Business Open Big Data Analytics to Support Innovative Leadership Decision in Canada

This paper summarizes how social media and other technologies continue to proliferate; the shifting economic landscape will precipitate more adaptive approaches for managers attempting to understand the multidimensional virtual aspects of communication with the artificial intelligence aspect. Also, we discover the different existing support of big data analytics to make the rational business decision. The methodology is the systematization literature sources within this context and approaches for underlining approach to open big data analytics and support innovative leadership decisions in Canada

Effectiveness, Efficiency, and Ethics of Marketing Analytics

Abstract The concept of big data has influenced the marketing field in numerous ways. By having access to more information about their consumers than ever before, marketers are presented with a unique opportunity to make the marketing process more streamlined and effective than ever; however, this also creates a challenge in understanding how this targeted advertising affects the brand’s perception by consumers. This study looks at the concepts of data marketing and re-targeted ads from three aspects. First, are marketers being as effective as possible to ensure they are sending the right advertisement, to the right customer, at the right time? Second, are marketers being as efficient as possible when choosing the correct platform to reach their target customers? Third, are companies remembering the ethical components of collecting this information on consumers, and ensuring they understand when consumers feel specialized advertising becomes an invasion of their privacy? To answer these questions, I first performed secondary research in the form of a literature review. From surveying the scope of the subject, I then performed primary research by conducting in-depth interviews and a survey. The results show that there are two distinct type of consumers: one group who is accepting of these re-targeted advertisements and welcoming of the specialized marketing, and a second group who is skeptical of this form of marketing and concerned over privacy issues. Marketers must be aware of these two distinct types of consumers and ensure they are choosing their advertising methods carefully to ensure an efficient utilization of resources and to make sure they are not presenting a detriment to their brand for the consumers who do not want catered advertisements

Knowing Your Population: Privacy-Sensitive Mining of Massive Data

Location and mobility patterns of individuals are important to environmental planning, societal resilience, public health, and a host of commercial applications. Mining telecommunication traffic and transactions data for such purposes is controversial, in particular raising issues of privacy. However, our hypothesis is that privacy-sensitive uses are possible and often beneficial enough to warrant considerable research and development efforts. Our work contends that peoples behavior can yield patterns of both significant commercial, and research, value. For such purposes, methods and algorithms for mining telecommunication data to extract commonly used routes and locations, articulated through time-geographical constructs, are described in a case study within the area of transportation planning and analysis. From the outset, these were designed to balance the privacy of subscribers and the added value of mobility patterns derived from their mobile communication traffic and transactions data. Our work directly contrasts the current, commonly held notion that value can only be added to services by directly monitoring the behavior of individuals, such as in current attempts at location-based services. We position our work within relevant legal frameworks for privacy and data protection, and show that our methods comply with such requirements and also follow best-practice

Ethics in Data-Driven Marketing

In today’s digitalized world, people leave trails of data about themselves when operating online. While marketers are eagerly utilizing this data for marketing insights, many consumers have a growing fear of their personal data ending up in the wrong hands and losing their privacy. This thesis aims to understand the role of ethics in current marketing based on consumer data and to examine how data could be used ethically in marketing. This thesis met these research aims through an extensive literature review related to the issue and through empirical research. The empirical research was conducted by analyzing secondary data from a 2018 survey about the use of digital services in Europe. This analysis was combined with a content analysis of Twitter tweets about data ethics. This research produced a number of key findings: First, privacy, confidentiality, and transparency are critical issues for consumers. Still, a large number of people are unaware of their data rights or think that is not important to change e.g. privacy settings. Also, some of the people feel that changing privacy settings has no effect. Second, people want to have the power to decline the selling of their personal data to third parties and to delete or adjust their personal data. Third, the lack of trust prevents individuals from using digital services, and the main factors for increasing trust are the security and reliability of the service. Other important factors are transparency and ease of use. Personalization of services based on the previous usage was the least important factor for a digital service. The content analysis showed that organizations are heavily criticized for jeopardizing privacy and confidentiality, whereas praise was given to organizations that focus on transparency. The main conclusions that can be drawn from these findings are that the need for ethics is even more apparent in marketing than ever before, because of the broad access to personal data. The power of Big Data does not come from the raw data itself but from the ability to combine and merge data, thus creating detailed insights about consumers. There also lies the foundation for ethical dilemmas. The most crucial challenge for organizations in the use of personal data is how to build and maintain trust. Consumers’ use of personalized services is dependent on whether the organization is perceived to be trustworthy or not, and both the empirical research and literature have shown that the lack of trust reduces the use of digital services. Transparency and clear privacy policies can then be seen as a key ingredient in trust-building. Giving the consumers the ability to opt-out from data use and data selling without it affecting the use of services can create an enormous competitive advantage for organizations in the future. Still, it can be challenging to combine the need for profitability with ethics. However, success in the long-term requires organizations to integrate ethics with the operations throughout the organization.Nykyajan digitalisoituneessa maailmassa, ihmiset jättävät jälkiä itsestään aina verkossa toimiessaan. Vaikka markkinoijat käyttävät mielellään tätä dataa lisätäkseen ymmärrystä asiakkaistaan, monet kuluttajat ovat lisääntyvästi huolissaan yksityisyytensä katoamisesta ja pelkäävät, että heidän henkilökohtaiset tietonsa päätyvät vääriin käsiin. Tämän tutkielman tarkoituksena on ymmärtää, millainen rooli etiikalla on nykyaikaisessa kuluttajadataan perustuvassa markkinoinnissa. Tämä työ pyrkii myös tutkimaan, kuinka dataa voidaan käyttää markkinoinnissa eettisesti hyväksytyllä tavalla. Työssä saavutettiin nämä tavoitteet laajan kirjallisuuskatsauksen ja empiirisen tutkimuksen kautta. Empiirisessä tutkimuksessa käytettiin sekundaarista dataa vuonna 2018 tehdystä tutkimuksesta, jossa tutkittiin kuluttajien digitaalisten palveluiden käyttöä Euroopassa. Tämän sekundaarisen analyysin lisäksi tutkimuksessa toteutettiin sisällönanalyysi Twitterissä olevista dataetiikkaan liittyvistä tviiteistä. Tutkimus tuotti useita tutkimustuloksia. Ensinnäkin, kuluttajat pitävät yksityisyyttä, luottamuksellisuutta ja läpinäkyvyyttä kriittisinä seikkoina. Kuitenkin, suuri osa kuluttajista on epävarmoja dataan liittyvistä oikeuksistaan tai he eivät pidä esimerkiksi yksityisyysasetuksia mitenkään tärkeinä. Osa kuluttajista on myös sitä mieltä, että asetusten muuttamisella ei ole mitään merkitystä käytännössä. Toiseksi, kuluttajat haluavat itselleen vallan kieltäytyä henkilökohtaisen datan myynnin kolmansille osapuolille ja myös oikeuden poistaa tai muokata henkilökohtaista dataansa. Kolmanneksi, luottamuksen puute vähentää merkittävästi kuluttajien halukkuutta käyttää digitaalisia palveluita. Luottamusta lisääviä seikkoja ovat palvelun turvallisuus ja toimintavarmuus, sekä läpinäkyvyys ja helppokäyttöisyys. Personointia aiemman käytön perusteella ei pidetä merkittävänä digitaalisen palvelun ominaisuutena. Sisällönanalyysi osoitti, että yritykset saavat paljon kritiikkiä siitä, jos yksityisyys ja luottamuksellisuus vaarantuvat, kun taas yritykset, jotka panostavat läpinäkyvyyteen, saavat paljon kiitosta. Tutkimustuloksista voidaan tehdä johtopäätelmä, että kuluttajadatan laaja käyttö markkinoinnissa on lisännyt tarvetta eettiselle pohdinnalle. Massadatan tehokkuus ei johdu niinkään datasta itsestään, vaan kyvystä yhdistää erilaista dataa luoden yksityiskohtaista ja tarkkaa tietoa asiakkaista. Tämä luo myös puitteet eettisille haasteille. Kriittisin haaste yrityksille henkilökohtaisen datan käytössä on luottamuksen rakentaminen ja ylläpito. Personoitujen palveluiden käyttö riippuu paljolti siitä, kokevatko kuluttajat yrityksen luotettavaksi. Sekä empiirinen tutkimus että aiempi kirjallisuus osoittavat, että luottamuksen puute vähentää digitaalisten palveluiden käyttöä. Läpinäkyvyys ja selkeät yksityisyyskäytänteet voidaankin nähdä merkittävinä seikkoina

Creating business value from big data and business analytics : organizational, managerial and human resource implications

This paper reports on a research project, funded by the EPSRC’s NEMODE (New Economic Models in the Digital Economy, Network+) programme, explores how organizations create value from their increasingly Big Data and the challenges they face in doing so. Three case studies are reported of large organizations with a formal business analytics group and data volumes that can be considered to be ‘big’. The case organizations are MobCo, a mobile telecoms operator, MediaCo, a television broadcaster, and CityTrans, a provider of transport services to a major city. Analysis of the cases is structured around a framework in which data and value creation are mediated by the organization’s business analytics capability. This capability is then studied through a sociotechnical lens of organization/management, process, people, and technology. From the cases twenty key findings are identified. In the area of data and value creation these are: 1. Ensure data quality, 2. Build trust and permissions platforms, 3. Provide adequate anonymization, 4. Share value with data originators, 5. Create value through data partnerships, 6. Create public as well as private value, 7. Monitor and plan for changes in legislation and regulation. In organization and management: 8. Build a corporate analytics strategy, 9. Plan for organizational and cultural change, 10. Build deep domain knowledge, 11. Structure the analytics team carefully, 12. Partner with academic institutions, 13. Create an ethics approval process, 14. Make analytics projects agile, 15. Explore and exploit in analytics projects. In technology: 16. Use visualization as story-telling, 17. Be agnostic about technology while the landscape is uncertain (i.e., maintain a focus on value). In people and tools: 18. Data scientist personal attributes (curious, problem focused), 19. Data scientist as ‘bricoleur’, 20. Data scientist acquisition and retention through challenging work. With regards to what organizations should do if they want to create value from their data the paper further proposes: a model of the analytics eco-system that places the business analytics function in a broad organizational context; and a process model for analytics implementation together with a six-stage maturity model

Big Data Research in Information Systems: Toward an Inclusive Research Agenda

Big data has received considerable attention from the information systems (IS) discipline over the past few years, with several recent commentaries, editorials, and special issue introductions on the topic appearing in leading IS outlets. These papers present varying perspectives on promising big data research topics and highlight some of the challenges that big data poses. In this editorial, we synthesize and contribute further to this discourse. We offer a first step toward an inclusive big data research agenda for IS by focusing on the interplay between big data’s characteristics, the information value chain encompassing people-process-technology, and the three dominant IS research traditions (behavioral, design, and economics of IS). We view big data as a disruption to the value chain that has widespread impacts, which include but are not limited to changing the way academics conduct scholarly work. Importantly, we critically discuss the opportunities and challenges for behavioral, design science, and economics of IS research and the emerging implications for theory and methodology arising due to big data’s disruptive effects

Artificial intelligence applied to marketing management: Trends and projections according to specialists

Marketing Management is one of the areas that has been progressively integrating artificial intelligence systems, and the pace of the development of intelligent software that is very useful for marketing seems not to slow down. In fact, the growth and sophistication of technological systems promise to increase even more, which will inevitably affect operations as well as management and planning. In an attempt to assess and measure the expected impacts of AI on marketing departments in the short / medium term, a Delphi was carried out. Thereby, a panel of 21 marketing specialists (13 Portuguese and 8 international) was gathered, which was asked to evaluate on a Likert scale a series of statements, and to comment and debate among them. In this case it was a Real Time Delphi since the study was conducted using an online platform, which allowed all comments to be immediately available and visible to all participants. With this exploratory study, it was possible to conclude that the areas that are expected to be helped by intelligent systems to a greater extent – this is, the areas that will assist the automation of more operations - are customer recognition , market segmentation, sales forecasting and programmatic communication. On the other hand, the two most controversial statements among experts - thus risky to draw lessons - were statements regarding the autonomous operation of website adjustments and developments, as well as the adoption of intelligent systems to support strategic and strategic decision-making.A Gestão de Marketing é uma das áreas que tem vindo progressivamente a integrar sistemas de inteligência artificial, e a cadência do desenvolvimento de softwares inteligentes com grande utilidade para parece não abrandam. Na verdade, o crescimento e o grau de sofisticação dos sistemas tecnológicos prometem aumentar cada vez mais, o que promete afetar a vários níveis as operações e até a definição de estratégias de marketing e de gestão. Na tentativa de avaliar e medir os impactos da inteligência artificial nos departamentos de marketing no curto/médio prazo, procedeu-se à realização de um Delphi. Para isso reuniu-se um painel de 21 especialistas na área do marketing e da inteligência artificial (13 portugueses e 8 internacionais), ao qual foi colocada uma série de afirmações para que fossem avaliadas numa escala de Likert, comentadas e debatidas. Neste caso tratou-se de um Real Time Delphi uma vez que o estudo foi realizado recorrendo a uma plataforma online, o que permitiu que todos comentários ficassem imediatamente disponíveis e visíveis a todos os participantes. Com este estudo, de cariz marcadamente exploratório, concluiu-se que as áreas que se esperam vir a ser auxiliadas por sistemas inteligentes em maior medida – ou seja, as áreas que assistirão à automatização de um maior número de operações – são o reconhecimento do cliente, segmentação de mercado, previsão de vendas e comunicação programática. Por outro lado, os temas que mais controvérsia geraram entre os especialistas – sendo pouco seguro retirar ilações – referem-se à operação autónoma de ajustes e desenvolvimentos de websites, bem como à adoção de sistemas inteligentes para servirem de apoio à tomada de decisões estratégicas e de planeamento