Chapter 1
Tracking citizens’ changing needs through data
To keep up with evolving citizen expectations, governments need to take their data strategy to the next level using four key technologies.
According to EY 2022 Tech Horizon survey respondents, meeting citizens’ changing demands is the most important objective of their transformation efforts and the most important factor driving successful transformation.
But keeping up can be a struggle: 65% of respondents cited difficulties in tracking fast-changing citizen needs, or the lack of data or technology to track and measure these changing needs, as the most significant challenge to improving citizen centricity in the organization. Against this backdrop, citizens are enjoying better private sector digital experiences, which has raised their expectations from their governments.
“The whole reason for our digital government focus right now is that idea of the changing citizen expectation,” said a Canadian government leader. “We are very focused on improving government services to be more aligned to how people are used to doing things in their day-to-day lives.”
To keep up with the pace of change and the evolving expectations of users, governments will need to prioritize better use of data by integrating technology and embracing a data-centric approach. This requires a data strategy that reaches across functions, expanding beyond the IT team — where in most cases, it currently sits — to the heart of a government’s operating model.
Four core technologies to help build data centricity
Governments with a strong tech-enabled transformation agenda bring a data-centric approach to every decision, innovation and experience via an effective technology stack. An organization’s data journey is built on a long-term investment in technology, but it’s often hard to know where to start or what to prioritize.
Tech Horizon research indicates that four core technologies are expected to account for the highest investment, and to deliver the greatest value in the next two years, serving as the foundation from which to construct a successful data transformation:
- Cloud
- Data and analytics
- Internet of Things (IoT)
- Artificial intelligence (AI) and machine learning (ML)
Chapter 2
Rapid innovation is on the horizon, but barriers remain
As public entities embrace innovation, a rapid shift in how governments deliver services is expected – but there are challenges to overcome.
In a few short years, data centricity will drive and predict the most important decisions, processes and interactions of government entities, helping them to meet the changing needs of citizens and offer more personalized and timely services. Using data in predictive modeling, they can anticipate and respond to future crises, improving resilience for the long term. These advancements will also help to improve potential revenue generation, resource allocation and operational efficiency — which is especially critical given the current high levels of public debt and pressure on government budgets.
Specifically, in the Tech Horizon research, we learned that 41% of respondents expect to use data science and AI over the next two years to develop self-serve tools to improve the experience of both citizens and employees, and 38% aim to predict trends to meet customer needs and optimize operations.
Barriers to achieving data centricity in government
Governments are, unfortunately, behind the curve on integrating data into their operations. From this research, we learned that only 17% of respondents believe they are a data-centric organization. At the same time, 47% of respondents believe that data and analytics is the most important digital and tech-related skill needed for an organization to transform.
Tech Horizon research
47%of respondents say data and analytics is the most important digital and tech-related skill for transformation.
Tech Horizon research
17%of respondents currently believe they are a data-centric organization.
Many data transformation projects fail in the early stages. To move forward successfully, it’s important to consider the range of potential challenges that governments could confront along the way, including:
- High technology infrastructure costs
- Difficulty in upgrading legacy infrastructure
- Complexity of connecting and integrating diverse systems
- Cybersecurity vulnerabilities
- Inability to scale proof-of-concept initiatives
- Lack of leadership and staff buy-in
- Compliance with changing regulatory requirements
In this research, respondents reported that complex security and privacy requirements (37%), the high cost of technology infrastructure (31%) and complexity with connecting multiple systems (31%) pose the most significant data and technology barriers to executing their data transformation.
In terms of data specifically, difficulty scaling data-driven products and services (36%), difficulty finding and combining data from multiple systems (31%), and leadership that relies on intuition to make decisions (26%) rank as the biggest challenges to embracing data centricity.
Managing risks around data
The increased use of data also comes with controversy. As more people and devices are connected, the volume and variety of data created, and the speed at which it is gathered, will increase. This raises the risk of potential encroachment on privacy and digital security. Effectively navigating this challenge helps to build trust between a government and its citizens, which is a core foundation upon which data centricity is built. To increase trust around the use of data, governments must continually educate their citizens about new digital capabilities and the benefits they offer.
“Governments and citizens need to trust, be brave and take some chances together,” said Jonas Groes, EY Nordics, Government & Infrastructure Leader. “We need to continue to build that trust to be able to utilize data to a higher degree.”
Many entities are focused on building trust specifically around the use of AI. From the research, we learned that organizations are focused on developing processes to evaluate AI risks such as biases and errors (41%), installing oversight by an AI ethics board (comprised of both internal and external experts) (38%), and designating a member of their C-suite or board of directors to have direct oversight of AI (37%).
Regulatory changes
In this quickly evolving landscape, regulatory, legal and governance frameworks also need to adapt. For example, policymakers must focus on issues such as data privacy, the inequities embedded in algorithms and the integrity of the information ecosystem.
The EU has taken the lead with the introduction of the General Data Protection Regulation (GDPR), and other jurisdictions are following suit with similar policies. Some governments are going further with legal frameworks that give people a level of active control over their data and the right to know what is being done with it. In Estonia, for example, citizens can choose how to share information with government bodies and see exactly which public servants are using their data and for what purpose. Additionally, the South Korean government is introducing MyData, which enables citizens to directly manage their personal information held by government agencies.
Chapter 3
Actions governments can take to become data-centric
The opportunity around data centricity is too large to ignore. We offer six actions governments can take to move forward.
Investment in data analytics is expected to be a key priority for governments around the world in the next two years. While currently there are notable barriers in place to achieve data centricity, the potential benefits are too great to ignore.
Through the Tech Horizon research, we identified six actions governments can take to progress their data centricity journey today and into the future:
- Align your leadership with your data strategy vision
- State the ambition.
- Outline clear goals and a roadmap.
- Build a leadership culture that drives change.
- Tailor data programs for local conditions.
- Promote collaboration.
- Create a business case summarizing the long-term goals and potential benefits of your data strategy
- Explore use cases.
- Identify long-term savings.
- Target quick wins.
- Explore commercial partnerships.
- Build a strong technology foundation that can adapt to a changing regulatory environment
- Facilitate data interoperability through your technology build.
- Ensure data quality.
- Implement robust governance policies.
- Prioritize cybersecurity measures.
- Work to build public confidence and trust regarding the use of data
- Build trust in use of data and related insights.
- Be transparent about what data is being used and shared, including who it’s being shared with and what it’s being used for.
- Build systems and programs that support staff adoption and proficiency
- Engage staff through user-centered design.
- Invest in training and change management programs.
- Highlight benefits and quickly address staff concerns.
- Encourage continual innovation to help embed change across your teams
- Evolve through evaluation and feedback.
- Incentivize fresh thinking.
- Harness technical capabilities.
- Foster a network among your team that supports new ideas and innovative thinking.
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Summary
The post-COVID-19 era provides the opportunity for governments to embrace a data-centric approach to better understand and serve the needs of their increasingly tech-savvy citizens while creating overall efficiencies in their operations. In fact, many jurisdictions are prioritizing investment in the data space over the next two years.
