Data Quality and UX in Finance

Understanding DQ

DQ is imperative as it organizes accuracy, completeness, and timeliness of data for operations of the business, customer insights, and regulatory compliance. From a business lens it helps inform leaders on critical decisions, mitigate risks, and comply with regulatory requirements.

Accuracy – free from errors
Completeness – fields filled
Consistency – uniformed across systems
Timeliness – up to date
Integrity – reliable and traceable

If DQ is not reliable, it can lead to financial loss, regulatory penalties, danger of risking reputation, and inefficiencies in the operational organization.

Regulatory compliance and reporting
- Misreporting financials, penalties and legal consequences
Risk management and fraud prevention
- Incorrect credit risk assessments and poor loans handouts/ high-risk loans
- Real-time fraud detection systems to flag unusual transactions and suspicious activity
Customer Experience and Personalization
- Clean data enables banks to provide personalized offers, recommendations, and financial products
- Inconsistent or incorrect customer data results in poor service and frustration
Operational efficiencies and cost reduction
- Poor data quality leads to redundant process, manual correctness, and inefficiencies
- Clearing data should be timely but could be avoided with proper data governance
Investment and financial decision making
- Forecasting, investment strategies, loan approvals
- Misinformed investment decisions, affecting profitability

Examples:

Duplicate customer records could affect credit history across accounts
Incorrect account balances could lead to inflated numbers if two of the same transactions are listed
Inconsistent data across department could be problematic because teams will be utilizing two sources of information and their ledgers will be incorrect
Delayed transactions due to incomplete data could lead to disruptions in processing a customer’s international transaction

Maintaining DQ is assured by integrating policies to ensure accuracy and consistency, data cleansing and standardization through auditing, automated data validation with ML/AI to detect abnormalities, and have a centralized data management systems for single source of truth.

DQ monitors, cleans, and analyzes data for accuracy, consistency, and compliance regulations. Risk management, data protection, and financial stability are the essence of the following regulatory frameworks:

Basel Accord = financial data management to assess risk
General Data Production Regulation = customer data production for security and accuracy
Basel Committee on Banking Supervision 239 = risk-related data accuracy and governance

Designing

In order to design an accurate framework, UX designers need to follow an agile approach with some formal structure. It is imperative to remember the main goals of being able to display accuracy, completeness, and reliability.

1. Understand the Framework of DQ in UX and Empathize with Users

Users rely on correct data for making decisions and need to know that they are being trusted by a reliable source to pull a personalized set of data easily. If there is poor data control then users will have a bad experience using the platform and their trust levels will dim. Designers want to focus data directly on user behavior, feedback, and interaction with the interface.

Is the data correct to promote Accurate Decision-Making?
Reliability of Features
User Trust
Personalization

2. Define the Scope and Goals of DQ

Before designing the framework, designers need to define the goals of data quality checking to get a better sense of why users need an organized system.

Accurate
Consistent and uniformed
Sufficient amount of detail (is all necessary data collected and consistent across the platform?)
Current and frequently updated and refreshed + timely or are there delays which can contribute to errors in data manipulation and reconciliation?

3. Collaborate with Stakeholders and Create a Brief

Identify who the key stakeholders (data scientists, business leads, product managers) are and work in union to understand why they need DQ check to place in a brief. There can be data owners who are responsible for looking at the accuracy and quality of data, data governance teams who are creating rules and guidelines for data usage, and compliance teams who are making sure that the data collected is in compliance with data privacy regulations.

The brief should include a summary (background description of the product, the business objective, a problem statement) , a scope (what is in scope, out of scope, and constraints), and details (definition of success, risks, opportunities, deliverables, primary personas, secondary personas, and dependencies). Overall, we want to try to understand the process of interpreting data and decision making.

How data is used in the product.
What specific data quality challenges exist.
What user-centric goals they have for data quality.

Designers also want to start to think of ways on how to gather data to help their research process through clear visualizations to understand reporting, provide contextual explanations, user segmentation, and actionable insights. Some ways to achieve this are by the following:

User testing with current use of platform
Analytic tooling: google, hotjar, etc.
Surveys
Heatmaps and clickmaps

4. Map Out the Data Flow (ETL)

Create a visual map of how data flows through your system. This helps in understanding where data comes from, how it’s processed, and how it reaches the user interface:

Data Sources: Identify where data is coming from (e.g., external APIs, databases, sensors).
Processing: How is data cleaned, transformed, or manipulated?
Delivery: How is data presented to users (e.g., dashboards, reports, graphs)?

5. Define Key Metrics and KPIs

Metrics for tracking data quality should align with UX objectives.

Error Rates: Frequency of incorrect or invalid data.
Data Completeness: Proportion of missing values in datasets.
Latency: Time it takes for the data to become available to users.
Data Consistency: How aligned the data is across systems or databases.
User Feedback: Monitor how users perceive data quality through surveys or feedback loops.

6. Design User-Centric Data Quality Features

Focus on how the user will interact with and be affected by data quality. A good UX design can help mitigate the effects of poor data quality:

Error Handling & Messaging: Provide clear, helpful error messages when data is missing or inaccurate.
Data Validation Feedback: Allow users to see whether their input is correct [validation] and guide them with meaningful feedback.
Progress Indicators: Show real-time data updates or loading indicators so users know the system is working to provide fresh data.
Fallback Mechanisms: Have fallback data in case the preferred data source is unavailable, so users don’t experience a broken UX.
Audition and Validation: to ensure accuracies, identify anomalies and flag incorrect and inconsistent data

7. Set up Data Quality Monitoring Tools

Automated Testing: Use tools to automate data validation, ensuring data accuracy and integrity before it reaches the user interface.
Data Quality Dashboards: Build an internal dashboard to monitor data quality metrics in real-time, providing alerts when data quality falls below acceptable thresholds.
User-Centric Metrics: Incorporate UX feedback into the monitoring system, so if users report issues related to data quality, it can be immediately flagged.

8. Establish a Feedback Loop

User feedback is critical in understanding the actual impact of data quality on the user experience:

Surveys/Usability Tests: Regularly assess how data issues affect the user experience through surveys or usability testing sessions.
In-App Feedback: Implement mechanisms within the product to let users report data-related issues easily.
Analyze User Behavior: Look at how users interact with data features and see if there are common actions that may indicate an underlying issue.

9. Iterate and Improve

A data quality framework should be flexible and allow for continuous improvements:

Address Issues: Once you identify data quality issues affecting UX, prioritize and address them systematically.
Enhance Data Cleaning Processes: Work with the data team to improve the quality of the data at the source or during transformation processes.
Refine UX Based on Data Quality: Ensure the UX adapts to data quality challenges and adjusts accordingly, such as loading times or fallback states.

10. Consider Data Security & Privacy

Ensure that the data quality framework also includes privacy and security considerations. If your UX involves sensitive data, users should be reassured about data protection:

Transparency: Clearly communicate how data is being used.
User Control: Allow users to manage their data, providing mechanisms to view, edit, or delete their information.
Compliance: Ensure compliance with regulations (e.g., GDPR, CCPA) when managing data.

Tools and Technologies to Support the Framework:

Data Quality Monitoring Tools: Talend, Informatica, DataRobot, or custom-built dashboards.
UX Tools for Data Visualization: Figma, Adobe XD, Tableau, Power BI.
User Feedback Tools: Hotjar, Typeform, Qualaroo, or built-in feedback systems in the app.

Data Quality Monitoring Tools: Talend, Informatica, DataRobot, or custom-built dashboards.
UX Tools for Data Visualization: Figma, Adobe XD, Tableau, Power BI.
User Feedback Tools: Hotjar, Typeform, Qualaroo, or built-in feedback systems in the app.