Systematic Literature Review and Research Agenda

Although interest in big data analytics (BDA) has increased in recent years, studies on the subject in the context of financial institutions (FI) are still rare. Given this gap, the objective is to analyze how scientific research approaches BDA in the context of FI. A systematic review of the literature is carried out. The results show that the literature focuses on the themes: risk management, marketing, web and social media, technology and data analytics and consequences of BDA use. In addition to defining and identifying the themes of BDA application in FIs, this study contributes to the proposition of a framework that consolidates the research agenda and proposes directions for future studies on BDA in the context of FI, such as the use of BDA in FI, in particular, on the themes of financial risk management and marketing

An Integrated Big and Fast Data Analytics Platform for Smart Urban Transportation Management

(c) 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.[EN] Smart urban transportation management can be considered as a multifaceted big data challenge. It strongly relies on the information collected into multiple, widespread, and heterogeneous data sources as well as on the ability to extract actionable insights from them. Besides data, full stack (from platform to services and applications) Information and Communications Technology (ICT) solutions need to be specifically adopted to address smart cities challenges. Smart urban transportation management is one of the key use cases addressed in the context of the EUBra-BIGSEA (Europe-Brazil Collaboration of Big Data Scientific Research through Cloud-Centric Applications) project. This paper specifically focuses on the City Administration Dashboard, a public transport analytics application that has been developed on top of the EUBra-BIGSEA platform and used by the Municipality stakeholders of Curitiba, Brazil, to tackle urban traffic data analysis and planning challenges. The solution proposed in this paper joins together a scalable big and fast data analytics platform, a flexible and dynamic cloud infrastructure, data quality and entity matching algorithms as well as security and privacy techniques. By exploiting an interoperable programming framework based on Python Application Programming Interface (API), it allows an easy, rapid and transparent development of smart cities applications.This work was supported by the European Commission through the Cooperation Programme under EUBra-BIGSEA Horizon 2020 Grant [Este projeto e resultante da 3a Chamada Coordenada BR-UE em Tecnologias da Informacao e Comunicacao (TIC), anunciada pelo Ministerio de Ciencia, Tecnologia e Inovacao (MCTI)] under Grant 690116.Fiore, S.; Elia, D.; Pires, CE.; Mestre, DG.; Cappiello, C.; Vitali, M.; Andrade, N.... (2019). An Integrated Big and Fast Data Analytics Platform for Smart Urban Transportation Management. IEEE Access. 7:117652-117677. https://doi.org/10.1109/ACCESS.2019.2936941S117652117677

Digital customer experience management in big data-driven marketing

Digitalisation has shaped the nature of business operations, and the focus of competition has shifted towards distinct and holistic customer experiences through data analysis. The substantial amount of big data compiled today extends the organisational opportunities by thorough execution of customer experience management, as various customer experience insights can be garnered from big data to embellish organisations’ extant strategies by expanding imperative data-driven operations and customer orientation. The aspiration for the thesis was to resolve how digital customer experience management in big data-driven marketing is executed, and thus connectively how big data analytics are used in big data-driven marketing and how big data-driven marketing is used for digital customer experience management. The thesis answers the call for descriptive scientific research that provides theoretical and practical development combining customer experience management and big data-driven marketing as research objects. The study was done as qualitative research. The theoretical framework worked as a process description divided into strategical planning and operational implementation by setting a direction and making choices to implement customer experience and comprehend the success of customer experience management to learn from it. The data were collected by conducting eight semi-structured thematic interviews with a purposeful sampling of professionals from considerable business management, marketing, and technology companies. Analysis was done with qualitative thematic analysis on NVivo. According to the research findings, continual big data analytics and data-driven marketing are the underlying driving forces for customer experience management that require clearly defined objectives and actions that can be measured and monitored to gain the necessary insights with data analytics. The capabilities of customer experience management necessitate comprehensive processes at the strategic and operational level, technology through data manoeuvrability, intelligence, and interpretation, as well as people who bring a strong and supportive organisational culture by understanding the value of customer experience for business growth and that they are responsible for it within their allocated roles in the process. Further, implementing customer experience happens by piloting on a smaller scale before moving towards larger target groups and thereafter maybe even modelling the concept into continuous use. Comprehensive management must be done systematically, in a structured manner so that it can be adhered to and documented. Moreover, customer experience management requires constant learning to renew customer experience by continually developing, maintaining, and repeating operations. The results of the study altogether support extant theory and research in many aspects. Still, this study provides a deeper practical look into the customer experience with the provided detailed process description. Significantly, customer experience management cannot be compromised since it is critical to the organisation's competitiveness in the 2020s to provide incredible and customised data-driven experiences whereby big data analytics and data-driven marketing play a crucial role when attracting, converting, and advocating customers.Digitalisaatio on muokannut liiketoiminnan luonnetta kilpailun painopisteen siirtyessä kohti erottuvaa ja kokonaisvaltaista asiakaskokemusta data-analyysin myötä. Nykyään kerätyn big datan huomattava määrä laajentaa organisaatioiden mahdollisuuksia perusteelliseen asiakaskokemuksen johtamiseen, sillä big datasta voidaan kerätä erilaisia ​​asiakaskokemusta koskevia oivalluksia, joilla voidaan täydentää organisaatioiden nykyisiä strategioita laajentamalla välttämättömiä dataan perustuvia toimintoja sekä asiakaslähtöisyyttä. Opinnäytetyön tavoitteena oli selvittää, miten digitaalinen asiakaskokemuksen johtaminen big dataohjatussa markkinoinnissa toteutetaan ja siten yhdistettynä, kuinka big data-analytiikkaa käytetään big dataohjautuvassa markkinoinnissa sekä vastaavasti, kuinka big dataohjautuvaa markkinointia käytetään digitaalisen asiakaskokemuksen johtamiseen. Opinnäytetyö vastaakin tieteenalan kehotukseen kuvailevasta tieteellisestä tutkimuksesta, joka tarjoaa teoreettista ja käytännöllistä kehitystä yhdistäen asiakaskokemuksen johtamisen ja big dataohjautuvan markkinoinnin tutkimuskohteina. Tutkimus tehtiin laadullisena tutkimuksena. Teoreettinen viitekehys toimi prosessikuvauksena, joka jakautui strategiseen suunnitteluun ja operatiiviseen toteutukseen asettamalla haluttu suunta ja tekemällä valintoja asiakaskokemuksen toteuttamiseksi ja ymmärtämiseksi. Aineisto kerättiin tekemällä kahdeksan puolistrukturoitua teemahaastattelua, joihin osallistui tarkoituksenmukaisesti valikoituja ammattilaisia liikkeenjohdon, markkinoinnin ja teknologian alan yrityksistä. Analyysi tehtiin kvalitatiivisella temaattisella analyysillä NVivossa. Tutkimustulosten mukaan jatkuva big data-analytiikka ja dataohjautuva markkinointi ovat asiakaskokemuksen johtamisen kantavia tekijöitä, jotka edellyttävät selkeästi määriteltyjä, mitattavia ja seurattavia tavoitteita ja toimia, jotta tarvittavat näkemykset data-analytiikalla ovat saavutettavissa. Asiakaskokemuksen johtamisen valmiudet edellyttävät kattavia prosesseja strategisella ja operatiivisella tasolla, teknologiaa datan ohjattavuuden, älykkyyden ja tulkinnan kautta sekä ihmisiä, jotka tuovat vahvan ja tukevan organisaatiokulttuurin ymmärtämällä asiakaskokemuksen arvon liiketoiminnan kasvulle ja sen, että he ovat vastuussa kokemuksesta omissa rooleissaan prosessin aikana. Lisäksi asiakaskokemuksen toteuttaminen tapahtuu pilotoimalla pienemmässä mittakaavassa ennen kuin siirrytään suurempiin kohderyhmiin ja sen jälkeen ehkä jopa mallintamalla konsepti jatkuvaan käyttöön. Kokonaisvaltainen johtaminen on myös tehtävä järjestelmällisesti ja jäsennellysti, jotta sitä voidaan noudattaa ja dokumentoida jatkoa varten. Asiakaskokemuksen johtaminen edellyttääkin jatkuvaa oppimista asiakaskokemuksen uudistamiseksi kehittämällä, ylläpitämällä ja toistamalla toimintoja jatkuvasti. Tutkimuksen tulokset tukevat kaiken kaikkiaan olemassa olevaa teoriaa ja tutkimusta monilta osin, mutta ennen kaikkea tämä tutkimus tarjoaa yksityiskohtaisella prosessikuvauksella syvemmän käytännön katsauksen asiakaskokemukseen. Asiakaskokemuksen johtamisessa ei voida tinkiä, sillä organisaation kilpailukyvyn kannalta 2020-luvulla on ratkaisevan tärkeää tarjota uskomattomia ja räätälöityjä dataohjautuvia kokemuksia, joissa big data-analytiikka ja dataohjautuva markkinointi ovat ratkaisevassa asemassa asiakkaiden houkuttelemisessa, käännyttämisessä ja kannattamisessa

A distributed workload-aware approach to partitioning geospatial big data for cybergis analytics

Numerous applications and scientific domains have contributed to tremendous growth of geospatial data during the past several decades. To resolve the volume and velocity of such big data, distributed system approaches have been extensively studied to partition data for scalable analytics and associated applications. However, previous work on partitioning large geospatial data focuses on bulk-ingestion and static partitioning, hence is unable to handle dynamic variability in both data and computation that are particularly common for streaming data. To eliminate this limitation, this thesis holistically addresses computational intensity and dynamic data workload to achieve optimal data partitioning for scalable geospatial applications. Specifically, novel data partitioning algorithms have been developed to support scalable geospatial and temporal data management with new data models designed to represent dynamic data workload. Optimal partitions are realized by formulating a fine-grain spatial optimization problem that is solved using an evolutionary algorithm with spatially explicit operations. As an overarching approach to integrating the algorithms, data models and spatial optimization problem solving, GeoBalance is established as a workload-aware framework for supporting scalable cyberGIS (i.e. geographic information science and systems based on advanced cyberinfrastructure) analytics

Applied business analytics approach to IT projects – Methodological framework

The design and implementation of a big data project differs from a typical business intelligence project that might be presented concurrently within the same organization. A big data initiative typically triggers a large scale IT project that is expected to deliver the desired outcomes. The industry has identified two major methodologies for running a data centric project, in particular SEMMA (Sample, Explore, Modify, Model and Assess) and CRISP-DM (Cross Industry Standard Process for Data Mining). More general, the professional organizations PMI (Project Management Institute) and IIBA (International Institute of Business Analysis) have defined their methods for project management and business analysis based on the best current industry practices. However, big data projects place new challenges that are not considered by the existing methodologies. The building of end-to-end big data analytical solution for optimization of the supply chain, pricing and promotion, product launch, shop potential and customer value is facing both business and technical challenges. The most common business challenges are unclear and/or poorly defined business cases; irrelevant data; poor data quality; overlooked data granularity; improper contextualization of data; unprepared or bad prepared data; non-meaningful results; lack of skill set. Some of the technical challenges are related to lag of resources and technology limitations; availability of data sources; storage difficulties; security issues; performance problems; little flexibility; and ineffective DevOps. This paper discusses an applied business analytics approach to IT projects and addresses the above-described aspects. The authors present their work on research and development of new methodological framework and analytical instruments applicable in both business endeavors, and educational initiatives, targeting big data. The proposed framework is based on proprietary methodology and advanced analytics tools. It is focused on the development and the implementation of practical solutions for project managers, business analysts, IT practitioners and Business/Data Analytics students. Under discussion are also the necessary skills and knowledge for the successful big data business analyst, and some of the main organizational and operational aspects of the big data projects, including the continuous model deployment

Unlocking the potential of big data to support tactical performance analysis in professional soccer:A systematic review

In professional soccer, increasing amounts of data are collected that harness great potential when it comes to analysing tactical behaviour. Unlocking this potential is difficult as big data challenges the data management and analytics methods commonly employed in sports. By joining forces with computer science, solutions to these challenges could be achieved, helping sports science to find new insights, as is happening in other scientific domains. We aim to bring multiple domains together in the context of analysing tactical behaviour in soccer using position tracking data. A systematic literature search for studies employing position tracking data to study tactical behaviour in soccer was conducted in seven electronic databases, resulting in 2338 identified studies and finally the inclusion of 73 papers. Each domain clearly contributes to the analysis of tactical behaviour, albeit in - sometimes radically - different ways. Accordingly, we present a multidisciplinary framework where each domain's contributions to feature construction, modelling and interpretation can be situated. We discuss a set of key challenges concerning the data analytics process, specifically feature construction, spatial and temporal aggregation. Moreover, we discuss how these challenges could be resolved through multidisciplinary collaboration, which is pivotal in unlocking the potential of position tracking data in sports analytics.Highlights Over the recent years, there has been a considerable growth in studies on tactical behaviour using position tracking data, especially in the domains of sports science and computer science. Yet both domains have contributed distinctly different studies, with the first being more focused on developing theories and practical implications, and the latter more on developing techniques.Considerable opportunities exist for collaboration between sports science and computer science in the study of tactics in soccer, especially when using position tracking data.Collaborations between the domains of sports science and computer science benefit from a stronger dialogue yielding a cyclical collaboration.We have proposed a framework that could serve as the foundation for the combination of sports science and computer science expertise in tactical analysis in soccer

A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures

Scientific problems that depend on processing large amounts of data require overcoming challenges in multiple areas: managing large-scale data distribution, co-placement and scheduling of data with compute resources, and storing and transferring large volumes of data. We analyze the ecosystems of the two prominent paradigms for data-intensive applications, hereafter referred to as the high-performance computing and the Apache-Hadoop paradigm. We propose a basis, common terminology and functional factors upon which to analyze the two approaches of both paradigms. We discuss the concept of "Big Data Ogres" and their facets as means of understanding and characterizing the most common application workloads found across the two paradigms. We then discuss the salient features of the two paradigms, and compare and contrast the two approaches. Specifically, we examine common implementation/approaches of these paradigms, shed light upon the reasons for their current "architecture" and discuss some typical workloads that utilize them. In spite of the significant software distinctions, we believe there is architectural similarity. We discuss the potential integration of different implementations, across the different levels and components. Our comparison progresses from a fully qualitative examination of the two paradigms, to a semi-quantitative methodology. We use a simple and broadly used Ogre (K-means clustering), characterize its performance on a range of representative platforms, covering several implementations from both paradigms. Our experiments provide an insight into the relative strengths of the two paradigms. We propose that the set of Ogres will serve as a benchmark to evaluate the two paradigms along different dimensions.Comment: 8 pages, 2 figure