Integration

Traditional cities are being transformed into “digital cities” in their evolution to become smart cities.  The digital city is enabling citizens to share data, knowledge and information in innovative ways using advanced information and communications technologies (ICT) infrastructure.  In addition to this, the wide array of sensors deployed to support better city management are generating large volumes of data related to public safety, traffic management, energy management and environmental pollution.  Most utilities, for example, are now comfortable deploying millions of smart meters and the Advanced Metering Infrastructure (AMI) is becoming quite robust now.  Gas, electric and water utilities, which are an integral part of a city infrastructure, now want to take advantage of this digitized infrastructure and utilize the data from these millions of data points to generate actionable insights. The other issues for utilities are how to enhance grid visibility and control and enable reduction in outage restoration time.  From better billing to fault detection, including pipeline leak detection that’s a major issue for water utilities, as well as, renewable energy integration to outage management, electric and water utilities are looking at new ways to improve their operations, while seamlessly integrating multiple data sets, like weather data, GIS data, and also data on overall consumption behaviors, consumer needs, etc.  Today, both utilities and the cities they serve are also thinking about setting up two-way communication with customers via Facebook APIs.  Together, these diverse and disparate data sources are continuously generating large volumes of both structured and unstructured data.  This is where analytics comes into play to make it useful for generating actionable insights to help city managers have better visibility of city operations and make operations more efficient via data-driven decision making, while improving customer service.  Predictive analytics helps city operations managers to monitor performance levels and anomalies to disseminate information to field maintenance crew in a timely manner, which has significant cost savings both in terms of man-hours and also truck rolls for fixing problems before they result in a major, system-wide, outage situation.

The advances in cognitive computing and innovations in cognitive computing technologies like IBM Watson ^TM  have added a new dimension to complement traditional analytics, since analytics systems are now able to learn and build contextual knowledge from structured and unstructured information and correlating it with other known data sets, while interacting more naturally with humans by understanding natural languages.  This has helped not only to predict outcomes and use that insight to influence actions, but also optimize operational workflows to deliver context-sensitive information and generate actionable insights quicker to city managers.  Today, city managers are able to continuously leverage feedback not only from a wide array of sensors and connected devices, but also systems that support connected operations to transform citizen experiences  for realizing the vision of smart cities.  Contextual information is also vital for providing predictive insights to future outcomes and is critical particularly when a broader context of data – encompassing sensor data and traditional transactional data – is involved.  In the case of smart energy management, for example, analytics helps to predict peak loads and to identify high energy users and efficient energy users, while providing predictive insights to maintenance issues that have an impact on capital expenditure-related decisions.  It is this predictive capability of analytics that enables insightful data-driven decisions.

This capability augmented by the emergence of intelligent edge gateways that enable collecting and analyzing data close to the source endpoints, has now enabled cities to achieve greater efficiency and scale using cloud-enabled platforms.  With a growing set of APIs for interfacing with multiple data sources - including open data sources – it’s possible for service providers to deliver rich analytics-as-a-service to cities.  In a cloud-based environment with intelligent edge gateways, data that that needs deeper analysis can be moved to the cloud for further analysis using high performance computing solutions.  New data sources can also be dynamically captured and business rules and processes can be dynamically updated based on what’s happening at that moment to generate near real-time actionable insights that’s current on an as-needed basis.  City managers can now get the information they need on a rich analytics-driven dashboard to view vital information based on data aggregated from different city systems like energy management, water resources management and traffic management that’s analyzed and visually presented for viewing city functions and operations holistically.

Utilizing the cloud to deliver analytics for enabling data-driven decision making has strategic advantages to cities that are shifting costs from capital expenditure-related costs to operating expense-related costs.  With a cloud-based analytics strategy, cities can also evolve into smarter cities faster, while ensuring scalability and reliability of city operations and services.

K.S.Ram Mohan is a Systems Architect within Verizon’s Products & New Business Innovation organization.  He is also the Principal Architect supporting the Public Sector vertical on Smart Cities initiatives and Energy/Utilities vertical on IoT initiatives.