Dubai Data and Statistics Establishment uses synthetic data as a powerful tool in leveraging Labor Force Surveys for research .
Data and statistics are needed to solve real-world problems, create social and economic benefits, and lay the foundations of a truly smart city.
Dubai Digital Authority (DDA) Digital Dubai was established by His Highness Sheikh Mohammed Bin Rashid Al Maktoum, Vice-President & Prime Minister of the UAE, and Ruler of Dubai, in June 2021 to develop and oversee the implementation of policies and strategies that govern all matters related to Dubai’s information technology, data and digital transformation.
Digital Dubai brings together the expertise of two entities:
1- Dubai Data & Statistics Establishment
2- Digital Dubai Government Establishment
Dubai Data and Statistics Establishment (DDSE)
Established by the Dubai Data & Statistics Law of 2023, Dubai Data & Statistics is creating an ambitious strategy where data & statistics is shared seamlessly, safely and securely, using data & statistics to solve real world problems, create social and economic benefits, and lay the foundations of a truly smart city. Along the way, encouraging participation from the data community as vibrant as it is diverse. The Dubai Data and Statistics Establishment is responsible for producing and disseminating high-quality official statistics that inform policy decisions and support the city’s development agenda. DDSE plays a crucial role in this study by lever aging its expertise in statistical methodologies, data collection, and analysis. Their commitment to enhancing data quality, accessibility, and security is a driving force behind the exploration of synthetic data as a means to improve the robustness and utility of labor force surveys.
How does Dubai handle Labor Force Survey data?
Labor Force Surveys are critical for understanding labor dynamics and informing policy decisions. Traditional data collection methods, such as direct surveys and administrative data, can raise concerns about privacy, security, and accessibility. The existing methods to handle the survey data with the utmost care are time-consuming and costly, which limits the potential value of the data. Testing new data services, facilitating external researchers and experimenting in a machine learning sandbox environment are examples of use cases that are difficult to realise with real survey data.
The commitment of DDSE to enhancing data quality, privacy, security, and accessibility is a driving force behind the exploration of new means to improve the robustness and utility of labor force surveys.
Artificial intelligence to generate synthetic survey data
With BlueGen.ai’s synthetic data platform, DDSE is able to generate a privacy-safe version of the survey data. Using artificial intelligence (AI), the BlueGen.ai platform creates synthetic data that looks and behaves just like the underlying real data. They are suitable for valuable data analysis and processing, while offering similar statistical insights.
Synthetic data addresses four key use cases for Dubai Data & Statistics Establishment:
Data privacy and confidentiality: Reduce privacy threats by developing datasets that do not reveal individual identities.
Data accessibility and sharing: Allow external researchers, institutes and other stakeholders to access precise and privacy-safe labor force data in order to conduct analysis and establish evidence-based initiatives.
Supporting policy simulation and scenario analysis: Enable policymakers to examine the possible effects of various policy initiatives in a risk-free setting, which is critical for making informed decisions and developing strategic plans.
Advancing statistical methods: Provides a chance to enhance statistical techniques, notably in data integration, model creation, and machine learning applications in labor force analysis.
Measuring the quality of synthetic data sets
DDSE has defined that the most important statistical values can not exceed a 0,5 percent difference between the real and synthetic data sets. This is a very small margin, which can be very challenging for generative models to reproduce.
The quality is measured in resemblance and utility, which are mostly covered by automated evaluation reports in the BlueGen.ai software that compares the real data with the synthetic data.
In addition to the standard evaluation by BlueGen.ai, DDSE has subjected the generated data to specific tests that are relevant in labor force research, like the economic participation and unemployment rates per class of the population.
Effectiveness and use of synthetic data set
This project demonstrates how synthetic data may improve labor force survey methodology by providing scalable, privacy-protected data collecting and analysis options. Synthetic data provides substantial benefits, notably in terms of data protection, accessibility, and large-scale policy simulations. While there are still issues in effectively capturing unusual events, continuous development of models and approaches can solve these limits.
Synthetic data will become increasingly significant in labor market analysis, complementing traditional data sources. As models develop, the use of synthetic data can spread beyond labor force surveys, assisting with larger research and decision-making activities across industries. This research establishes the groundwork for future innovation in data-driven policy making, ensuring that privacy issues are handled while increasing the utility of labor statistics.
Speed up the access to data process
Developing new innovative services
Safely involve more research(ers)
“Our strategic use of synthetic data has delivered remarkable success, showcasing its transformative potential in data innovation while ensuring privacy and transparency. This milestone not only strengthens our leadership in advanced analytics but also sets a pioneering model for future growth and scalability.”