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Disruptive technologies for smart cities - Data analytics

Disruptive technologies for smart cities - Data analytics

Author: KISMC/CSKC team

The article is a continuation of the series of articles for disruptive technologies for smart cities we started publishing in April 2020. It is the result of the ongoing Erasmus+ project Smart technologies by design (Smart by Design) and is based on the outputs produced by the project partners GAIA & DEUSTO and ARIES T. Data Analytics is an approach that allows companies to analyze the data they generate in their activity enabling them to draw conclusions that affect their business. Better known as Big Data, companies manage this information in order to adopt strategies that will help them to improve their business turnover.

Thus, they can improve operational efficiency, customer user experience, and allows them to improve their business models. All these data generated by companies in their activity is one of the concerns they have to face today. They should evaluate the importance of this information, what information they will have to store, or even what part of all this data can sell.

Data analysis means the translation of information into opportunities for companies to take advantage of all these data (Schneider. 2017). This is why, “Data Analytics” is also called a translator or business generator, because it allows us to explore personalized solutions to carry out your projects. At present information as services is a business model that is expanding wherein increasingly more businesses are seeking to monetize the information they obtain. According to the International Statistical Institute, businesses that use information will see their productivity increased by 430 billion dollars by 2020 in contrast with those that do not use it.

Existing platforms

Services offered by platforms related to information analysis is growing along with new solutions in terms of storage capacities as well as processing. Some of the platforms that currently exist are as follows: Hadoop, Gridgain, HPCC, Storm, Spark, Hive, Kafka, Flume.

Existing standards

The first standard on big data was published at the end of 2015 by the International Telecommunication Union (ITU), hence already international rules and standards are there. ITU-T Y.3600: provides requisites, capabilities, and use cases of cloud computing-based big data (Y.BigDatareqts, 2015). Big Data when merged with Cloud Computing offers the ability to collect, store, analyze, visualize and handle large amounts of data, which cannot be analyzed with traditional technologies (Iglesias. A, 2015).

Key applications

When we refer to data analysis, we can differentiate the following 5 key applications of such technologies:

1. Explore massive data or Big Data management. Information management is assumed to be one of the biggest challenges that the companies will be facing for best decision-making, operations improvement, and risk reduction.

2. Obtain a more complete view of customers. The companies have a greater number of information sources about their customers, which they manage to provide better and more personalized services, as well as to predict customer behavior.

3. Increase in security. Such technologies are used in order to prevent attacks by locating anomalies that may occur, by analyzing patterns and threats. In this usage type, we can distinguish three applications:

    • Improve intelligence and surveillance: with continued real-time analysis to find patterns.
    • Prevention of attacks: with network traffic analysis to deal with espionage, intrusiveness, cyber-attacks
    • Prediction and prevention of cybercrime: by analyzing telecommunications and social network data to analyze threats and to act before the criminals.

4. Operations Analysis. Helps companies to make operational decisions, increasing their intelligence and efficiency. To do so, they can check the updated information with the different possible systems;

5. Increase in data storage. Creation of new data storage structures.

 Expected evolution over time

The expected evolution is that the data volumes will continue to grow due to the expected increase in the number of networked devices. The future platforms will improve the ways in which data is analyzed, while SQL will continue to be the standard, Spark is emerging as a complementary tool which will continue to grow.

New tools will be created to analyze without an analyst, companies such as Microsoft and Salesforce have announced such type of solutions. Programs such as Kafka and Spark that allow us to use these data in real-time will also continue to be developed. According to many experts, it is thought that “fast data” and “actionable data” will replace big data. It is also expected that algorithm markets will emerge. Companies will begin to buy algorithms instead of programming them and add their own information (Logicalis, 2016). Although such type of solutions already exists, it is assumed that these will grow multi-fold. On the other hand, one of the challenges data analytics platforms will face is privacy, especially since the latest regulations made by the European Commission.

Expected standards

With regard to standards, The Big Data Value Association (BDVA), is working to define standards of Big Data priorities and interoperability. The association has a team dedicated to this matter (Task Force 6) that as of today has already defined a reference model for Big Data. A workshop was held in Brussels in June 2017 to collaborate with other standardization communities to create a roadmap for the harmonization of Big Data standards. Representations from ETSI, AIOTI WG3, CEN/CENELEC, OASC, ISO/JTC1/WG9, W3C, OneM2M, Industry 4.0, European Commission, PPP based important Big Data projects among others, participated in the event. Follow-up activities are expected to take place in 2017 on the side-lines of the ISO IEC JTC1 WG9 Data Reference Architecture meeting, which will be held in Dublin.

Potential applications

Information analysis has a large number of potential applications and areas of use (Marr.B, 2016):

  • Continue working in customer segmentation
  • Optimization and understanding of business processes
  • Monitoring and optimization of business processes
  • Improve public health systems
  • Improve sport yields of citizens
  • Improvements in science and innovation
  • Optimize the performance of machinery of companies
  • Improvement in security and support for the fulfillment of the law
  • Applications in Smart Cities related solutions
  • Finance

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