A dashboard used by government agencies to monitor key performance indicators (KPIs) and communicate progress made on strategic outcomes with the general public and other interested stakeholders.

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.

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 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.

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.

Detail and statistics surrounding the Quality Key Performance Indicator comprising details of audit checks on titles with outcome and any feedback for the local office.

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.

--- 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...

Comparison of registries to DHIS2 data for surgical KPIs.

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.

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.

Experimental data products are innovative statistical products created using new data sources or methodologies that benefit data users in the absence of other relevant products.

The development of experimental data is one important path towards the creation of new, regularly occurring statistical products. When resources permit, experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production and will be published via https://www.census.gov/data/experimental-data-products.html.

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 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.

IntroductionThe Lithuanian Stroke Database (StrokeLT) aims to automate data collection and key performance indicator (KPI) monitoring across all stroke-ready hospitals, addressing the limitations of manual processes and facilitating evidence-based improvements in stroke care nationwide. This publication outlines the selection process and target values of the KPIs designed to standardise and enhance stroke care quality in Lithuania.Study populationThe database will include all adult patients diagnosed with stroke or transient ischemic attack (TIA), admitted to Lithuanian stroke-ready hospitals, encompassing approximately 9,582 annual stroke and 1,899 TIA admissions based on 2023 data. The database will ensure comprehensive national coverage by integrating data from stroke centres via a centralised electronic health record system.Main variablesA total of 53 KPIs were selected through a multi-stage Delphi process involving national experts and guided by international standards. These KPIs include 44 process metrics, such as timeliness metrics, early rehabilitation, and availability of secondary prevention, as well as 8 outcome metrics, including functional recovery, completion of a patient feedback survey and mortality. This framework enables comprehensive monitoring across all stages of patient care, as well as incorporating valuable patient feedback.ConclusionThe Lithuanian Stroke Database establishes a standardised automated framework for monitoring stroke care using 53 KPIs, selected through a multi-stage Delphi process involving all relevant stakeholders.