Artificial Intelligence & Machine Learning , Data Analytics / Predictive Analytics , Technology

How Governments Leverage Multi-Source Data for Insights

Post-digital governments are rapidly evolving their data and analytics investments to generate critical insights for their core mission needs. As global, economic and environmental instability increases, 'simple' extrapolation from past experiences is no longer sufficient to address government challenges.

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Governments must use real-time data from multiple sources to make informed decisions, whether for individual intervention or to react to a complex developing situation.. The ability to come up with flexible and quicker responses is becoming critical and requires context sensitivity and best-practice decision intelligence.

Integrating Insight and Prediction With Real-Time Response

Traditional approaches in analytics have been reactive in nature. A mature organization may adopt a proactive or predictive approach. In such cases, the existing data is mined for insights that can be converted into models. Using these, policy is determined, or, in more advanced cases, actions are guided. For example, the Indian Government has opted for geotagging the entire agriculture sector to make it more secure and profitable. The program, Forecasting of Agriculture outputs through Satellite, Agro-meteorology and Land-based observations (FASAL) , uses remote sensing and data analysis to enable farmers to predict the yield of major crops and increase their income.

Government actions can have significant impacts, therefore, it is important for these actions to be appropriate to prevent secondary problems in order to achieve an optimal stable outcome for the people.

Embracing empathy with citizens and other stakeholders is critical, leveraging insights to anticipate optimal engagement and respond effectively to the unexpected. Timely feedback is as important as monitoring the impact of the action.

Managing and Using Multi-Source Data

There is a lack of significant volumes of data in many government organizations that could be used to create value for their missions. Government data is complex and large, outside of a narrow set of specific use cases. Moreover, the government collects data for different purposes in different formats, which makes sharing of government data more challenging. Aggregated data may provide more value, but it increases the cost of management and the risk of data exposure.

Post-digital governments are aware of this and use multisource data - some complex in (relatively) moderate volumes and some captured as a byproduct of normal operations, which may be high in volume but less complex in nature. Examples of this are the Real-Time Economy Project in Finland and the Ticket BAI

Using Multiple Techniques and Tools

While the aggregation of data can provide a scale base for machine learning, it can have significant flaws. Even the highest scale approaches, such as those used to support large language models, including OpenAI's ChatGPT and Google’s Bard are known to generate hallucinations. During the early stages of a developing situation, scale data may not be available. An illustration of this could be the difficulties faced by governments in providing disaster support in the wake of environmental disasters such as hurricanes, floods or fires.

A post-digital government’s response might be the use of parametric insurance. Instead of attempting to process claims for support after a disaster, the payouts are automatically calculated based on the likely needs of the relevant population.

This requires pre-analysis of impacts on a community. Payments can then be triggered easily by a much smaller and simpler analysis at the time of the disaster, such as river levels reaching a certain height. It can significantly reduce the administrative burden and delays in economic and community recovery.

Ultimately, for government organizations to deliver on their mission in the post-digital era, it is important that governments "close the loop" - collecting data that will rapidly inform both changes in policy and improve decision-making in context on the ground.

Governments should use that near-real-time feedback to enable continuous tuning of their actions, thus optimizing the ability to deliver desired policy outcomes with available resources.