🌐 Examples

The Directorate of BPS (Badan Pusat Statistik) Statistics Indonesia, which manages tourism statistics, was able to convince its leadership to invest half of the domestic tourism survey budget into a collaboration project with the operator, with a plan to replace existing surveys in two years. The project paid off in terms of cost savings and quality improvements.

Indonesia is one of the largest countries in the world (in terms of population) to demonstrate the power of mobile network data for statistical practices. Previously, the domestic tourism survey of BPS Statistics Indonesia was a massive undertaking, involving thousands of enumerators—around 8,000 per year—covering the vast country door-to-door. 

By replacing annual household surveys with operator data and a digital survey, Indonesia has achieved increased levels of detail and quality with significantly fewer resources. A small team of data scientists has successfully implemented and is managing this innovative approach along with other big data projects. Cost-savings reach 50 percent annually while at the same time producing domestic tourism outputs of a higher detail than before.

Uruguay’s National Statistical Office (INE) has found that a consultation on data needs could be organized as an event with stakeholders, excluding mobile network operators initially. The objective of this preliminary gathering would be to align government ministries on potential statistical applications and their specific data requirements, while strategizing with regulators on accessing mobile network data. Engaging a strong ally, such as a Ministry of Planning, could bolster the initiative, as the NSO alone may struggle to drive this effort.

❗Tips

❌ Broad or generic data requests without specific linkages to statistical objectives will raise doubts about the purposes of data access.
✅ Clearly define specific statistical objectives and corresponding data requirements.

❌ Not connecting data needs to the country's development goals will make it harder to garner broad stakeholder support for the project.
✅ Link data needs explicitly to the country's development programs and priorities.

❌ Presenting operator data as “just another data source” underrepresents its unique value to national statistics.
✅ Highlight the benefits of using mobile network data compared to traditional methods of collecting data for statistics, like surveys, emphasizing  cost, timeliness, and granularity.

📖 Resources

The Generic Statistical Business Process Model (GSBPM) is the tool to create a detailed plan for how new data sources such as mobile network data can be integrated into the production of statistics. Creating a GSBPM is a helpful way to navigate building a business case for data. https://unece.org/statistics/documents/2019/01/standards/gsbpm-v51 

⏩ Next actions

  • Spend time identifying user needs and understanding how the operator solves these needs and increases the efficiency of public decision-making.

  • Match the objective of the project to national development goals.

  • Estimate the potential impact on statistical processes, quality, and results.

  • Use this preparation work to get the full backing for the project from leadership and supervising ministries or central government authority.