46,634 research outputs found
Big Data and the Internet of Things
Advances in sensing and computing capabilities are making it possible to
embed increasing computing power in small devices. This has enabled the sensing
devices not just to passively capture data at very high resolution but also to
take sophisticated actions in response. Combined with advances in
communication, this is resulting in an ecosystem of highly interconnected
devices referred to as the Internet of Things - IoT. In conjunction, the
advances in machine learning have allowed building models on this ever
increasing amounts of data. Consequently, devices all the way from heavy assets
such as aircraft engines to wearables such as health monitors can all now not
only generate massive amounts of data but can draw back on aggregate analytics
to "improve" their performance over time. Big data analytics has been
identified as a key enabler for the IoT. In this chapter, we discuss various
avenues of the IoT where big data analytics either is already making a
significant impact or is on the cusp of doing so. We also discuss social
implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski
(eds.) Big Data Analysis: New algorithms for a new society, Springer Series
on Studies in Big Data, to appea
Big data analytics:Computational intelligence techniques and application areas
Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
Pushing the limits of accountability: big-data analytics containing and controlling COVID-19 in South Korea
Purpose:
The purpose of this paper is to illustrate how big data analytics pushed the limits of individuals' accountability as South Korea tried to control and contain coronavirus disease 2019 (COVID-19).
Design/methodology/approach:
The authors draw upon Deleuzo-Guattarian framework elaborating how a surveillant assemblage was rhizomatically created and operated to monitor a segment of the population holding them accountable. Publicly available secondary data, such as press release from the government and media coverage, were used.
Findings:
A COVID-19 Smart Management System and a Self-Quarantine Safety Protection App constituted a surveillance assemblage operating in a âstate-formâ. This comprises the central government departments, local councils, policing systems, providers of telecommunication and financial services, and independent groups of people. This assemblage pushed the limits of accountability as individuals who tested positive or might bear possible future risks of the infection and transmission were held accountable for their locations and health conditions.
Practical implications:
Policymakers may consider constructing this type of state-form for containing and controlling pandemics, such as COVID-19, while dealing with the issue of undermined privacy.
Social implications:
The mass may consider to what extent individuals' personal information should be protected and how to hold the governments accountable for the legitimate use of such information.
Originality/value:
While accountability studies have largely focussed on formal organisations, the authors illustrated how a broader context of a state-form, harnessing big data analytics, pushes the limits of accountability
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Application of Big Data to Support Evidence-Based Public Health Policy Decision-Making for Hearing
Ideally, public health policies are formulated from scientific data; however, policy-specific data are often unavailable. Big data can generate ecologically-valid, high-quality scientific evidence, and therefore has the potential to change how public health policies are formulated. Here, we discuss the use of big data for developing evidence-based hearing health policies, using data collected and analyzed with a research prototype of a data repository known as EVOTION (EVidence-based management of hearing impairments: public health pOlicy-making based on fusing big data analytics and simulaTION), to illustrate our points. Data in the repository consist of audiometric clinical data, prospective real-world data collected from hearing aids and an app, and responses to questionnaires collected for research purposes. To date, we have used the platform and a synthetic dataset to model the estimated risk of noise-induced hearing loss and have shown novel evidence of ways in which external factors influence hearing aid usage patterns. We contend that this research prototype data repository illustrates the value of using big data for policy-making by providing high-quality evidence that could be used to formulate and evaluate the impact of hearing health care policies
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To boardrooms and sustainability: the changing nature of segmentation
Market segmentation is the process by which customers in markets with some heterogeneity
are grouped into smaller homogeneous segments of more âsimilarâ customers. A market
segment is a group of individuals, groups or organisations sharing similar characteristics and
buying behaviour that cause them to have relatively similar needs and purchasing behaviour.
Segmentation is not a new concept: for six decades marketers have, in various guises, sought to
break-down a market into sub-groups of users, each sharing common needs, buying behavior
and marketing requirements. However, this approach to target market strategy development
has been rejuvenated in the past few years. Various reasons account for this upsurge in the
usage of segmentation, examination of which forms the focus of this white paper.
Ready access to data enables faster creation of a segmentation and the testing of propositions to
take to market. âBig dataâ has made the re-thinking of target market segments and value
propositions inevitable, desirable, faster and more flexible. The resulting information has
presented companies with more topical and consumer-generated insights than ever before.
However, many marketers, analytics directors and leadership teams feel over-whelmed by the
sheer quantity and immediacy of such data.
Analytical prowess in consultants and inside client organisations has benefited from a stepchange,
using new heuristics and faster computing power, more topical data and stronger
market insights. The approach to segmentation today is much smarter and has stretched well
away from the days of limited data explored only with cluster analysis. The coverage and wealth
of the solutions are unimaginable when compared to the practices of a few years ago. Then,
typically between only six to ten segments were forced into segmentation solutions, so that an
organisation could cater for these macro segments operationally as well as understand them
intellectually. Now there is the advent of what is commonly recognised as micro segmentation,
where the complexity of business operations and customer management requires highly
granular thinking. In support of this development, traditional agency/consultancy roles have
transitioned into in-house business teams led by data, campaign and business change planners.
The challenge has shifted from developing a granular segmentation solution that describes all
customers and prospects, into one of enabling an organisation to react to the granularity of the
solution, deploying its resources to permit controlled and consistent one-to-one interaction
within segments. So whilst the cost of delivering and maintaining the solution has reduced with
technology advances, a new set of systems, costs and skills in channel and execution
management is required to deliver on this promise. These new capabilities range from rich
feature creative and content management solutions, tailored copy design and deployment tools,
through to instant messaging middleware solutions that initiate multi-streams of activity in a
variety of analytical engines and operational systems.
Companies have recruited analytics and insight teams, often headed by senior personnel, such as
an Insight Manager or Analytics Director. Indeed, the situations-vacant adverts for such
personnel out-weigh posts for brand and marketing managers. Far more companies possess the
in-house expertise necessary to help with segmentation analysis. Some organisations are also
seeking to monetise one of the most regularly under-used latent business assets⊠data.
Developing the capability and culture to bring data together from all corners of a business, the open market, commercial sources and business partners, is a step-change, often requiring a
Chief Data Officer. This emerging role has also driven the professionalism of data exploration,
using more varied and sophisticated statistical techniques.
CEOs, CFOs and COOs increasingly are the sponsor of segmentation projects as well as the users
of the resulting outputs, rather than CMOs. CEOs because recession has forced re-engineering of
value propositions and the need to look after core customers; CFOs because segmentation leads
to better and more prudent allocation of resources â especially NPD and marketing â around the
most important sub-sets of a market; COOs because they need to better look after key
customers and improve their satisfaction in service delivery. More and more it is recognised that
with a new segmentation comes organisational realignment and change, so most business
functions now have an interest in a segmentation project, not only the marketers.
Largely as a result of the digital era and the growth of analytics, directors and company
leadership teams are becoming used to receiving more extensive market intelligence and
quickly updated customer insight, so leading to faster responses to market changes, customer
issues, competitor moves and their own performance. This refreshing of insight and a leadership
teamâs reaction to this intelligence often result in there being more frequent modification of a
target market strategy and segmentation decisions.
So many projects set up to consider multi-channel strategy and offerings; digital marketing;
customer relationship management; brand strategies; new product and service development;
the re-thinking of value propositions, and so forth, now routinely commence with a
segmentation piece in order to frame the ongoing work. Most organisations have deployed
CRM systems and harnessed associated customer data. CRM first requires clarity in segment
priorities. The insights from a CRM system help inform the segmentation agenda and steer how
they engage with their important customers or prospects. The growth of CRM and its ensuing
data have assisted the ongoing deployment of segmentation.
One of the biggest changes for segmentation is the extent to which it is now deployed by
practitioners in the public and not-for-profit sectors, who are harnessing what is termed social
marketing, in order to develop and to execute more shrewdly their targeting, campaigns and
messaging. For Marketing per se, the interest in the marketing toolkit from non-profit
organisations, has been big news in recent years. At the very heart of the concept of social
marketing is the market segmentation process.
The extreme rise in the threat to security from global unrest, terrorism and crime has focused
the minds of governments, security chiefs and their advisors. As a result, significant resources,
intellectual capability, computing and data management have been brought to bear on the
problem. The core of this work is the importance of identifying and profiling threats and so
mitigating risk. In practice, much of this security and surveillance work harnesses the tools
developed for market segmentation and the profiling of different consumer behaviours.
This white paper presents the findings from interviews with leading exponents of segmentation
and also the insights from a recent study of marketing practitioners relating to their current
imperatives and foci. More extensive views of some of these âleading lightsâ have been sought
and are included here in order to showcase the latest developments and to help explain both
the ongoing surge of segmentation and the issues under-pinning its practice. The principal
trends and developments are thereby presented and discussed in this paper
Big data for monitoring educational systems
This report considers âhow advances in big data are likely to transform the context and methodology of monitoring educational systems within a long-term perspective (10-30 years) and impact the evidence based policy development in the sectorâ, big data are âlarge amounts of different types of data produced with high velocity from a high number of various types of sources.â Five independent experts were commissioned by Ecorys, responding to themes of: students' privacy, educational equity and efficiency, student tracking, assessment and skills. The experts were asked to consider the âmacro perspective on governance on educational systems at all levels from primary, secondary education and tertiary â the latter covering all aspects of tertiary from further, to higher, and to VETâ, prioritising primary and secondary levels of education
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