[CITY] will invest in skills, tools, and modern procurement strategies to enhance data-driven decision-making across the municipality.

Understanding [CITY]’s evolving data maturity

A maturity assessment provides a generalised understanding of the city’s current data capabilities and sets a baseline to track progress over time. By identifying strengths and weaknesses, the municipality can prioritise its efforts and allocate resources effectively. A criticism of data maturity assessments has been that they actually hinder organisations in data maturity because they create a rigid step based process that has to be fulfilled in order to progress. The municipality should be embracing descriptive, diagnostic, predictive and prescriptive analytics in its data work. However this should less be seen as a progression, but rather something that each unit, department or agency is deploying all of the above to solve real-world problems, based on an iterative process. The municipality should conduct a data maturity assessment, and through that begin to work at all levels of analysis based on problem fit and city capability readiness.

There are several global examples of data maturity measurement, such as models designed by Think Digital and Harvard Kennedy School. Based on [CITY]’s needs, data and digital maturity, should unpack the following issues:

data and digital governance,
data and digital capacity,
the use of data in decision-making,
openness and transparency.

An example of a data and digital maturity that was conducted in [CITY] can be found here.

Developing new skills through a data academy

As data becomes more integral to city operations, staff across all levels must develop the necessary skills to manage and use data effectively. Cities around the globe previously felt that technology and data was not their job and relied on outsourced vendors and the city’s IT team to advise about topics related to data and technology. However, modern cities require a different approach. Each and every municipal official needs to have the minimum viable technical knowledge required to use data in their day-to-day work.

To build a data-savvy workforce, the data academy will emphasise three interdependent spheres: people, processes, and ways of working.

People: This sphere focuses on equipping city staff with the core data skills needed to perform their roles effectively. Training should include foundational data literacy, data ethics, and privacy practices. Additionally, more advanced data skills, such as data analysis and visualisation, will need be available for specific roles. By enhancing data competency across all staff levels, the city ensures that employees can interpret, handle, and communicate data confidently, contributing to informed decision-making across departments. To improve municipal capacity for inclusive data governance, [CITY] will prioritise the recruitment, training, and career development of women, youth, and historically marginalised professionals in data-related roles. This includes establishing mentorship programs for women in data science, funding training programs for municipal staff on ethical data use, and developing community-led data literacy programs to empower residents with the skills to interpret and use municipal data.

Processes: Effective data management relies on robust processes. Training will need to cover data governance, data quality management, and the implementation of data standards, ensuring that staff understand the importance of consistent and secure data handling. Processes also include lifecycle management, from data collection to storage and eventual archiving. This framework will help staff work within a structured, secure system, contributing to high-quality data outputs.

Ways of Working: Beyond individual skills and processes, the city will need to adopt collaborative and agile working methods to enhance productivity and innovation. Agile training needs to focus on enabling staff to work efficiently in cross-departmental teams, manage projects iteratively, and adapt to changing requirements. By creating a safe environment to share ongoing work, the city can promote a learning-focused, resilient workforce prepared to evolve with changing demands. This is outlined further in the section titled “Ways of Working.”

By embedding training within these three spheres, the city can build an adaptable, capable workforce that views data as a core aspect of every role. This holistic training approach aligns with the city’s vision of becoming a data-driven, innovative, and responsive organisation.

Defining new specialist roles

For a data-driven city building a skilled workforce is foundational. In addition top providing training, there must be clearly defined job roles for data specialist. People with the right skills then need to be found to fill those roles. The following are some example roles [CITY] can develop:

Data Analyst - Interprets and visualises data to uncover insights that inform municipality decisions and improve processes.
Product Manager - Defines the vision, strategy, and roadmap for a product, coordinating teams to delivery value to users and achieve goals.
User Researcher / Designer - Studies user behaviours and needs to design intuitive, user-centred experiences that enhance product usability and satisfaction.

Using the above list as a starting point, the Data Council should agree and publish updated job role descriptions for data related roles across the municipality. For the recruitment of new roles, a representative from the Office of the Chief Data Officer will aid departments with recruitment.

Build vs buy

The data tools used in [CITY] need to be adopted based on different considerations. The current [CITY] model is procurement-led, where software is purchased from third-party vendors. However, cities around the globe are moving towards a hybrid approach where software capabilities are also created within the city and tools are built based on user needs. Rather than limiting the choice to one or another, the city decides based on their needs, considering various factors.

The considerations for the decision can be grouped into two main aspects; the organisation-wide strategy and the department service delivery. The former ensures that the software aligns with the City’s vision, limits its risk in terms of privacy, security, etc. and enables departments to collaborate towards improving decision making. The latter ensures the department’s autonomy and specific “local” needs are taken into consideration towards meeting their service delivery KPIs.

💡 Idea: Jennifer Pahlka, a leading digital governance adviser, astutely shows in her article how software needs evolve and rather than think about spending to procure in one chunk, we need to create systems where we can build software over time.

In a nutshell, “software is never done”. With the product and agile thinking, our institutions can go into the new age of using their data with the best tools that work for their teams.

Just like cities, software only works when it works for its users.

Action Items

Conduct a city-wide data capability assessment and repeat it every 18 months
Create or update role descriptions for key data roles
Design and test a data training course for city staff
Design and test an agile delivery training course for municipal officials
Develop an assessment framework to evaluate when to build in-house data tools or host open-source data tools versus when to procure commodity tools from commercial vendors
Establish a governance process for technology decisions that balances city-wide strategy with departmental service delivery needs