1.2 Target groups and use cases for the utilisation of data

Investing in the City’s data capabilities produces numerous benefits, such as contributing to an effective and higher quality knowledge base for decision-making and management. Over the long term, the cost savings resulting from more efficient operations far outweigh the costs of improving data capabilities.

Data provides more value the more extensively it is utilised. On the other hand, data that is collected at great effort but is not interoperable and remains unused is the most expensive kind of data. Because of this, it is important for the City that the data produced by the City’s divisions and ecosystem is utilised as close to real time, extensively and effectively as possible in the City’s processes and service production. To this end, the City should strive for the continuous and scaled utilisation of data in reporting and analytics. In order to derive the maximum benefit from the data resources that it produces, the City will also aim to increase the utilisation of advanced analytics, such as machine learning.

The utilisation of data must produce value for an extensive range of target groups. Below are some examples of how different target groups can benefit from the utilisation of data.

The City’s customers: Improving the customer experience

One of the strategic goals of the City’s Digitalisation Programme is that customers are to be served proactively and in a targeted manner. One example of this kind of proactivity is the City’s ongoing pilot project involving the offering of pre-primary education places, in which instead of having to apply for the service, the customer is automatically offered a place in pre-school education for their child in their local area when their child reaches the age for pre-primary education.

Companies in the private sector make extensive use of analytics methods to target their services to individual customers. A city can likewise utilise data to better target its services, within certain limits. Mos importantly, it should be noted that in many cases, this type of targeting is based on the customer’s explicit consent and preference to receive recommendations and suggestions. Examples of this type of targeting include providing library users with recommendations on interesting books based on their borrowing history, or providing a student studying Italian with information on cooking classes focusing on Italian cuisine.

One example of how the customer experience can be improved is a project being carried out by the Education Division involving the development of learning analytics applications. The aim of the project is to provide new pedagogic methods and tools for personalising learning, monitoring the progress of studies, automatic steering and feedback and pedagogic management, among other things. Learning analytics can also help identify potential learning problems at an early stage, thus reducing school drop-out rates and social exclusion.

The individual targeting of services can also be utilised in more challenging contexts, such as using analytical models to identify and offer specific services to individuals at risk of social exclusion. When it comes to this type of profiling and automated decision-making, it becomes especially important to also address data security issues.

Managing the City and its divisions: Data-driven decision-making

The Data Strategy recommends that the City’s data-driven decision-making should be based on situation pictures of the City’s functions and phenomena that are as close to real-time as possible. In practice, a situation picture can encompass awareness of what kind of patients visit Helsinki’s health stations at each time of day, month or year; how many customers there are who use both library and theatre services; or how roadworks affect traffic flow, for example.

These situation pictures are presented to the City’s decision-makers (both executive and operative management) in the form of constantly updated visualisations and reports that clearly show the development of key indicators and forecast future trends. This information can be utilised in budgeting, the prioritisation of tasks, the impact assessment of measures and resource allocation, among other areas.

One example of how data-driven decision-making is already being carried out in the area of urban development is the utilisation of the three-dimensional semantic model of Helsinki and associated realistic visualisation that the City has built. This city model makes it possible to analyse and simulate different alternatives (such as wind, energy and traffic models), thus helping decision-makers understand how different measures would affect the operation of the City in practice. Promising examples of the model’s utilisation include:

1. The promotion of the Carbon-neutral Helsinki 2035 objective with the geothermal energy potential service and a heating energy consumption forecast.
2. The presentation of planning projects and information material with the city model.
3. The presentation of service map material with the city model.
4. Wind, rain and runoff water analyses and the simulation of sea level fluctuations.

The City’s service-producing personnel: Production optimisation

The City operates numerous services and processes, which also generate the majority of the City’s costs. Optimising service production can thus potentially generate significant cost-savings, improve service quality and reduce the workload of City employees.

All of the City’s functions utilise data to produce services. Because of this, it is essential for the data in operative systems to be of high quality: up to date, accurate, internally consistent, human- and machine-readable and interoperable with other data. In addition to this, data should also be easily accessible and updatable. Ultimately, the aim is to for City employees to be able to use the interfaces and APIs that support processes to simultaneously process all the different types of data required in their work without having to waste time looking for and assembling operative information from multiple systems. This aim applies equally to the data produced by the City’s internal operators and the data produced by external parties (such as the Digital and Population Services Agency’s Population Information System data).

In addition to facilitating the daily operation of services and processes, data will also be utilised to optimise and develop the City’s services and process. Instead of being carried out on operative systems, this will involve importing data via APIs to the data platform where the different types of data produced by processes can be examined en masse. This will allow the optimisation and development to be based on the use and combination of data over long periods of time from as many different sources as possible instead of focusing on the data of individual customers.

A good example of this is the optimisation of the City’s energy-efficiency, which facilitates the accomplishment of the City Strategy’s key climate objective of making Helsinki carbon-neutral by 2035. To become carbon-neutral, the City will need to reduce its greenhouse gas emissions by at least 80% compared to the 1990 level. Key means of reducing greenhouse gas emissions include developing sustainable transportation and improving the energy-efficiency of buildings. This, in turn, requires optimising transportation and energy consumption, which is largely dependent on the collection and analysis of data at different levels.

For example, having access to real-time and accurate data on energy consumption will allow Helsinki-based companies, the City and the state alike to reduce their energy consumption. With this type of data, organisations will also be able to more effectively target their repair investments and steer consumption.

In a similar vein, City residents can also influence the energy consumption of their own households, but without access to data and analysed information, optimising energy consumption is very difficult for individual consumers. To help consumers make informed choices, they can be offered productised analyses of the energy efficiency of their residential building’s heating system or the electrical appliances that they use, for example. One example of this type of service is Helen’s Kiinteistövahti service, which makes it possible to monitor energy consumption at the building level.