On 16th May 2024 I led a 90 minute session as part of the Digital Academy Masterclass, hosted by the Government of Azerbaijan’s Innovation and Digital Development Agency, and delivered by Digital Nation.
I’ve broken the presentation into 4 parts. After the introduction, Part 1 considered the potential of data to deliver public value; this is Part 2 and looked at the elements needed to actually build a data-driven public sector; and Part 3 explored how to unlock the value of data without losing public trust.
Unless otherwise indicated or an obvious screenshot, the images were generated by ChatGPT.
David McCandless, of Information is Beautiful, suggested that instead of thinking about data like oil, we should rather think of it like soil. Data is a fertile environment from which good things might happen.
But just like soil, it is something you have to nurture and care for if you want it to give you a good return.
And this is where we start our second section – if we’re in roles with responsibility for building a data-driven public sector then we need to think about our job in terms of farming. We need to be mindful that when it comes to data our job is to make good soil and nurture data-driven ways of working.
Another way of talking about that is to call it ‘data governance’.
The OECD takes a multi-layered approach to this which I think is a really helpful way to think about it.
The starting point is strategic, reflecting the importance of leadership for the data agenda and a vision of its value.
Then it’s the tactical level, addressing issues of implementation capacity and regulation to ensure data flows regularly within government, across sectors and borders, and always under conditions conducive to trust.
Finally at a delivery, or operational, level, it is essential that data infrastructure and architecture are prioritized to simplify the ways in which departments access and use data throughout the data value cycle.
The first defining element of good data governance is providing the leadership and vision needed to ensure strategic direction and purpose for the data-driven conversation across the public sector.
Data strategies help define and create consensus around goals and roles.
The US Federal Data Strategy took a 10 year perspective on attempting to realise the full value of the country’s federal data assets. These things need to be for the long-haul, and span multiple administrations.
National strategies need to be acknowledged by strategies at organisation, sector and municipal levels
In all cases, they can benefit from open and participatory processes, integrating contributions from actors outside the public sector with a view to greater ownership of policies.
It is important to distinguish between political and administrative leadership roles.
Political leaders provide the high-level support to advance the political agenda, but data-centric administrative leaders are needed to implement and drive implementation, ensuring the continuity and sustainability needed to achieve results beyond policy mandates.
These officials may have different names and be part of different government organisations:
- The Government Chief Data Steward in New Zealand is a role held by the Chief Executive of Statistics New Zealand. They lead data policy in the country.
- In France, the Administrateur Général des Données was appointed into Etalab on its creation, alongside its wider responsibilities for data and transformation.
- The Nordic approach is less hierarchical and sees the role shared by different individuals to seek a more consensus-based leadership model in the form of a data taskforce composed of leading public sector agencies
The tactical layer enables the capacities for consistent and coherent implementation across government.
It relies on coordinating bodies, people, committees, working groups and training programs.
Ireland’s Data Governance Board was created to oversee the development and implementation of data management standards, guidelines and activities.
Individual organisations also need leadership. In Korea the creation of officers responsible for the provision of public data and its management was set out in law.
Those leaders also need to be connected and co-ordinated and work together. As do all those holding roles that influence and shape the use of data in society. The UK has a very strong model of ‘Communities of Practice’ that works at practitioner level to compare notes and share stories and challenges.
Skills and training is a further tactical step because without a competent public sector and an effective supplier base, limited progress will be achieved.
Data needs to be recognised as a core skill for all public servants. Indeed, as discussed in the OECD Framework for Digital Talent and Skills in the Public Sector, it’s part of ensuring society as a whole has the 21st century skills to thrive.
That investment in foundational skills may then be overlaid and complemented with investment into specific professional skills.
Finally under your tactical thinking, what are you doing with your funding?
This quote from Joe Biden encapsulates very well the reality that can be found of verbally making commitments to the importance of data but where other priorities actually make their way into an organisation’s budget.
If data is to be handled as a valuable asset then that can’t happen on a shoestring budget.
In Portugal, Korea and elsewhere, funding is being used to encourage interesting uses of data and to give public servants permission to experiment.
The next tactical layer is all of the regulatory activity to help define, steer and ensure compliance
Regulation helps in defining the set of rules to control the access to and sharing of data, promote openness, and ensure and enforce the protection of sensitive data. These instruments help also in the definition and enforcement of common data standards towards greater data interoperability and streamlined data-sharing practices.
Regulation can also be an obstacle if the capacity for implementation is not for good data governance for the proliferation of fragmented instruments and uncoordinated efforts can hinder cross-institutional and data integration and sharing
But legislation isn’t necessarily the only answer. It’s absolutely vital to be asking not only what can be done without new legislation? But complement it by thinking about how you make existing legislation actionable and scalable.
This is where the softer materials of Codes of Practice, Recommendations and Guidelines become valuable. Various countries have developed extensive materials that support and equip their public servants to do a good job in this area. A couple of examples would be Denmark’s Arkitektur, and the UK’s Service Manual.
Ultimately the effectiveness of regulation relies on the effectiveness of your capacities and capabilities to implement anything. Regulation is almost worthless without good approaches to governance, networking, skills and funding.
The operational layer integrates the set of processes, mechanisms and tools that allow for data governance to unlock delivery at a more granular level.
To unlock the value of data for the public sector it is crucial to understand how the different aspects of data governance connect with the Government Data Value Cycle.
Each stage is critical; overlooking any stage from the strategic or tactical levels will impede the creation of public value.
We start with data collection, encompassing both governmental and non-governmental sources. Whether data collection is intentional or as a by product of services, it must be focused and deliberate.
Once collected, data must be securely stored and processed. This phase is foundational: only with well-managed data can we move to the next stages of sharing and applying this valuable resource.
After processing, data becomes ready for sharing and publishing – suddenly data becomes a usable commodity.
In and of itself making data available begins to generate public value
But of course it is the final stage where true value emerges and data is applied beyond its initial purpose, combined with other datasets, and used to innovate and inform.
So the Government Data Value Cycle guides us through collecting, securing, processing, sharing, and reusing data. Each stage is critical; overlooking any step will impede the creation of public value.
As such it is a critical part of thinking about data governance more broadly – to make sure that the strategic, tactical and operational layers are working in concert.
All leadership, strategy, vision, regulation, training activity builds up from understanding that any work involving public sector data falls into one of these areas, with the intention to generate public value.
Data infrastructure refers to the foundational elements that allow for the collection, storage, and dissemination of data.
This infrastructure includes various technological components and systems that enable data to be managed and accessed efficiently.
Let’s explore some key aspects of data infrastructure that are crucial for enabling effective data-driven decisions in government
Data registers, catalogues and open data websites are foundational components of data infrastructure in terms of answering the operational question ‘how do I get the data that I need’?
Registers are authoritative lists storing essential information for record-keeping and transactions in public services. They are the backbone of data management, ensuring accuracy and reliability in public administration.
Open data websites are those places where government is proactively making datasets available to enable transparency, foster innovation and enhance public engagement.
Here are a few examples from Denmark, Norway, Sweden and Azerbaijan but again there are so many from around the world that would reflect this need.
Data federation and APIs are keys to unlock that data and start to allow for data to be joined together and systems to be integrated.
We touched on the value of X-Road earlier and platforms like that, or TRAY in Slovenia, provide an incredible infrastructure to join these resources together and underpin interoperability and proactive service design. Again, many countries are building their digital government maturity on foundations like these.
Data architecture involves the strategic design and arrangement of data.
It focuses on standards, interoperability, and the relationships between data sets to optimise their use and impact.
This architecture is pivotal for ensuring that the data infrastructure not only exists but also works efficiently and effectively.
Standards and semantics are core components of data architecture.
Standards are the rules and guidelines which ensure data is formatted and used consistently to maintain quality and compatibility.
Semantics is the meaning and interpretation of data, which ensures that information is understood consistently.
Again, various countries have addressed these challenges in different ways. Here are examples from France, Sweden and Norway.
That’s the conclusion for this second part on thinking about the different strategic, tactical and operational/delivery elements
Again, it’s time to discuss your own experiences. Of the six areas that we’ve discussed which are the most problematic in your organisation at the moment? Make the most problematic ‘Priority 1’ and the least problematic ‘Priority 6’.
If your organisation thinks that it’s doing really well and you’ve got practices that you think others in the Azerbaijani public sector should know about then make a note of your organisation.
And if you’ve still got time then there are a few more questions to think about.
Go back to the introductory slides, or Part 1 on the potential of data to deliver public value; or move on to Part 3 on how to unlock the value of data without losing public trust.