product experience.
DisciplineEnterprise AI / Data Platform
IDQSample workspace
IDQ Assistant
Explore your data in one workspace.
Ask questions, investigate quality, and move into the right analysis without losing dataset context.
Source rowsSample
Observed columnsReady
Workspace datasetsDemo
Suggested actions
+
Ask anything about your data...
IDQ
01 / Project overview
IDQ gives data teams one place to understand a dataset, investigate quality issues, and move into deeper analysis. Chat, dataset management, analysis tools, and reusable workflows stay connected so users can work without losing context.
AI assistant
Data profiling
Quality rules
Anomaly detection
Time-series analysis
Forecasting
Text analysis
Product architecture
02 / Context and problem
Data quality work often spans separate tools for profiling, rules, anomalies, forecasting, and reporting. That fragmentation slows investigation and makes findings harder to act on. IDQ turns those capabilities into a guided workspace built around the user's active dataset.
03 / Contribution
What I built and shaped
Shaped the product experience across the assistant, data management, and analysis workflows.
Built guided journeys for profiling, rules, anomaly detection, forecasting, and text analysis.
Connected advanced analysis to clear explanations, next actions, and reusable quality workflows.
Worked across product architecture, delivery, and reliability to keep the experience coherent from input to result.
04 / Constraints
What shaped the work
Support varied datasets without overwhelming the user
Keep quality findings understandable and actionable
Give longer analyses clear progress and result states
Make advanced tools approachable from one workspace
05 / Process and key decisions
Make the system understandable before making it impressive.
01
Center the workspace on the data
The active dataset stays visible as users move between chat, management, and analysis, preserving the context behind every question and result.
02
Use AI as a guide
Suggested questions and quality actions help users begin quickly while keeping analysis choices and findings easy to understand.
03
Bring specialist tools together
Anomaly detection, rules, time-series analysis, forecasting, and text analysis share a consistent product language instead of feeling like separate utilities.
04
Design for the next action
Results are framed to help users investigate, compare, and continue rather than ending at a chart or technical output.
06 / Product view
One workspace, several ways into the data.
Use the tabs to explore a public-facing product view based on IDQ's assistant, data, analysis, and marketplace navigation.
IDQSample workspace
IDQ Assistant
Explore your data in one workspace.
Ask questions, investigate quality, and move into the right analysis without losing dataset context.
Source rowsSample
Observed columnsReady
Workspace datasetsDemo
Suggested actions
+
Ask anything about your data...
IDQ
07 / Final solution
The product combines an AI assistant, dataset management, analysis tools, and reusable quality workflows in a unified interface. Users can upload or select data, ask guided questions, run specialist analyses, and keep the resulting context together in one workspace.
08 / What the work reinforced
Principles carried into the next build.
01A useful AI assistant needs strong product context around it.
02Advanced analysis becomes more approachable when the first action is obvious.
03Consistency across tools helps users trust and reuse what they learn.