Chapter 7. Competences And Organisation

The building of data capabilities is based on capable people. The City’s best concentration of analytics experts is currently found in the Urban Research and Statistics Unit, which employs researchers proficient in the use of statistical methods and tools, among others. In addition to this, the division-specific administrators of the HETA environment include experts in reporting and a technology architect, who is responsible for the technical platform. Meanwhile the City’s 3D team includes professionals skilled in the building of simulation and visualisation models. Other units, such as the service register platform, also include experts who carry out reporting.

However, the City has few experts in hard data science, such as data scientists specialising in advanced analytics, data mining and machine learning methods. Moreover, the administration of the City’s information resources is primarily handled by external service partners, due to which the City has a need for data engineers of its own specialising in the administration and development of cloud, analytics and data lake environments.

The business administration departments of the City’s divisions do not have enough data and analytics expertise in relation to modern needs and opportunities. Because of this, City divisions are not always able to translate their own objectives into data and data science requirements or understand how data could be effectively utilised to achieve said objectives. One solution to this would be to employ ‘interpreters’ specialising in data use cases, i.e. data strategists. In addition to this, the City will organise training courses exploring the possibilities of data, analytics and AI for business and other personnel.

Figure 11 presents four key competence areas for the efficient utilisation of data. The first is the services and processes of City divisions, which steer the objectives of data utilisation. The second is data science and BI, which derive value out of data using reporting and analytics (such as machine learning) methods. The third is information resource management, which encompasses all the data in operative systems and data platforms as well as data quality, integrations, data modelling, standards, metadata and other related areas. The fourth is technical infrastructure, which is related to the technical development and administration of data warehouses and data lakes.


Figure 11. The key competence areas of the Data Strategy

Data experts can be categorised in many ways, but the roles considered most critical for the City are listed in the table below based on the competence areas described above (more detailed role descriptions are provided in an appendix):



Competence area

Data and AI strategist

Translates business (City divisions’) requirements into data and AI language, serves as a project manager in implementation projects and ensures that results are put to use in business operations.

Services and processes

Data scientist

Is proficient in machine learning, statistics and programming; builds analytical models and algorithms; effectively communicates about results.

Data science and BI

Data architect

Defines the City’s/divisions’ data architecture; database solutions and components; database descriptions; master data management and data standards.

Information resource management

Data engineer

Plans and builds the infrastructure needed for the transfer of data (cloud environments, data pipelines), which includes boards, databases, automation and scalability, for example.

Information resource management

City-wide data and analytics team

Helsinki needs to increase its data and analytics know-how and carry out related recruitment at both City and division levels. In order for Helsinki to take the next step in the utilisation of advanced analytics, the Digitalisation Unit needs a separate data and analytics team or other administrative solutions that will ensure the realisation of the measures highlighted in the Data Strategy. This team should include the resources critical for the building of the data platform and recruit a sufficient number of data scientists and data engineers. The team must have sufficiently extensive know-how to be able to independently develop the City’s common data platform, support City divisions in the development of data-intensive services and help make the City’s decision-making more data-driven.