I rely on quality data regularly. On 28 March 2025, quality data informed many of my decisions following the magnitude 7.7 earthquake in Myanmar, which also affected Bangkok. People evacuated homes, offices, and shopping malls and gathered in streets, causing severe traffic congestion. Public transport was suspended as a safety and security precaution. This information was available in real time via social-media reels, and guidance from UN arrived regularly to my phone.

I was in the FAO Regional Office for Asia and the Pacific (FAO-RAP), working on the regional agricultural census questionnaire when the earthquake hit. Social media reels shared news that Bangkok’s governor had declared a state of emergency and that the subway system had been shut down for safety reasons. Ride-hailing apps indicated wait times which were two to three longer just for the arrival of taxis. This informed my decision to have dinner near the office and head home later. Several colleagues said that what is usually a 40-minute taxi ride turned into a three- or four-hour journey home. I’m sure you also have similar examples and experiences where the right data on time helped you make better decisions in your everyday life.

This raises two questions: (1) What is quality data and statistics? (2) Why is it important for organizational decision-making?

As for the first question, for data and statistics to be considered of quality, data must be fit for use. That is, they must be relevant, accurate, and timely for their intended use. FAO defines data quality across several dimensions: relevance; accuracy and reliability; timeliness and punctuality; coherence and comparability; and accessibility and clarity[1]. For many national statistical offices, relevance, accuracy, and timeliness are the most important among these dimensions.

On the second question, quality data is essential for governments, businesses, and civil society for the same reasons it matters to each of us.

When data are fit for use and accurate, reliable, and timely, they provide an organization–be it a government, a company, a university, or a charitable non-profit–with solid evidence for designing sound policies and interventions. As a result, we often hear that data are the foundation of evidence-based decision-making. For example, whether it’s food security, climate resilience, sustainable development or poverty reduction, evidence-based decisions directly contribute to the FAO’s “Four Betters”: better production, better nutrition, better environment, and better life.

FAO-RAP, is  often called on to provides technical assistance in measuring the severity of food insecurity experienced by individuals or households across the region (SDG 2.1.2). The statistical method we rely on is the Food Insecurity Experience Scale (FIES) which identifies the percentage of a population that has experienced food insecurity at moderate and severe levels. These metrics help governments estimate how many people may need income support and plan budgets more effectively.

FAO supports government partners to harness science, technology, and innovation (STI) to modernize data collection and processing–digitizing surveys, applying automated data-quality checks, and deploying remote-sensing technologies. FAO-RAP provides technical support and capacity building to countries across the region. The objective is to enhance the quality of agricultural data and statistics and to reduce the costs of producing them.

National statistical offices and ministries of agriculture benefit from FAO capacity development and technical assistance to improve the quality of data and statistics.

FAO has assisted countries to shift from paper-based questionnaires to computer-assisted methods using desktops, laptops, tablets, and other mobile devices. When conducted in person, it is called computer-assisted personal interviewing (CAPI); when administered online, it is computer-assisted web interviewing (CAWI). This approach improves the accuracy and timeliness of the data by preventing invalid responses and eliminating the time required to manually enter data from paper-based forms into the computer.

Specifically, built-in skip patterns, range checks, and on-screen prompts reduce data-entry errors and missing values, while GPS timestamps (with optional photo verification) strengthen data integrity and quality.

The CAPI software offers benefits to management such as same-day feedback to enumerators–even in remote areas–is built into the workflow: completed interviews sync to the central server, supervisors review them on the dashboard, and the approve/reject with comments functions send clear instructions back to enumerators on the next sync. Moreover, built-in progress and quality reports (response rates, per-enumerator duration, and map-based coverage), along with automated flags for under-covered areas, highlight anomalies and enable rapid reassignments and corrective visits.

Recent technical assistance with colleagues at Sri Lanka’s Department of Census and Statistics for the 2025/26 Economic Census (Agricultural Activities), used CAPI software to streamline the survey workflow, made interviews more user-friendly, shortened interview time, and improved data quality.

FAO-RAP supports over 25 countries across the region in statistical capacity development and modernization. Our areas of work include agriculture censuses and surveys, sampling design, food consumption surveys, the 22 SDG indicators under FAO custodianship, and adoption of new Big Data sources (such as satellite imagery) and cost-effective technologies (such as tablet-based digital data collection). The office has also produced how-to guidelines and hands-on virtual training materials in the above areas to expand our capacity to assist countries.