Smart District Data Infrastructure (SDDI)

In other word, we can define a district as complex and open distributed systesms which are tightly interconnected to each other. The complexity is not only due to the technical aspects but also due to the diverse interests of various stakeholders in different domains. On the one hand, the system is open in that it should be extensible. This means that different partners can be part of the system in different ways. On the other hand, the system is called distributed because a number of different stakeholders (e.g. owners, operators, solution providers, citizens, and visitors), agents, communities and various data layers including sensors, analysis tools, etc. are present in it. Therefore, it is essential to break down the complexity of districts which are indeed complex distributed systems. To do so, one way it to look at such systems from different viewpoints.

Viewpoint modeling has become an effective approach for dealing with the inherent complexity of large distributed systems. There are many framework examples out to model the viewpoint for different use cases.

To define the process of SDDI we needed to structure our viewpoints according to the idea of having an open and distributed system. In the “OGC Smart Cities Spatial Information Frameworks” white paper it is recommended to follow the standard ISO 10746 “Information technology — Open Distributed Processing – Reference model (ODP-RM). The way that we adapt this reference model is explained in detail in the page "Path to SDDI".

https://www.bedrockdata.com/blog/7-data-integration-difficulties-for-todays-marketers

Successful data integration is a business’ dream: Coordinated databases, multiple software systems and various team members keeping in step, functioning seamlessly through continual access to critical information—without any hiccups. To reach that pinnacle, marketers often face a myriad of data integration difficulties. To overcome these challenges with ease, marketers must carefully analyze the required steps in the process of choosing a data-integration solution.

Challenge #1: Isolation

In most cases, applications are built and deployed in isolation, making data integration an afterthought. In an ideal world, business professionals would build and deploy business applications with data integration challenges in mind, so that they’d already be thinking about how to tackle issues like the workflows of their colleagues, as well as compliance and future technology upgrades and additions.

Challenge #2: Business needs

Even if an enterprise employs a few standardized database systems, it’s more likely that it uses multiple data products that don’t automatically work together. Aside from systems, companies have a variety of data, and a lot of it, employed through CRMs, marketing, support and finance systems.

Challenge #3: Department needs

A given department’s needs and reasons for using applications continually change, requiring the use of new applications. For instance, when the marketing or sales department needs to employ a great application that will revolutionize how they do business, it’s likely that the app will need to integrate with non-compliant systems. The process is unintentionally designed to serve data integration needs as secondary to business activities.

Challenge #4: Technological advancements

Innovative IT engineers will forever be generating new and improved products that meet mission-critical processes that no other application can serve. Unfortunately, again, integrating the data is not on top-of-mind for IT professionals. It’s a job that’s essentially the user’s problem.

Challenge #5: Data problems

There is nothing that brings data problems to the forefront like the data-integration process. The process brings to light challenges with data integration when data is incorrect, missing, uses the wrong format, incomplete, and so on. Before onboarding systems, businesses should first profile data to assess its quality—for both the data source and the environment in which it will integrate.

Challenge #6: Timing

Any data-integration system that doesn’t take into account how to deliver real-time data as well as periodic access isn’t worth its salt. For an integration solution to work, it must be able to handle real-time needs and batch updates to suit the business activities it’s designed to support.

Challenge #7: It works today…

But will it work tomorrow? Let’s say you’ve put in the long, hard hours getting your existing data-integration platform to work for your existing business systems. But what about tomorrow? As technology changes and you add new applications and management systems, will the platform work to seamlessly integrate these new data requirements? Forward-thinking companies take future needs into account when choosing a data-integration solution.

For an enterprise to use an array of critical applications, the systems must be able to effectively use data, seamlessly sourcing it from where it’s located to where it can be used. Even though data integration difficulties are—and always will be for at least the near future—a reality, the systems still greatly benefit corporations. Strategic project planning, and inclusion of critical team members, leads to a successful integration process, overcoming the challenges inherent to integrating data.

Ultimately, effective, comprehensive integration procedures leads to a unified view of highly usable data. Enterprise can achieve these goals by partnering with a data-integration provider who understands these challenges and has the foresight to generate compliant systems.