Insights on Smart Cities: Interview with Ruthbea Yesner

A. Leaders of new initiatives within cities are change agents. They must facilitate a complex process of digital transformation, which involves many factors that support technology adoption and effective data use. Data itself is not a competitive differentiator, it is what a city, or supplier to a city, does with the data that creates that differentiation. One of the first things Smart City leaders must do is think of data as a strategic asset, and begin to make the necessary changes in culture, processes, governance and policies, training and reskilling workers, and testing new technologies.

IDC’s Smart City maturity model identifies specific actions that Smart City leaders can do to tie data to outcomes:

• Develop a Smart City strategic action plan. City leaders should develop a Smart City strategy that clearly articulates priority initiatives, the desired outcomes of projects within these initiatives, how project success will be measured, and what resources are needed to move projects forward. A key element of this strategy is that success is measured and KPIs are identified as these will quickly lead to thinking about data use.
• Work on a culture of data sharing and transparency. This can be one of the most difficult adjustments for city employees who may have concerns about potential negative responses to being more transparent, and opening their departments up to criticism from the community. Creating an Open Data policy is a critical way to address these concerns and community concerns in terms of how data is used, what data is collected, what can be shared and what is kept private, who has access to what levels and types of data, and how personally identifiable information is protected. The process of developing the policy is also a tool to bring groups together to discuss data governance.
• Begin to think about data product and services. This applies to leaders within cities and the suppliers of services to cities. Data services are not just about data monetization, though that may be an end goal. To create data-based services, careful thought needs to be given to analytics and information management, including platforms that can intermix data, and automating data processes to increase speed, accuracy, and data integrity.
• Determine which new skills will be needed. A focus on data services and data-based innovation may require workers with new skills and new attitudes. IDC suggests training to help existing workers learn new skills in needed areas as opposed to first thinking of outside hires. Some cities are hiring a Chief Data Officer to manage all aspects of data as a strategic asset.
• Pay attention to data ownership, governance and policy. Data as an asset requires that data access and ownership becomes embedded into the procurement and supplier selection process. Data management policies should be incorporated into contracts, terms and conditions, and even into RFPs.

IDC’s benchmark of U.S. cities’ use of data shows that most cities are in the early to middle stages of maturity, with less than half having an Open Data policy, and close to 40% with very limited data sharing across departments. In terms of using data more effectively with analytic tools, most cities have moved beyond relying solely on data specialists and there is solid adoption of data blending to analyze data from multiple sources and using tools open to non-specialists via visual interfaces. However, analytics is still not embedded consistently in every day workflows and data tools are not tied to KPIs in any automated way for strategy execution.

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A. As digitization increases and physical systems become digital assets, it is fundamental to connect disparate systems and siloes to gain better insight, operational efficiency, and create the new services that businesses, tourists, and communities are expecting.

Data plays a key role in Smart Cities because it provides access to information that may be new (such as new information from transit providers or street light infrastructure), timelier (i.e. sent automatically in real-time), more accurate and granular (collected from sensors as opposed to manually or via estimation) and in formats ready for advanced analysis and integration into business applications. This provides the foundation for using data for policy decisions, quicker decision-making and to create new services.

In cities, operational systems are interconnected and one part impacts the whole; for example, a water main break can create road congestion which can impact emergency services, work commutes and school bus logistics. If these separate systems are proactively coordinated, and supported with integrated data, the impact of an event like a water main break can be mitigated by a coordinated response across transportation, public safety and school departments, through automation and analytical tools. This coordination can improve the entire city -in effect, smart cities are trying to create IT systems and data processes that support the interconnected operations of the city itself.

A McKinsey Global Institute report, “The Internet of Things: Mapping the Value Beyond the Hype”, states that this systems interoperability represents 40% of the total value of IoT in cities. Add in the ability to use shared data to create innovative applications, and this value can be reinforced through new services, such as mobile phone apps that alert drivers to accidents before they begin their morning commute, or lights that can trigger video cameras in the event of an emergency. These examples illustrate the industrial operations and IT convergence that is becoming more important in Smart Cities and how interoperability and data-sharing platforms support this convergence.

Data interoperability and data management are paramount to extract value from data coming from many different sources and systems, including private and quasi-private entities and hundreds of legacy applications. Smart City data, coming from such a multitude of sources, must accommodate data from open source and proprietary solutions, most often relying on open APIs and open standards, where they exist, for interoperability.

This means that city operators and providers to cities must embrace open APIs and data sharing, as they provide new digital industry platforms (even if they are non-tech companies) and provide available and accessible data that can be intermixed with other datasets. Cities are looking for partners that offer these interoperable systems, and that embrace data sharing as part of their own internal transformation. In fact, IDC predicts that in 2018, cities will spend 2 times more with partners that are committed to open APIs, sharing data, and long-term relationships to deliver business outcomes.

A. Smart City platforms are really multiple platforms working together across various systems and operations to manage endpoints and connectivity; access, ingest and process data; visualize and analyze data, and provide application development tools. A data-sharing platform provides the ability to access, ingest and process data across multiple platform products provided by cataloguing, standardizing, aggregating, and publishing data sets. A data management and data-sharing platform can combine multi-source data from different city domains and service providers, including data from city databases and business applications, from legacy Machine-to-Machine (M2M) systems, from new single- or multi-domain IoT platforms, and even from other data-sharing platforms, like external open data portals or data marketplaces. And, many data-sharing platforms provide built-in data visualization and dashboarding capabilities with data access via APIs for feeding advanced analytics systems.

These data-sharing platforms also provide the data inputs for new solutions, which can be developed either in house or via an ecosystem of providers, such as vertical specialists and local suppliers. These platforms help cities and other partners to attract and nurture app.

developers, SaaS vendors, SIs, and other IT companies to build value-added and differentiated solutions on the top of the platform.

A. There are some key, high-level aspects to a data-sharing platform that are essential for its success as an interoperable data management tool that can extract maximum value from data:

• A data-sharing platform should be a cloud-based platform. Cloud-based data sharing enables improved privacy, interoperability, security and secure data sharing, scalability, and fast, agile app development and testing. A platform on which many applications can run also offers these capabilities for specialized, domain-specific applications as well as provides access to the most up-to-date technology.
• The platform should have an API-first/ API-centric design with solutions that natively support high volumes of multi-source real-time sensor data that can integrate with legacy systems, structured data, and public data sets. An API (application programming interface) is software that exposes some content and services in an application so that programs can interact and share data. Open APIs are publicly-shared and enable multiple vendors to work together to create solutions across proprietary products.
• The platform should support public and private sharing. If solutions are going to intermix data, then the governance, security and usage monitoring and management becomes more important to control access.
• The platform should support non-technical users. It should offer the ability to understand and manipulate data into information that people from many different roles can understand. This means data-sharing platforms need to have ways to transform raw data into stories using charts and graphs, non-technical texts, and even interactive formats. And, since not all data is relevant to all people, or viewed in the same context, stories can be tailored to whether someone is a government employee, a community member, a journalist or researcher, etc.
• The platform provider should have an innovative and collaborative mindset. Cities will not develop these solutions in-house; they will be provided from outside suppliers both as a cloud-platform, but also provided as new data services from infrastructure providers. More and more, non-IT companies need to provide information and services in new digital formats. For example, GE is no longer just providing street light hardware but cloud-based digital energy information. This means that city operators and suppliers need to also use an open data, or data-sharing, platform to work as part of the Smart City ecosystem, and to quickly create and scale data products for Smart Cities.

As Smart City leaders shift their thinking towards data-based services, there needs to be a corresponding shift by CIOs to think less about data characteristics like volume, velocity and variety and more about analytics and information management via data awareness, augmentation and automation. These factors create a data-sharing platform that enables data to be tied to outcomes by enacting the following:

• Universal, standardized, and timely access to data of any type as opposed to siloed, proprietary and limited access.
• Data governance and management mechanisms (including data protection)
• Moving away from the physical handling of data to automation
• Using data cleansing and augmentation to offer improved data visualization, advanced analytics, and data integrity

A. Open Data Bristol is a great example of how a city has used data sharing to improve policy-making, accountability, local innovation and address city challenges. Open Data Bristol is a data-sharing platform that offers data management, data sharing across the public and private sector, and the ability to create new services and actively foster community engagement.

The Bristol City Council has been publishing data since 2010 and is very experienced in the use of Open Data. The Council launched Open Data Bristol in June 2017 using an Opendatasoft portal. Currently, Bristol has 130 shared datasets with mapping tools and archives of data and statistics. Each dataset has an API, and there are visualization options so that users can perform basic analysis. This data is also used for the Council’s dedicated transport data platform to catalyze developers to build transport-related products, which are a priority for the community.

By localizing data to one platform, city data innovators can combine and visualize data quickly, develop project dashboards, and identify new insights. This also provides the prototype dashboard for The One City Plan which provides a single, shared action plan across the public, private, voluntary and academic sectors.

Open Data Bristol has all the characteristics of successful data-sharing initiatives:

• An articulated position on open data and data sharing, and goals to promote community engagement and provide new services via collaboration, supported by the city leadership, in this case the Bristol City Council.
• Investment in technologies to make large amounts of data available in useful formats, such as APIs.
• The understanding that residents are not going to be the primary users of the data. Volunteers, city teams, academics, and private companies will take the data and transform or blend it into useful information for consumption. This includes data from utilities, transport or other operations or outside providers.

Many cities have taken these initiatives one step further by partnering with private companies that generate relevant public service data. The ability to merge public and private data represents a significant opportunity for generating value from open data portals enabled by data-sharing platforms. Cities can enable these opportunities through a commitment to data interoperability and data sharing.

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March 27, 2018