The development, integration and adoption of information management and governance frameworks are a necessity, especially in an era where the quality of data collected is more important than ever. As information is a strategic asset, governments need to protect, leverage and analyse both structured and unstructured information to better serve and meet mission requirements.

Public sector leaders need to lay the groundwork to correlate dependencies across events, people, processes and information to establish data-driven organisations to accomplish their mission. This is why creating effective information and data governance strategies, policies and frameworks to drive the quality, accuracy and availability of insights are essential.

With recent advancements in data analytics, business intelligence, Machine Learning and Artificial Intelligence, governments can better predict and anticipate problems more accurately rather than react to them.

While this is not new, the difference today is the regularity, accuracy and consistency delivered made available with the current power of analytics. Massive data sets, millions of pages of unstructured text and information stored across silos and borders can now be analysed to identify patterns, forecast trends and mitigate problems.

Data analytics allows governments to see the bigger picture – understand where to increase efficiencies, cut waste, improve policies and monitor budgets.

Public sector agencies are working to radically improve their operations and services – driving the need to structure, collect and store data that will improve analysis and offer better actionable insights. Therefore, there has never been a more important time for data collaboration and a single source of truth.

The Singapore public sector has been leading the charge in digital transformation and data analytics in the region. The nation has developed new infrastructure to digitally industrialise the management, governance and use of data to support and scale data transformation initiatives.

In the world of data democratisation, breaking down information silos is the first step toward user empowerment. This can only be done with reliable analytics tools capable of desegregating and connecting previously siloed data, making it manageable from a single place. Governments need to be more intuitive to sense and respond to new technology opportunities that could drive digital transformation in times of constant change.

HyperIntelligence – a relatively recent concept – is about making data available to the staff to ensure convenience, access and safety. Considered the future of Data Analytics, it relies on trusted sources and personalising information for specific roles within the company. The future of Data Analytics is to provide critical data insights for specific keywords on all web applications.

To effectively leverage data insights to deliver citizen-centric services, data and analytics are crucial for government agencies. While it cannot be used to solve every challenge in society today, it is a great step in the right direction.

This was the focal point of the OpenGov Breakfast Insight on 10 September 2021 – a closed-door, invitation-only, interactive session with Singapore’s top government agencies. This session aimed to provide the latest information on how government agencies can use data analytics to drive mission outcomes.

Finding Partners to Leverage Data Analytics

To kickstart the session, Mohit Sagar, Group Managing Director and Editor-in-Chief at OpenGov Asia delivered the opening address.

Data on a global scale has taken on an entirely different dimension and Singapore is no different. In fact, compared to other countries in the region, the nation is well ahead of the curve and leads in data analytics. The public sector has spent huge amount of money on technological innovations.

While Singapore collects massive amounts of data, quantity alone is not enough to make informed decisions. Where, how and when is critical as is how the data is structured and made uniform. For better and more relevant data, information silos need to be broken down. Democratisation, integration and sharing will all be key to bettering citizen services and enhancing citizen experience.

To democratise data, the public sector needs to empower its entire workforce – from top to bottom. For the most part, data is often only accessible to people in higher positions or specific departments, creating disparity and lacunae. The information gap must be bridged with appropriate empowerment – be it through awareness, training or skill up-gradation.

Access to large data sets is essential for a government’s digital transformation journey. Of course, data in and of itself is not the end goal – data must serve as a tool to derive understanding that enables effective decision making. Actionable insights from analytics will ultimately enrich the citizen experience.

In closing, Mohit emphasised the importance of partnerships that could help leverage data analytics for an organisation. By working with the right people, a company can accelerate its digital journey towards effective digital transformation.

Global State of Enterprise Analytics​

Kyung-Whu Chung, Director, Sales Engineering, APAC, MicroStrategy spoke next of the criticality of quality of data in digital transformation. To set the context, Kyung Whu revealed that a recent survey showed that 94% of respondents say that Data and Analytics are important to their business growth and digital transformation. While this may be obvious, it bears more elaboration and explanation.

There are huge benefits for organisations to use data analytics, including improved efficiency and productivity. Better data analytics leads to faster and more effective decision-making and, ultimately, results in better financial performance. Data analytics also assist organisations to identify and create new promising products and services.

While benefits are clear internally, there are advantages for the consumer as well. Customer satisfaction and experience are both critical for a company to thrive was the key. Data analytics help better understand consumer behaviour, trends, demands and also identify issues. It has improved customer acquisition and retention with enhanced customer experience.

Contrarily, the same survey showed that only 21% of potential business users are using data. The vast majority (97%) of real-time decisions are data deprived. This indicates, surprisingly, that organisations and agencies are still relying on their intuition and manual analysis to solve complex problems with multiple variables.

Barriers that limit the uptake of analytics have been well articulated. Kyung-Whu identified the top three concerns  – data and privacy concerns, limited access to analytics and lack of talent and training.

On the issue of privacy, 38% of organisations said more than 50% of their data is certified by an organisation authority or adheres to corporate policies. Despite this, customers are concerned about their sensitive and personal data. Organisations need to build trust and communicate properly on the use of data responsibly. This will encourage customers to be more inclined to provide their information.

When it comes to access, data-driven culture often gets stuck at the top. Access to the organisation’s data and analytics is usually concentrated on specific roles. Democratising data is important as it empowers all departments and encourages data-driven decisions at all levels throughout the company.

The last challenge that organisations need to tackle is the lack of talent and training. While simple enough to understand, there needs to be a more intentional drive and strategy to reskill and upskill employees.

In closing, Kyung-Whu encouraged delegates to expanded their thinking and embrace a multi-tool environment​. A data-driven culture can only be built on data democratisation, enabling everyone to access every process and every app. Collecting data is only a start, organisations need to enrich the data to gain deeper insights.

Health AI Strategy

Delegates then heard from Sutowo Wong, Director, Analytics and Information Management Division, Ministry of Health, Singapore who elaborated on the AI strategy and use cases in the nation’s health division.

Sutowo acknowledged as the nation shaped its health AI strategy, it needed to be mindful of the external macro trends. One such trend is the democratisation of data and analytics. Self-service analytics and the rising demand for data visualisation requires a better user experience for both data and insights​.

The next trend was the rise of analytics apps. Role-based actionable insights needed to be more easily consumed and deployed. Moreover, the ability to support decision making is still the most significant challenge to realising value from investments in analytics​.

Singapore’s health AI strategy is aligned with the national AI strategy  – a vision that is committed to making Singapore a leader in developing and deploying scalable, impactful ​AI solutions in key sectors of high value and relevance to citizens and businesses states by 2030.

Specifically, the vision in the health field is to transform and enhance policy decision making, delivery of care and patient outcomes as well as internal operations through the development and deployment of scalable AI solutions in the healthcare sector​.

To achieve the vision, the government has developed a strategy and framework for health​, identified and driven impactful and feasible AI use cases that could be scaled across the healthcare system, and leveraged ecosystem enablers for AI in the health sector.

Sutowo shared the example of the self-learning retinal screening tech as a successful use case of deploying AI in healthcare. Singapore Eye LEsionN Analyser (SELENA+) is a deep learning system jointly developed by the Singapore National Eye Centre (SNEC) and the National University of Singapore (NUS) that can cut the time needed to screen for Diabetic Retinopathy (DR). ​SELENA+’s capabilities in analysing retinal images could be extended to a predictive risk assessment model for cardiovascular diseases.

Another use case is AI in the health grand challenge, JARVIS. The initiative aims to help primary care teams stop or slow disease progression and complication development in Diabetes, Hypertension and hyperLipidemia (DHL) patients by 20% in 5 years​.

Singapore is upskilling talent based on the whole government analytics competency framework. In the end, Sutowo believes, that beyond AI, the rapid growth in digital health presented opportunities to redefine Singapore care and financing models.

Interactive Discussion

After the informative presentations, delegates participated in interactive discussions facilitated by polling questions. This session is designed to provide live-audience interaction, promote engagement, hear real-life experiences and impart professional learning and development for the participants. It is an opportunity for delegates to gain insight from subject matter experts, share their stories and take back strategies that can be implemented in their organisations.

The opening poll inquired about the main challenge delegates face in their data strategy journey. Almost half (47%) chose a lack of data culture/literacy/skill across employees as their primary challenge. A little less than one-third (32%) thought that missing an overall strategy that crosses departments and teams is their biggest obstacle. Data privacy and security concerns are the biggest challenges for 16% of delegates while 5 % chose a lack of a centralised tool for sharing and collaboration.

The second question inquired about the best option to overcome the people challenge. Again, almost half (44%) believed that their best choice is to increase data literacy by providing education and certification programs. A quarter chose the leadership team to mandate all employees to use the analytic tool as their best option while 19% opted to improve the current process for business users to get instant data. About a tenth (12%) indicated that providing employees with a self-service analytic tool would be their best option.

On being asked about what their business users do when they have new data requirements, almost two-thirds (65%) approached the IT department directly for support. While almost a quarter (23%) went by their gut feeling, 12% opted to raise a Helpdesk ticket.

The next question was about their agency’s biggest data management barrier. Delegates were equally divided (26%) between data collection and data accessibility and sharing. A little more than one-fifth (21%) identified data accuracy – providing a single source of truth – as their main barrier. While 16% chose real-time insights, about 11% went with regulatory compliance.

Delegates were asked what their agency is doing to manage their data management challenges. Almost half (48%) chose a combination between working with current service providers for better efficiencies, and sourcing for service providers to bridge the gap. A third chose to work with current service providers to improve efficiencies and maintain costs. Almost a fifth (19% ) chose to source for service providers to bridge the gap, alongside existing vendors.

On being queried about how many systems their agency store its data, almost three quarters (71%) employed over 10 systems. One-fifth (20%) used 2-4 systems while 10 % had between 5-9 systems.

The next poll asked which applications delegates spend most of their working days. A majority (70%) spent their time with email while a quarter often used productivity applications (like Microsoft Office).

Asked about whether delegates have considered zero-click experience for data, almost two-thirds (68%) have not considered it while a third (32%) have.

The final issue asked delegates’ top data strategy priority in the next 2 years. Well over half (60%) prioritised data sharing to generate insights across agencies boundaries to equip decision-makers with information needed to execute operations better and plan for future contingencies. The remaining delegates were equally split (20%) between prioritising to empower staff with meaningful data insights to drive decisions and to accelerate legacy modernisation to improve resilience and agility.

Conclusion

The Breakfast Insight concluded with remarks from Kyung-Whu Chung who highlighted the role of data analytics and the need for agencies to begin leveraging it. He urged agencies to become data-driven and advised them to accelerate their digital transformation.

In closing, he invited the delegates to reach out to his team to explore ways they could work together to assist them on their journey.