An exploratory research on urban data platform

The other day I was sharing my enthusiasm with university friends regarding smart city project that I am going to be part of. While the conversation was interesting, one thing was certain: people are not sure about the term “smart city” and its utility or usability. What does it really mean then? Here’s a short answer. To foster city development and address negative effects of urbanization, cities are changing traditional operation to intelligent operation using technology and digitalization. In simple words, you do things smart, not by putting hours of hard work.

Now, one might ask what are the key factors that would make a city smart? The answer will vary, because each municipal city has its own approach, vision and strategy. Despite that, one key success factor is government data that are and can be made available to public. Below depiction will clear understanding of smart city ecosystem in which data from government is observed.

Smart city ecosystem adopted from "smart cities: an overview of the technology trends driving smart city (Lea, 2017)

To do so, the city administration needs to build a portal/website/ platform of data, in which city can fetch and store different datasets so that anyone can use for making new data-based product, service or research etc. Let’s call this “urban data platform” for the sake of simplicity. However, this is where I come in.

As a part of my master’s thesis work, I have planned to study the operational method of  “urban data platforms”, meaning that how the platform as a stand-alone organization should work, how it should facilitate data management, what value should it offer to people who are seeking to innovate with data within city, and how a circulation of data economy can be established. The aim of the project was therefore to see:

• if supply and demand mechanisms can be applied or have been observed into the operation of an urban data platform,
• if supply chain mechanism can create circular economy of data
• and whether urban data platform is similar to multisided platform in terms of business model.

In my study, I have only chosen some modern supply chain practices into urban data platform operation. My initial proposed practices were:

1. Having planning, collaboration and ready a framework for data guideline with key stakeholders at the beginning stage of platform operation.
2. An open knowledge sharing environment among data suppliers, urban data platform operator and data consumers for effective and efficient supply and demand management of data.
3. Outsourcing procurement task such as data procurement, data cleaning, data verification and veracity, data analytical services.
4. Data assembly and manufacturing of datasets into service
5. Customer service for pre and post transaction service.
6. Applying reverse logistics.

To implement chosen practice and reviewed materials from academia, I have built a concept urban data platform operational model (see figure 2). Later to verify the validity of my concept model, I examined three data platforms which are organized by municipality, these are city of Turku in Finland, Amsterdam in Netherlands and Copenhagen city in Denmark.

Proposed conceptual model of urban data platform operational model and ecosystem.

The findings of the research were fascinating. Instead of discussing the insights, I am going to shortly present my suggestions towards public and private practitioners (urban data management, multisided platform operators) the findings from the result. Just to refresh memory, we are talking about municipal’s data platform initiative. Here are my suggestions based on the findings and insights:

Procure both data and service to create data platform ecosystem:

• If your data platform is a closed system, then you can procure data (datasets) from internal secured and trusted internal stakeholders (for instance your trusted partners/source or employees).
• For open and experienced urban data platforms, managers can procure data from external stakeholders (literally anyone as in citizens, private businesses, researchers, NGO and so on).
• However, sourced data must be cleaned and verified so that value of the data given to consumers are consistent.

At this point one thing I should mention that an experienced platform is likely to be open system, and vice-versa. Also, manager must remember that it’s the duty of the urban data platform operators to check data veracity according to their data administrative model (there has to be one), and they should take liability of published misinformed data. Data platforms may or may not want to take responsibility for mistakes in privately published data. However, at the end of the day, data is published in their platform and to comply with standards, the data privacy, security and veracity maintenance should be initiated from their end.

Build a “model of collaborative planning & performance and data guidance”

In all smart multisided platform business, more specifically in peer-to-peer platform business such as Uber, Airbnb etc. collaborate to share resources to minimize cost and optimize operation is observed. Importance of collaboration exists in modern supply chain practice as well, as researchers suggest value adding partnership (VAP) to practitioners where multiple stakeholders will perform a portion of a complete task in order to achieve a singular goal. Since supply and demand of data is seen in urban data platform, we can assume that to identify data requirement, managers need to work closely with both data supplier (public and private) and data consumers (public and private). Hence creating a connected model with key internal and external stakeholders (both data supplier and consumer) inside data platform operation would expedite service response and transparency in business processes.

In addition, as exploiting consumer data is a sensitive issue, a data administrative model that addresses liability, compliance, security, privacy, governance policy, transparency and platform scalability should be formed with the discussion with key stakeholders.

Create an open knowledge sharing environment among key stakeholders

If information exchange for optimized operation can be achieved in supply chain of tangible goods, why not applying it also in digital world. We have seen Dells approach to information sharing, where they share customer order information directly to suppliers; Thereafter lead time and inventory carrying cost for supplier and Dell is reduced and customer service of Dell is improved. Similarly, Toyota shares their internal operation to their trusted partners so that competitive advantage can be achieved. Therefore, practitioners are suggested to facilitate a connected network through which information will be reciprocated between data consumers, suppliers and any internal intermediary.

Outsource data cleansing and analytical service and perform core task

An ideal set up for multisided urban data platform managers would be to outsource tasks that are costly and only perform tasks that are core to the platform. Platform owners can outsource the data cleaning task, while data verification can still be part of the platforms responsibilities to keep data truthful and useful. Providing customized service with data can also be done with third party analytical company, who can visualize and interpret the given dataset for data consumers. However, to retain the ownership of the platform, managers can complete basic administrative and strategic tasks of the urban data platform. For example, Uber, as a multisided platform, sets the price for ride sharing, performance indicators of service and guidelines and imposes the technical standards on the basis of which two user groups interact, share resource and achieve either parties goal. This means platform owners plays a central role and control actors’ engagement in the ecosystem. Outsourcing will allow businesses to enter urban data platform ecosystem.

Don’t forget customer service

Customer service is fundamental not only for satisfying customers but also to gather customer feedback and demand to improve performance upon that. Customer service in urban data platform is mostly during data sought period and after sales customer service. Since the context is in digital age, customer service will be in place by enforcing an open knowledge sharing that would reduce lead time. Besides that, a customer service can be achieved by following the “perfect order” guideline from Edward Marien (Blanchard, 2010).

My concept model using supply and demand analogy can be a starting point for urban data platform operation. However, it needs further investigation and experimentation to turn it into use case. In the following blog, I would present the key findings of business model in urban data platform from my conducted research. It will also shed light the missing points in current data platform practice and how we can commence a circular economy of data.

Ref:

• Rodger Lea, 2017. Smart Cities: An Overview of the Technology Trends Driving Smart Cities
• Blanchard D. 2010. Supply Chain Management best practices: best practices.