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decision-making, data needs to be integrated. Unfortunately decades of delayed, over-budget, and failed IT systems integration projects across all sectors reinforces a perception that integration is complex, expensive and is likely to come with delivery risks.


A common challenge across many system integration projects has been the integration of proprietary IT systems, often from a process perspective rather than a data perspective. We have set out to integrate the systems, and then we work out how we integrate the data. To make matters worse we integrate from the perspective of the internal proprietary data structures of the systems rather than the true semantic meaning of the information the data represents. “Information” has become a second-class citizen to “Technology” and has been constrained by the underlying technologies used to manage data.


Paradigm shift


The confluence of the key digital trends of the moment - the Internet of Things, cloud computing, big data and mobile - combine to provide a paradigm shift for IT, and for our approach to system integration. It is a shift from information technology to digital services, and provides the opportunity to put the focus back onto information.


On the Internet of Things, we will see data migrate from private and proprietary relational database management systems that hold a representation or estimation of observed data, to a globally interconnected network of


devices. These devices will hold accurate actual observed data from sensors, as we see with the roll-out of smart meters and the growing market for smart home devices. Data will increasingly become real-time and live. Couple the Internet of Things with cloud computing technology and you gain unprecedented economies of scale processing large volumes of data and new opportunities to reinvent the IT environment that underpins our products and services.


Meanwhile, the semantic web promoted by W3C provides opportunities to represent data in a meaningful and natural manner that reflects the complexities of common language and the real-world, unlocking the constraints of the relational database management system. Distributed processing technologies such as Hadoop help to reduce the latency of processing real-time data. At the same time, analytics technology such as machine learning and cognitive computing, including IBM Watson, provide opportunities to improve the quality of decisions and make better sense of large and complex datasets, and begin to automate decisions. The role of machines in business is beginning to evolve beyond traditional physical tasks and computational tasks, to “thinking” tasks.


In addition, the proliferation of mobile user devices such as smart phones and tablet devices means that employees and consumers will expect to have instant and immediate access to relevant data, within the context of their location. This will be delivered through


Opening up data In Glasgow, data about traffic volume is gathered at road junctions through the deployment of SCOOT sensors that detect vehicles and are used to optimise traffic flow. The data has traditionally only ever been used for the purpose of traffic engineering and many stakeholders in cities have been unaware of the data that was collected. It is an example of dark data.


However, the data is now published as open data via a real-time Datax II open application programming interface (API) that is available on the Glasgow City Council website: https://data.glasgow.gov.uk/dataset/glasgow-road-traffic-events.


location-based services, whether a person is at home, travelling, in an office or another work environment.


The new era


The key to integration for whole system approaches is to adopt a “data-centric” as opposed to a “system-centric” mindset. We also need a willingness to make the outputs of systems open, with data published in an open and standard manner, where applicable. As engineers we must embrace this new era of digital technology and move away from the separate worlds of information technology and physical products. We should engineer converged physical/digital solutions that allow us to connect in the digital world as well as the physical one or vice versa. We also need to consider the value chain of products and services, and importantly, place an increased focus on value chains of data. Engineering solutions should be more transparent to help us understand the cause and effect of systems, stimulate new levels of innovation and provide opportunities for economic growth.


By doing this we can begin to lift engineering to a new level of whole system design that will address the growing crisis of energy consumption in cities, and improve the security, resiliency and sustainability of energy supply, and of cities themselves.


Dr Colin Birchenall is Digital Transformation Manager at Glasgow City Council.


Already the data has been used to develop innovative real-time smart phone apps. The data has also correlated with a range of other previously disparate city datasets to create models of how busy the city is throughout the year and to provide forecasts for how busy the city could be in the future. In the same way the weather forecast is valuable to organisations and individuals for planning purposes, a forecast of ‘city centre busyness’ has the potential to be used by a range of stakeholders such as marketers, retailers, transportation planners, public safety teams, street cleansing teams, and could even be used to project future energy demand.


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