Data transformation framework: data personas report
Personas of users and organisations produced by UserVision and Effini. This project supports the development of a data transformation framework to improve and enable data reuse in the Scottish public sector.
6 Evaluation of personas
The draft high level personas were validated by comparing the activities and needs participants identified through the workshop and survey with expectations. In many cases the possible high-level persona did not match reality once the breadth of the individual's role was understood in detail.
Persona validation
An initial observation was the surprising number of participants that reported they undertook strategic work daily within their role. Initially these individuals had been mapped to a Leader/Strategist persona, however it became clear that in general they did not have the required budget or full strategic oversight of an organisation to actively bring about the changes required. We identified the need for an additional persona who is more data-savvy than the Leader/Strategist, but with the scope only to deliver change within their area of the organisation. We have introduced the concept of a Data Transformation Lead to occupy this new persona.
Since the scope of these personas is limited to those that will be engaging with the Data Transformation Framework, it was felt that it was not relevant to include Citizens. Although they will be end users of public sector data products, they should not be included within the framework. More important was to call out the vast majority of public sector workers who are either not yet engaged with data or at the start of their data journey. Therefore, we have introduced the Data Novice persona, to represent these users.
We had originally made a distinction between technical data users, whom we assumed worked with data significantly, but it was not the main aspect of their role, and those we had called analysts, whom the role was specifically focused on data. There was an assumption that the analysts had undertaken more formalised training than the data users, however the survey results proved differently, where in most cases formalised training had not been undertaken. We therefore do not see sufficient distinction between these two personas to separate them and propose to merge together. The market researcher should also fall into this same merged persona.
In addition, the term Modeller/statistician as it is currently used within Scottish Government and the wider public sector does not align to the type of activities this persona would be expected to undertake, therefore we propose to rename this persona to be a Data Scientist. Their focus would be on developing data science solutions.
We had also originally made a distinction between data management specialists and data architects. On review the breadth of the data management specialist is sufficiently broad to include data architecture within its remit, therefore we propose to merge these personas together.
Final identified personas
The final proposed list of personas is documented below, alongside a summary of the type of their required needs and knowledge. This level of detail is not yet sufficient to bring the persona to life for users, which should follow on from this piece of work. The persona names may also require additional refinement to make sure they align to terminology used or desirable within the Public Sector.
| Final persona | Persona summary | Participant number | Summary of needs/knowledge |
|---|---|---|---|
| Data novice | A public sector worker who is just beginning to understand the potential that data can bring to their role and their organisation. They are often users of systems that capture and create data. | 6 |
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| Data consumer | A non-technical data user that consumes visual information through reports or metrics to extract insights and support decision-making, research or planning activities. | - |
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| Data analyst | A technical data user that manipulates data to create output analysis and visuals to extract insights, support decision-making, diagnose issues and answer questions. The types of data analysed can be general or specific, such as GIS data. | 9, 11, 12, 14 |
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| Data scientist | A data professional who uses modern data science techniques, such as AI and machine learning, to develop analytical solutions to problems. | - |
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| Data engineer | A data professional who uses software engineering techniques to create data pipelines that support the automation and reproducibility of data solutions | - |
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| Leader | A strategic business leader who wishes to use data to realise value for their organisation. Will be responsible for overall organisational strategic direction with access to budgets to deliver change. | - |
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| Data transformation lead | A team (or department) leader who champions the effective use of data within their area of the organisation. They may be responsible for programmes of work, or leading teams to deliver change, but without the scope or budget align activities across the entire organisation. | 2, 3, 8 |
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| Data product/service owner | A solution innovator that is developing products or services that are data rich. Although they may not be highly data literate, they understand the importance of good data management in developing a scaleable solution. | 1, 5, 15, 16 |
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| Technology specialist | A technology professional who is responsible for the creation and maintenance of tools, systems and applications used to capture and store data. They also work to ensure the integrity, confidentiality, and security of all data assets within these systems and applications. | - |
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| Data governance specialist | A worker with responsibilities for the policies and processes that ensure data can be managed as an asset within an organisation. This includes ensuring compliance across legal and regulatory frameworks. | 10 |
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| Data management specialist | A specialist responsible for one of many activities required to manage data as an asset. This includes the standards, structure, integration, quality and definition of data, including processes to manage these. | 4, 7, 13 |
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This is a total of 11 final personas. They all have a distinct set of needs and cover the breadth of activities contained within the Data Transformation Framework. However, the volume of personas was considered quite large and could perhaps become a barrier to adoption and use in the future. Therefore, it was decided to focus initial persona activities on a priority set.
The priority set identified to drive the most value was:
- Data novice
- Data analyst
- Data leader
- Data transformation lead
- Technology specialist
- Data management specialist
The priority personas have been identified from several different perspectives. They cover areas where there are largest volume individuals who with whom public sector employees would identify - data novice and data analyst. Areas where the most progress can be made to embed the Data Transformation Framework within roles - data transformation lead and data management specialist. Finally, areas where volumes are currently low, but where increased knowledge and adoption of the Data Transformation Framework can deliver significant changes in ways of working - data leader, technology specialist.
Summary and next steps
This is the first iteration in developing data personas for the DTF and as only 6 of these were validated within the workshops they should viewed as a work in progress, rather than the final set. Additional work will be required to validate all 11 personas and provide additional details to flesh out the needs, behaviours, and goals of the full set.
The next steps for the data personas project will include:
1. Additional validation of the remaining final personas: ensure that any gaps in validating the final personas are filled by specifically targeting individuals who are expected to map to these. In addition, it is important to identify any gaps, such as personas that are missing in their entirety within current public sector roles. The data scientist, data engineer and technology specialist are expected to be rare within existing job roles.
2. Build out the personas: bring all the personas to life by understanding in detail their needs, behaviours, and goals. This will involve additional validation with a wider range of public sector employees. Given the large number of personas, it is recommended to focus initially on the priority personas.
3. Map the personas to the data transformation framework: This will involve mapping the required needs and knowledge of each persona to each of the data transformation framework categories, starting with the priority set of personas. It may also be important to understand the current knowledge strengths and weaknesses of each, in order to identify suitable learning interventions.
4. Develop DTF maturity pathways: The maturity pathways will involve a prioritised set of interventions for each persona, whereby knowledge and skills can be incrementally developed over time, encouraging data improvements and reuse.
5. Develop case studies: Case studies of maturity pathways can be developed for each priority persona to demonstrate the benefits. This could involve actual individuals. However, initially it may be easier to develop these using the personas and a fictionalised case study.
6. Communicate and publish the personas: Although it is important to communicate the personas throughout this project, to ensure that they continue to be validated and demonstrate their value. It is also important to finally publish the personas for wider visibility, demonstrating the direction of travel. It will then allow the maturity pathways to be incorporated into role profiles and performance evaluation frameworks.
Whilst finalising the personas it will be important to ensure the barriers to improvement identified within the workshops are also considered. In particular, as maturity pathways are developed, these should not just include upskilling in specific tools, but also focus on best practice data use, interpretation, communication and management alongside understanding the strategic drivers, policies and initiatives.
Documenting data, in particular metadata creation and standards is another area that should start to be embedded into existing processes, not just for open data creation, but also for internally shared datasets, critical reports and metrics used for decision-making and policy.
There is already quite a focus on data management processes within the public sector, however data quality improvement, such as a data remediation capability, data quality reporting and designing systems for high-quality data capture are also areas to consider when developing the maturity pathways.
It is expected that the choice of Technology Specialist as a priority persona will highlight the need to embed these individuals more closely to the business. This will ensure systems are designed and developed for high-quality data capture, the data is designed for ease of sharing, reporting and interoperability, and the cyber and security risks are identified and more formally addressed.
All of these steps will support the aims of the data transformation framework to improve and increase public sector organisation's capability in using data.
Contact
Email: data.standards@gov.scot
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