Cluster Sofia knowledge City
Most of the start-ups/scaleups that were attracted to apply and participate in the MediaMotor Europe acceleration program are deep-tech companies. In general, such companies propose platforms including marketing ones that use data analytics and the big data accumulated in their systems to improve operations, provide better customer service, create personalized marketing campaigns based on specific customer preferences, and, ultimately, increase profitability. Businesses that utilize big data hold a potential competitive advantage over those that don’t since they’re able to make faster and more informed business decisions, provided they use the data effectively. Many of the startups/scaleups propose solutions that enable usage data analytics for ROI and customized engagement. They usually propose “information as a service” as a well-accepted business model. Thus, data analytics and big data technologies are well represented in the Media Motor Europe project, which is why this short article aims to present some of the important issues related to these technologies like the current situation, existing known platforms, key applications, standards and trends. The article is also based on a study of one of the cluster members KISMC and his partners.
Data Analytics is an approach that allows companies to analyse 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 these 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 as a translator or business generator, because it allows to explore personalized solutions to carry out the 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.
Platforms and standards
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.
The first standard on big data was published in end 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, analyse, visualize and handle large amounts of data, which cannot be analysed with traditional technologies (Iglesias. A, 2015).
The Big Data Value Association (BDVA) is defining standards of Big Data priorities and interoperability. The association has a team dedicated to this matter (Task Force 6) that 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 took place in Dublin on the side-lines of the ISO IEC JTC1 WG9 Data Reference Architecture meeting.
Currently known applications
When we refer to Data Analytics & Big Data, we can differentiate currently five known applications:
- 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.
- 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 behaviour.
- Increase in security.Such technologies are used in order to prevent attacks by locating anomalies that may occur, by analysing 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 analysing telecommunications and social network data to analyse threats and to act before the criminals.
- 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;
- Increase in data storage.Creation of new data storage structures.
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 analysed, 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 analyse without an analyst, companies such as Microsoft and Salesforce have announced such type of solutions. Programs such as Kafka and Spark that allow users 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.
There are large number of potential applications and areas of use (Mar., 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 fulfilment of the law
- Applications in Smart Cities related solutions