How to start with big data - a multiple case study

As part of an advancing digitalization, many enterprises feel the need to explore the possibilities big data may provide for their business. However, only a few companies use big data applications productively, despite its high expected potential. How companies examine the possibilities of big data, is therefore a highly interesting and relevant question. Based on a multiple case study we identify three different approaches: Companies either initially focus entirely on business aspects, or on a systematic build-up of a big data technology and data platform. Innovation adoption research is used as a theoretical basis

The Role of Big Data Analytics on Innovation: A Study from The Telecom Industry

Telecom companies face a fierce competition from innovation based start-up companies, particularly those that are using internet networks to offer communication services through the voice and video over internet protocol (VoIP & PVoIP) technologies. More than 10 years have passed from the time the internet and VoIP were widely used, but still, telecom companies are having a great deal of business success through offering a wide spectrum of services, products, deals, and packages to consumers using both B2C and B2B models. The future landscape of how telecom companies will evolve in the market is still not clear, particularly with the increase of aggressive competition from companies that are technology-innovative and starting to deliver new forms of ubiquitous communication technology and services. Understanding why and how telecom companies innovate in the market is very crucial in order to predict the future of this business sector. In this paper, we argue that telecom companies are utilising their capabilities that have a significantly important role in fostering innovation, namely information technology (IT) capability and knowledge management (KM) capability. IT capabilities have changed dramatically in the last few years with the introduction of intelligent systems, big data analytics, the Internet of Things and the wide use of mobile apps and sensors. It is not clear how these technologies play a role in telecom companies’ innovation and it is not clear whether IT impacts innovation directly or if KM capability has a mediation role in utilising technology to support innovation. This paper is a position paper to establish grounds for understanding how telecom companies innovate, and in particular how IT and KM capabilities influence innovation. We outline the methodology of this investigation as a qualitative study with stakeholders from multiple telecom companies and we expect at the end of the study to be able to offer a holistic view on the way these companies innovate in regard to their products and services. We aim at providing a cross case studies comparison towards a prediction of the future of the telecom business sector

AsterixDB: A Scalable, Open Source BDMS

AsterixDB is a new, full-function BDMS (Big Data Management System) with a feature set that distinguishes it from other platforms in today's open source Big Data ecosystem. Its features make it well-suited to applications like web data warehousing, social data storage and analysis, and other use cases related to Big Data. AsterixDB has a flexible NoSQL style data model; a query language that supports a wide range of queries; a scalable runtime; partitioned, LSM-based data storage and indexing (including B+-tree, R-tree, and text indexes); support for external as well as natively stored data; a rich set of built-in types; support for fuzzy, spatial, and temporal types and queries; a built-in notion of data feeds for ingestion of data; and transaction support akin to that of a NoSQL store. Development of AsterixDB began in 2009 and led to a mid-2013 initial open source release. This paper is the first complete description of the resulting open source AsterixDB system. Covered herein are the system's data model, its query language, and its software architecture. Also included are a summary of the current status of the project and a first glimpse into how AsterixDB performs when compared to alternative technologies, including a parallel relational DBMS, a popular NoSQL store, and a popular Hadoop-based SQL data analytics platform, for things that both technologies can do. Also included is a brief description of some initial trials that the system has undergone and the lessons learned (and plans laid) based on those early "customer" engagements

Conversations on a probable future: interview with Beatrice Fazi

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Ethical Implications of Predictive Risk Intelligence

open access articleThis paper presents a case study on the ethical issues that relate to the use of Smart Information Systems (SIS) in predictive risk intelligence. The case study is based on a company that is using SIS to provide predictive risk intelligence in supply chain management (SCM), insurance, finance and sustainability. The pa-per covers an assessment of how the company recognises ethical concerns related to SIS and the ways it deals with them. Data was collected through a document review and two in-depth semi-structured interviews. Results from the case study indicate that the main ethical concerns with the use of SIS in predictive risk intelli-gence include protection of the data being used in predicting risk, data privacy and consent from those whose data has been collected from data providers such as so-cial media sites. Also, there are issues relating to the transparency and accountabil-ity of processes used in predictive intelligence. The interviews highlighted the issue of bias in using the SIS for making predictions for specific target clients. The last ethical issue was related to trust and accuracy of the predictions of the SIS. In re-sponse to these issues, the company has put in place different mechanisms to ensure responsible innovation through what it calls Responsible Data Science. Under Re-sponsible Data Science, the identified ethical issues are addressed by following a code of ethics, engaging with stakeholders and ethics committees. This paper is important because it provides lessons for the responsible implementation of SIS in industry, particularly for start-ups. The paper acknowledges ethical issues with the use of SIS in predictive risk intelligence and suggests that ethics should be a central consideration for companies and individuals developing SIS to create meaningful positive change for society

The Digitalisation of African Agriculture Report 2018-2019

An inclusive, digitally-enabled agricultural transformation could help achieve meaningful livelihood improvements for Africa’s smallholder farmers and pastoralists. It could drive greater engagement in agriculture from women and youth and create employment opportunities along the value chain. At CTA we staked a claim on this power of digitalisation to more systematically transform agriculture early on. Digitalisation, focusing on not individual ICTs but the application of these technologies to entire value chains, is a theme that cuts across all of our work. In youth entrepreneurship, we are fostering a new breed of young ICT ‘agripreneurs’. In climate-smart agriculture multiple projects provide information that can help towards building resilience for smallholder farmers. And in women empowerment we are supporting digital platforms to drive greater inclusion for women entrepreneurs in agricultural value chains