Data Quality Software Market size was valued at USD 4.7 Billion in 2024 and is projected to reach USD 8.3 Billion by 2031, growing at a CAGR of 7.4 % during the forecast period 2024-2031.

Global Data Quality Software Market Drivers

Rising Data Volume and Complexity: The proliferation of data is one of the leading drivers of the data quality software market. With businesses generating massive amounts of data daily—from customer interactions, financial transactions, social media, IoT devices, and more—the challenge of managing, analyzing, and ensuring the accuracy and consistency of this data becomes more complex. Companies are relying on advanced data quality tools to clean, validate, and standardize data before it is analyzed or used for decision-making. As data volumes continue to increase, data quality software becomes essential to ensure that businesses are working with accurate and up-to-date information. Inaccurate or inconsistent data can lead to faulty analysis, misguided business strategies, and ultimately, lost opportunities.

Data-Driven Decision-Making: Organizations are increasingly leveraging data-driven strategies to gain competitive advantages. As businesses shift towards a more data-centric approach, having reliable data is crucial for informed decision-making. Poor data quality can result in flawed insights, leading to suboptimal decisions. This has heightened the demand for tools that can continuously monitor, cleanse, and improve data quality. Data quality software solutions allow companies to maintain the integrity of their data, ensuring that key performance indicators (KPIs), forecasts, and business strategies are based on accurate information. This demand is particularly strong in industries like finance, healthcare, and retail, where decisions based on erroneous data can have serious consequences.

CSV files with monthly LNDS KPI numbers starting from March 2024 to May 2025. The data format and glossary were updated on 02-07-2025. Glossary: Year-Month (YYYY-MM) - The timeline of data and the format is YYYY-MM. fte_status_month -The total number of FTEs, here FTE means full-time equivalent, it is a standard unit used in Human Resources to measure an employee’s workload relative to a full-time schedule. One FTE equals one full-time employee, typically working 35–40 hours per week, depending on company policy. safe_training_status - The percentage of all employees (more than 3 months after onboarding) get SAFe (Scaled Agile Framework) training, integrated into onboarding activities, and is calculated using a 3-month rolling average. dp_training_status - The percentage of all employees (more than 3 months after onboarding) get data protection training, integrated into onboarding activities, and is calculated using a 3-month rolling average. individual_plan_training_status - The percentage of permanent employees who are more than 6 months in the organization who have up-to-date individual training, integrated into onboarding activities, and is calculated using a 3-month rolling average. data_projects - The number of completed or active data projects; projects that are in planning or have been cancelled are not included. datasets_registered - The number of datasets in which LNDS was a key factor in registering the data in a local or national data catalogue. The datasets registration was formally started on Dec. 2024. subsidy_project - The percentage of all delivered milestones/deliverables of subsidy project for each month. We count the delivery percentage within that month. If nothing needs to be delivered during that period, then it is “n/a”. services released - The number of new services that meet all defined MVP specifications, documentation, and release criteria. external_tools_released - The percentage of achievement of expected quarterly updates during annually period. external_tools_production - The number of new tools released in production for external users. data_summit_registrations - The number of people registered for the data summit that year, as well as the number for 2025, will be calculated starting from September 1st. The value for 2025 prior to September is marked as “n/a”. newsletters - The number of regular newsletters published calculated per year. website_content - The number of content pieces added to the LNDS website per month calculated per year. tickets - The percentage of resolved tickets in 5 days, and the data was collected from July 2024.

CSV files with the monthly LNDS KPI numbers starting from April 2023. Glossary: fte_status_month - the total FTE of all employees active in the organisation, as measured on the first day of the month following the reporting month (e.g. the FTE count of October equals the active FTE on the 1st of November). FTE means “full-time equivalent”, i.e. how many hours employees work during the week divided by a full-time schedule of 40 hours. fte_delta_month - delta FTE compared to the previous month (e.g. October delta FTE is the difference between the FTE count on 1st of November minus the count on 1st of October). data_partners_status – the number of entities that utilise LNDS services either in a data provider or data consumer role. data_partners_delta - delta of the number of partners in the current month minus the previous month. data_projects_status – the number of data projects that are actively worked on by data partners and LNDS (in planning or execution). Data projects have a clear goal and outcome, they can be planned, and their completion can be verified. data_projects_delta - delta of the number of data projects in the current month minus the previous month. services_progress_status – the amount of services that are actively developed (in planning, implementation or testing). LNDS develops services in the areas of capability building and training, community management, legal-ethical-societal impact assessment, technical building blocks and platform infrastructure, as well as the components of a secure end-to-end data processing pipeline, support for data discovery, access, enrichment, pseudonymisation, quality and more. services_progress_delta - delta of the number of services in the current month minus the previous month. services_production_status – the amount of services that are in production. services_production_delta - delta of the amount of services in production in the current month minus the previous month. tooling_internal_status – the number of software tools actively used within LNDS (internal use). tooling_internal_delta - delta of the number of internal software tools in the current month minus the previous month. tooling_external_status – the number of software tools developed, tested and deployed for the use by partners of LNDS (external use). tooling_external_delta - delta of the number of external software tools in the current month minus the previous month.

Introduction: Health surveys constitute a relevant information source to access the population’s health status. Given that survey errors can significantly influence estimates and invalidate study findings, it is crucial that the fieldwork progress is closely monitored to ensure data quality. The objective of this study was to describe the fieldwork monitoring conducted during the first Portuguese National Health Examination Survey (INSEF) regarding protocol deviations and key performance indicators (KPI). Methods: Data derived from interviewer observation and from the statistical quality control of selected KPI were used to monitor the four components of the INSEF survey (recruitment, physical examination, blood collection and health questionnaire). Survey KPI included response rate, average time distribution for procedures, distribution of the last digit in a specific measure, proportion of haemolysed blood samples and missing values. Results: Interviewer observation identified deviations from the established protocols, which were promptly corrected. During fieldwork monitoring through KPI, upon implementation of corrective measures, the participation rate increased 2.5-fold, and a 4.4-fold decrease in non-adherence to standardized survey procedures was observed in the average time distribution for blood pressure measurement. The proportion of measurements with the terminal digit of 0 or 5 decreased to 19.6 and 16.5%, respectively, after the pilot study. The proportion of haemolysed samples was at baseline level, below 2.5%. Missing data issues were minimized by promptly communicating them to the interviewer, who could recontact the participant and fill in the missing information. Discussion/Conclusion: Although the majority of the deviations from the established protocol occurred during the first weeks of the fieldwork, our results emphasize the importance of continuous monitoring of survey KPI to ensure data quality throughout the survey.

CSV files with the monthly LNDS KPI numbers starting from March 2024.

Glossary (last refreshed 06.05.2024):

fte_status_month - acronym FTE means full-time equivalent, how many hours employee works during the week. Each full-time employee counts as 1 FTE, an FTE indicates 0.5 half of a full workload. FTE of an employee = Employee's actual working hours / Standard working hours of a full-time employee (min:50, target:60, max:70).

safe_training_status - the KPI tracks Scale Agile Framework training % of all employees (more than 3 months after onboarding) from 01-Aug 2024 onwards and is calculated using a 3 month rolling average (min:80%, target:95%, max:100%).

dp_training - the KPI tracks data protection training % of all employees (more than 3 months after onboarding) from 01-Aug 2024 onwards and is calculated using a 3 month rolling average (min:80%, target:95%, max:100%).

individual_plan_training - percentage of employees with an individual training plan (min:70%, target:85%, max:100%).

data_projects_status - the number of completed or in progress data projects is taken into account (the target of 50 in total, ideally >20 completed and >30 in progress). Data projects have a clear goal and outcome, they can be planned, and their completion can be verified (min:40, target:50, max:60).

datasets_registered - number of datasets in which LNDS was a key factor in registering the dataset (not through indexing or simple copying), (min:30, target:100, max:150).

subsidy_project - all сompleted milestones/deliverables of subsidy project for each month. We count delivery percentage within that month. The KPI measures the #artefacts-delivered-on time / #artefacts-due * 100% on a moving-annual basis (min:80%, target:90%, max:100%).

services_%_released - the percentage of released services that meet their objective use (min:60%, target:80%, max:100%).

services_delivered - the number of new services that meet all defined MVP specifications, documentation, and release criteria (min:4, target:6, max:9).

external_tools_release - percentage of achievement of quarterly updates in 2024. # updates released / # expected updates per quarter* 100% (min:60%, target:80%, max:100%).

external_tools_production - the number of new tools released in production for external users (min:5, target:7, max:10).

isms_pages - cumulative sum of Information Security Management System (ISMS) pages approved and deployed / target # of ISMS pages * 100% (min:80%, target:90%, max:100%).

data_summit_participants - number of people who attended the data summit (an annual figure), (min:300, target:500, max:600).

newsletters - the number of published regular newsletters (min:8, target:10, max:12).

website_content - number of content units added to the LNSD website per month (min:15, target:20, max:25).

fte_status_delta - compared to the previous month.

safe_training_delta - compared to the previous month.

dp_training_delta - compared to the previous month.

individual_plan_training_delta - compared to the previous month.

data_projects_delta - compared to the previous month.

subsidy_project_delta - compared to the previous month.

services_delivered_delta - compared to the previous month.

external_tools_release_delta - compared to the previous month.

external_tools_production - compared to the previous month.

isms_pages_delta - compared to the previous month.

data_summit_participant - compared to the previous month.

newsletters_delta - compared to the previous month.

website_content_delta - compared to the previous month.

This provides the underlying data and volumes behind the reported performance of CSG Customer Service and presented quarterly to the Performance and Contract Management Committee. It is recognised that the email volumes recorded do not reflect the total number of emails received by the council as has always been the case, and includes some webforms. This does not affect the quality of the service but needs to be addressed to show the full level of email and webform contact across the council’s services

This report, generated from Emergency Care Data Set (ECDS), sets out data coverage, data quality and performance information for the following five A&E indicators: • Left department before being seen for treatment rate • Re-attendance rate • Time to initial assessment • Time to treatment • Total time in A&E Publishing these data will help share information on the quality of care of A&E services to stimulate the discussion and debate between patients, clinicians, providers and commissioners, which is needed in a culture of continuous improvement. As of June 2020 data for the Provisional Accident and Emergency Quality Indicators is sourced from the Emergency Care Data Set (ECDS) instead of the Hospital Episode Statistics (HES) Accident and Emergency data. The specific Quality Indicators have not changed, although some of the data quality measures are to be developed at a later date. The data used in these reports are sourced from Provisional ECDS data, and as such these data may differ to information extracted directly from Secondary Uses Service (SUS) data, or data extracted directly from local patient administration systems. Provisional ECDS data may be revised throughout the year. The publication now includes an interactive visual tool, an open data csv file and a newly designed metadata file. These are in addition to the Excel tables.

This provides the underlying data and volumes behind the reported performance of CSG Customer Service and presented quarterly to the Performance and Contract Management Committee. It is recognised that the email volumes recorded do not reflect the total number of emails received by the council, as has always been the case, and includes some webforms. This does not affect the quality of the service but needs to be addressed to show the full level of email and webform contact across the council’s services.

--- DATASET OVERVIEW --- This dataset captures detailed performance data for individual vacation rental properties, providing a complete picture of operational success metrics across different timeframes and market conditions. With weekly updates and four years of historical data, it enables both point-in-time analysis and long-term trend identification for property-level performance.

The data is derived from OTA platforms using advanced methodologies that capture listing, calendar and quote details. Our algorithms process this raw information to produce standardized and enriched performance metrics that facilitate accurate comparison across different property types, locations, and time periods. By leveraging our other datasets and machine learning models, we are able to accurately detect guest bookings, revenue generation, and occupancy patterns.

--- KEY DATA ELEMENTS --- Our dataset includes the following core performance metrics for each property: - Property Identifiers: Unique identifiers for each property with OTA-specific IDs - Geographic Information: Location data including neighborhood, city, region, and country - Property Characteristics: Property type, bedroom count, bathroom count, and capacity - Occupancy Metrics: Daily, weekly, and monthly occupancy rates based on actual bookings - Revenue Generation: Total revenue, average daily rate (ADR), and revenue per available day (RevPAR) - Booking Patterns: Lead time distribution, length of stay patterns, and booking frequency - Seasonality Indicators: Performance variations across seasons, months, and days of week - Competitive Positioning: Performance relative to similar properties in the same market - Historical and Forward Looking Trends: Year-over-year and month-over-month performance changes

--- USE CASES --- Property Performance Optimization: Property managers can leverage this dataset to evaluate the performance of individual listings against market benchmarks. By identifying properties that underperform relative to similar listings in the same area, managers can implement targeted improvements to pricing strategies, property amenities, or marketing approaches. The granular performance data enables precise identification of specific improvement opportunities at the individual property level.

Competitive Benchmarking: Property owners and managers can benchmark their listings against competitors with similar characteristics in the same market. The property-level performance metrics enable detailed comparison of occupancy rates, ADR, and revenue generation across comparable properties. This competitive intelligence helps identify realistic performance targets and market positioning opportunities.

Portfolio Optimization: Vacation rental portfolio managers can analyze performance variations across different property types and locations to optimize investment and management decisions. The dataset supports identification of high-performing property configurations and locations, enabling strategic portfolio development based on actual performance data rather than assumptions.

Seasonal Strategy Development: The historical performance data across different seasons enables development of targeted seasonal strategies. Property managers can analyze how different property types perform during specific seasons or events, informing marketing focus, pricing adjustments, and operational planning throughout the year.

Performance Forecasting: Historical performance patterns can be leveraged to develop accurate forecasts for future periods. By analyzing year-over-year trends and seasonal patterns, property managers can anticipate performance expectations and set realistic targets for occupancy and revenue generation.

--- ADDITIONAL DATASET INFORMATION --- Delivery Details: • Delivery Frequency: daily | weekly | monthly | quarterly | annually • Delivery Method: scheduled file loads • File Formats: csv | parquet • Large File Format: partitioned parquet • Delivery Channels: Google Cloud | Amazon S3 | Azure Blob • Data Refreshes: daily

Dataset Options: • Coverage: Global (most countries) • Historic Data: Available (2021 for most areas) • Future Looking Data: Available (Current date + 180 days+) • Point-in-Time: Available (with weekly as of dates) • Aggregation and Filtering Options: • Area/Market • Time Scales (daily, weekly, monthly) • Listing Source • Property Characteristics (property types, bedroom counts, amenities, etc.) • Management Practices (professionally managed, by owner)

Contact us to learn about all options.

--- DATA QUALITY AND PROCESSING --- Our data processing methodology ensures high-quality, reliable performance metrics that accurately represent actual property performance. The raw booking and revenue data undergoes extensive validation and normalization processes to address inconsistencies, identify anomalies, and ensure comparability across different pro...

This provides the underlying data and volumes behind the reported performance of CSG Customer Service and presented quarterly to the Performance and Contract Management Committee. It is recognised that the email volumes recorded do not reflect the total number of emails received by the council, as has always been the case, and includes some webforms. This does not affect the quality of the service but needs to be addressed to show the full level of email and webform contact across the council’s services.

This provides the underlying data and volumes behind the reported performance of CSG Customer Service and presented quarterly to the Performance and Contract Management Committee.

It is recognised that the email volumes recorded do not reflect the total number of emails received by the council, as has always been the case, and includes some webforms. This does not affect the quality of the service but needs to be addressed to show the full level of email and webform contact across the council’s services.

Retail Fashion Boutique Data Sales Analytics 2025

Overview

This comprehensive fashion retail synthetic dataset contains 2,176 real-world style records spanning seasonal collections, customer purchasing behavior, pricing strategies, and return analytics. Perfect for data science projects, machine learning models, and business intelligence dashboards focused on retail analytics and e-commerce insights.

Dataset Highlights

• 📊 Complete Sales Cycle: Purchase patterns, pricing strategies, and customer feedback
• 🔄 Return Analytics: Detailed return tracking with specific reasons and patterns
• 🛍️ Multi-Brand Coverage: 8 major fashion brands across diverse product categories
• 📈 Seasonal Intelligence: Four-season data with realistic markdown strategies
• ⭐ Customer Insights: Rating systems and purchasing behavior analysis
• 💰 Pricing Analytics: Original pricing, markdown percentages, and final pricing data

Key Applications

• Retail Analytics: Sales performance analysis and trend identification
• Customer Segmentation: Behavior analysis and purchasing pattern recognition
• Inventory Management: Stock optimization and seasonal demand forecasting
• Return Prediction: Machine learning models for return likelihood prediction
• Pricing Strategy: Dynamic pricing and markdown optimization analysis
• Business Intelligence: Comprehensive retail KPI dashboards and reporting

Column Details

Data Quality Features

• ✅ Realistic Business Logic: 15% return rate matching industry standards
• ✅ Seasonal Pricing: Authentic markdown patterns aligned with retail cycles
• ✅ Missing Data Handling: Strategic nulls for data cleaning practice (15% in ratings, size nulls for accessories)
• ✅ Balanced Distribution: Even representation across brands, categories, and seasons
• ✅ Price Consistency: Mathematically accurate pricing with discount calculations

Perfect For

• Data Analytics Projects: Retail KPI analysis, sales forecasting, customer behavior studies
• Machine Learning Models: Return prediction, demand forecasting, recommendation systems
• Business Intelligence: Executive dashboards, performance tracking, trend analysis
• Academic Research: Retail analytics case studies, pricing strategy research
• Portfolio Development: Comprehensive data science project demonstrations

File Formats Available

• CSV: Universal compatibility for data analysis tools
• Excel: Business reporting and stakeholder presentations
• JSON: API integration and web applications
• SQL: Database integration and advanced querying

Sample Use Cases

1. Return Prediction Model: Build ML models to predict return likelihood based on product attributes
2. Seasonal Demand Forecasting: Analyze purchasing patterns across different seasons and categories
3. Pricing Optimization: Study markdown effectiveness and optimal pricing strategies
4. Customer Satisfaction Analysis: Correlate ratings with return patterns and product characteristi...

This provides the underlying data and volumes behind the reported performance of CSG Customer Service and presented quarterly to the Performance and Contract Management Committee. It is recognised that the email volumes recorded do not reflect the total number of emails received by the council, as has always been the case, and includes some webforms. This does not affect the quality of the service but needs to be addressed to show the full level of email and webform contact across the council’s services.

In 2015, the Ethiopian Federal Ministry of Health (FMOH) developed the Saving Lives through Safe Surgery (SaLTS) initiative to improve national surgical care. Previous work led to development and implementation of 15 surgical key performance indicators (KPIs) to standardize surgical data practices. The objective of this project is to investigate current practices of KPI data collection and assess quality to improve data management and strengthen surgical systems. The first portion of the study documented the surgical data collection process including methods, instruments, and effectiveness at 10 hospitals across 2 regions in Ethiopia. Secondly, data for KPIs of focus [1. Surgical Volume, 2. Perioperative Mortality Rate (POMR), 3. Adverse Anesthetic Outcome (AAO), 4. Surgical Site Infection (SSI), and 5. Safe Surgery Checklist (SSC) Utilization] were compared between registries, KPI reporting forms, and the DHIS2 (district health information system) electronic database for a 6-month period (January—June 2022). Quality was assessed based on data completeness and consistency. The data collection process involved hospital staff recording data elements in registries, quality officers calculating KPIs, completing monthly KPI reporting forms, and submitting data into DHIS2 for the national and regional health bureaus. Data quality verifications revealed discrepancies in consistency at all hospitals, ranging from 1–3 indicators. For all hospitals, average monthly surgical volume was 57 cases, POMR was 0.38% (13/3399), inpatient SSI rate was 0.79% (27/3399), AAO rate was 0.15% (5/3399), and mean SSC utilization monthly was 93% (100% median). Half of the hospitals had incomplete data within the registries, ranging from 2–5 indicators. AAO, SSC, and SSI were commonly missing data in registries. Non-standardized KPI reporting forms contributed significantly to the findings. Facilitators to quality data collection included continued use of registries from previous interventions and use of a separate logbook to document specific KPIs. Delayed rollout of these indicators in each region contributed to issues in data quality. Barriers involved variable indicator recording from different personnel, data collection tools that generate false positives (i.e. completeness of SSC defined as paper form filled out prior to patient discharge) or missing data because of reporting time period (i.e. monthly SSI may miss infections outside of one month), inadequate data elements in registries, and lack of standardized monthly KPI reporting forms. As the FMOH introduces new indicators and changes, we recommend continuous and consistent quality checks and data capacity building, including the use of routinely generated health information for quality improvement projects at the department level.

This provides the underlying data and volumes behind the reported performance of CSG Customer Service and presented quarterly to the Performance and Contract Management Committee. It is recognised that the email volumes recorded do not reflect the total number of emails received by the council, as has always been the case, and includes some webforms. This does not affect the quality of the service but needs to be addressed to show the full level of email and webform contact across the council’s services.

This provides the underlying data and volumes behind the reported performance of CSG Customer Service and presented quarterly to the Performance and Contract Management Committee. It is recognised that the email volumes recorded do not reflect the total number of emails received by the council as has always been the case, and includes some webforms. This does not affect the quality of the service but needs to be addressed to show the full level of email and webform contact across the council’s services

The Job Services Australia (JSA) Quality Standards Pilot was established to enable Employment Services Providers and the Department of Employment to work together to finalise the operational detail of a revised Quality Assurance Framework for the next employment services contracts. This report is provided by Department of Jobs and Small Business (previously Department of Employment).

This is a development key. The index is based on users’ assessment of residents’ self-determination and the importance of day-to-day operations. The key figures are normalised so that all municipalities’ values are placed on a scale from 0 to 100 where 0 is the worst and 100 is the best. To avoid excessive impact, the value is set to 0 for municipalities with values below percentile 2.5, and 100 for municipalities with values above percentile 97.5. For those municipalities that have data on both KPIs, the unweighted average is calculated, and then the mean is also normalised to a scale of 0 to 100 in the same way. The index is the normalised average. In cases where a municipality does not have values in the key ratios index calculated, values are used from the previous year. If they are missing, this is counted as a failure. RKA’s calculations based on data from SKR.