We took on the initiative to address data quality bottlenecks through the adoption of machine learning techniques. This case study highlights how Arrayo’s innovative approach helped the client proactively identify and rectify data quality issues, leading to improved operational efficiency and increased trust in the data.

Challenge

The client, a prominent organization in the financial services industry, faced data quality challenges due to the increasing volume of data processed daily. This impacted their business and operational monitoring, leading to delays in identifying and resolving data issues.

Before engaging Arrayo, the client encountered several pain points:

• Frequent data quality tickets from end users, which disrupted development activities.
• Manual efforts by Business and Data Analysts to identify and understand data issues, leading to time wastage.
• Jeopardizing trust between the business and the data office due to recurring data quality problems.

Delivery

Arrayo devised a proactive data quality monitoring strategy using machine learning techniques. The key components to the approach were as follows:

• Selecting a target dataset for investigation.
• Applying unsupervised machine learning algorithms to identify outliers and anomalies.
• Presenting detected anomalies through a dashboard, showcasing data quality Key Performance Indicators (KPIs).

The Data Analysts, end users, and Subject Matter Experts (SMEs) collaborated to confirm the identified anomalies as data issues. Based on their impact on reports, priority was assigned to the issues for remediation. Arrayo’s team communicated the identified data issues to the developers for prompt resolution.

Value

The adoption of machine learning for data quality monitoring resulted in several favorable outcomes for the client:

• Reduced time spent on data quality tasks, enabling Business and Data Analysts to focus on developing new reports and innovations.
• Strengthened trust between the business and the data office, fostering a smoother collaboration.
• Decreased manual effort in identifying data issues, leading to improved operational efficiency.

Throughout the project, Arrayo learned valuable insights, including:

• The importance of involving SMEs in the feedback-driven approach to fine-tune the machine learning model.
• The need for data profiling and analysis to ensure accurate anomaly detection.
• The significance of building a knowledge base of anomalies to enable supervised learning for future enhancements.

The successful implementation of machine learning for data quality improvement showcased Arrayo’s commitment to delivering innovative solutions that drive business success. Through the proactive resolution of data quality challenges, Arrayo enabled the client to make informed decisions, enhance operational efficiency, and mitigate data-related risks effectively.