BEST PRACTICES: Smart City & Initiative Best Practices service as the part of SDP’s Management Consulting & Government Advisory service offering for Public, Private and Social sector clients.
Product/ Service Delivery Duration:
Min 1-2 months depending upon Size of offering required and Scope of Work.
Ideal Client Type:
Public, Private and Social sector clients: International Agencies, National Governments-Ministries; Local Governments-Municipalities, Development Authorities, Smart City SPVs/ offices, Private Companies.
What is in the package of Product/Services (Deliverables)?
- Smart City & Initiatives Best Practices Report.
Product Offering/SoW Overview:
Establish best practices, which will enable project developers and cities to learn and replicate lessons can be shared and smart city best practices defined and researched, taking into account various smart city ‘deployment models’.
Categorize identification of the best practices on the basis of Smart City Initiatives, Geographical area, Demonstration area, Population in the area, Total investment, Funding, Final energy savings, CO2 reduction etc.
Identify barriers and point out lessons learned, with the purpose of finding better solutions for technology implementation and policy development
Provide recommendations to policymakers on support and the policy actions needed to address market gaps.
Using a narrative framework that focuses on the data-driven smart city as a real–world problem and provides essential facts about it, including relevant background information.
Introducing the reader to key concepts, technologies, practices, and strategies relevant to the problem under investigation.
Explaining the actual solutions in terms of plans, the processes of implementing them, and the expected outcomes.
Offering analysis and evaluation of the chosen solutions and related issues, including strengths, weaknesses, tradeoffs, and lessons learned.
Descriptive case study as a basis of Backcasting
Selection criteria, unit of analysis, and data collection and analysis methods
Review of city data (i.e., plans, programs, project descriptions, policy documents, and other secondary data sources) and the scientific literature that is related to the role of data-driven technologies in advancing sustainability.
Pattern recognition (searching for themes) entails the ability to see patterns in seemingly random information. The aim is to note major patterns within the result of the first step. This second step looks for similarities within the sample and codes the results by concepts and themes. Coding involves identifying passages of text that are linked by a common theme, allowing to index the text into categories and thus establish a framework of thematic ideas about it.
Reviewing themes is about combining, separating, refining, or discarding initial themes in line with the aim of this study and thus the backcasting study.
Producing the report involves transforming the analysis into an interpretable piece of writing by using vivid and compelling data extracts that relate to the themes, research aim, and literature associated with this study.
The outcomes as numerous themes that are associated with the emerging data-driven approach to smart urbanism.
A comprehensive understanding of the content of the documents and scientific literature and to be familiarized with all aspects of the data.
The avoidance of silos between various government departments.
The accessibility, within the earlier mentioned transparency context and the scope of the various relationships, of ICT and IoT infrastructure to users inside and outside of government.
Scalability is another important smart city best practice.
The making of a smart city: policy recommendations for local, national and decision-makers will reach out to decision-makers at local, regional, national and other levels to advise them on analysis drawn from the data acquired through smart city projects.
The making of a smart city: best practices across the region will present projects with the aim of profiling the best techniques and role models.
The making of a smart city: replication and scale-up of innovation will present how key technologies and innovative solutions can be replicated and the steps necessary to achieve this.
Engage early and often, respond more quickly to innovations in your sector.
Make decisions in the open, Establish smart governance
Document public privacy and impact assessments
Develop robust open data practice.
Additional Free Offerings:
- The system boundary is undefined or inconsistent.
- The use cases are written from the system's (not the actors') point of view.
- The actor names are inconsistent.
- The actor-to-use case relationships resemble a spider's web
- The use case specifications are too long.
- The use case specifications are confusing.
- The use case doesn't correctly describe functional entitlement.
- The customer doesn't understand the use cases
- The use cases are never finished.
- The functional nature of use cases naturally leads to the functional decomposition of a system in terms of concrete and abstract use cases that are related by extends and uses associations which further scatters the features of the objects and classes among the individual use cases.
- The use case model and the object model belong to different paradigms (i.e., functional and object-oriented) and therefore use different concepts, terminology, techniques, and notations. The simple structure of the use case model does not clearly map to the network structure of the object model with its collaborating objects and classes.
- Too many use cases that can produce an essentially infinite number of usage scenarios, especially with today's graphical user interfaces and event driven systems.
- The use case modeling typically does not yet apply all of the traditional techniques that are useful for analyzing and designing functional abstractions.
- The use cases ignore the encapsulation of attributes and operations into objects, issues of state modeling that clearly impact the applicability.
- The lack of formality in the definitions of the terms use case, actor, extends, and uses.
- The archetypical subsystem architecture of use cases that can result from blindly using use cases
- Use cases are defined in terms of interactions between one or more actors and the system to be developed.
- Basing increments on functional use cases threatens to cause the same problems with basing builds on major system functions.