Creating Value with Big Data in Marketing

Big data is a celebrated topic in Business as well as research community for several years. With the revolution of Big Data, it is becoming easy and less expensive to store tremendous amount of data for future analysis. Weather data gets accumulated very fast and on a large scale. Thorough analysis and research is required on handling this big data and utilizing it for accurate weather prediction. As deterministic weather forecasting models are usually time consuming, it becomes challenging to efficiently use this large volume of data in hand. Machine learning methods are already proved to be good replacement for traditional deterministic approaches in weather prediction. These algorithms are popular for their scalability and hence more suitable in big data solutions. This paper proposes an approach of processing such Big volume of weather Data using Hadoop. Proposal includes Artificial Neural Network implemented on Map-reduce framework for short term rainfall prediction. Rainfall is predicted one day ahead using temperature and rainfall data of immediately preceding days. Temperature and Rainfall data of India over past 63 years (1951-2013) is used for this study.

The term Big Data can be defined as analysis a large volume of data in unstructured databases, organizations of different segment and size, has been employing the Big Data Analytics philosophy as support tool strategic to anticipate valuable insights and trends on the behavior of consumers and their expectations, thus gaining a competitive advantage in the market in which they operate. However, to extract information from values as the goal of turning that volume of data into predictive information or insights is still a big challenge in Big Data. The main objective of this work is to present an implementation of a Big Data project, using data originated from social networks, as well as text mining techniques and machine learning, through the implementation of K-Means and SVM algorithms, with intention of identifying patterns from dengue outbreaks, through analyzes that show insights of probable outbreaks of dengue in a particular region of Brazil. The results obtained indicate that the implemented project had a satisfactory yield if compared of the data collected of the Ministry of Health of Brazil, thus indicating a potential for utilization of its purpose. It is observed that the main advantage of the analyzes in Big Data is related to the possibility of use the unstructured data that can be obtained in social networks, e-commerce sites, among others and structured data obtained from traditional databases and from this union, to extract information from values that can be used to benefit of organizations, thus allowing know the future behaviors and thereby act in a preventive way.

Chapter 21 - Big Data Integration

Big Data OverviewDriven by the need to generate business value, the enterprise has started to adopt Big Data as a solution, migrating from the classical databases and data stores which lack the flexibility and are not optimized enough [1].The changes in the environment make big data analytics attractive to all types of organizations, while the market conditions make it practical. The combination of simplified models for development, commoditization, a wider palette of data management tools, and low-cost utility computing has effectively lowered the barrier to entry. [2]. The concept addresses large volumes of complex data, rapid growing data sets that may come from different autonomous sources.In recent approaches, Big Data is characterized by principles known as the 4VVolume, Variety, Velocity and Veracity [3]. There are opinions about accepting other principles as Big Data characteristics, such as Value.Each day more businesses realize that Big Data is relevant as the applications generate large volumes of data generated automatically, from different data sources, centralized or autonomous. As traditional databases hit limitations when the need of analyzing this data, dedicated solutions must be considered.Important BigData Solutions:* Apache HBase/Hadoop is based on Google's BigTable distributed storage system, which runs on top of Hadoop as a distributed and scalable big data store. This means that HBase can leverage the distributed processing paradigm of the Hadoop Distributed File System (HDFS) and benefit from Hadoop's MapReduce programming model. It combines the scalability of Hadoop with real-time data access as a key/value store and deep analytic capabilities of Map Reduce [4].HBase allows to query for individual records as well as derive aggregate analytic reports across a massive amount of data. It can host large tables with billions of rows, millions of columns and run across a cluster of commodity hardware. HBase is composed of three types of servers in a master slave type of architecture. Region servers are responsible to serve data for reads and writes. When accessing data, clients communicate with HBase Region Servers directly. Region assignment, DDL (create, delete tables) operations are handled by the HBase Master process.* Apache Cassandra is a distributed database used for the administration and management of large amounts of structured data across multiple servers, while providing highly available service and no single point of failure. It provides features such as continuous availability, linear scale performance, data distribution across multiple data centers and cloud availability zones. Cassandra inherits its data architecture from Google's BigTable and it borrows its distribution mechanisms from Amazon's Dynamo.The nodes in a Cassandra cluster are completely symmetrical, all having identical responsibilities. Cassandra also employs consistent hashing to partition and replicate data. it has the capability of handling large amounts of data and thousands of concurrent users or operations per second across multiple data centers.Cassandra has a hierarchy of caching mechanisms and carefully orchestrated disk I/O which ensures speed and data safety. Write operations are sent first to a persistent commit log (ensuring a durable write), then to a write-back cache called a memTable; when the memTable fills, it is flushed to a sorted string table - SSTable - on disk. A Cassandra cluster is organized as a ring, and it uses a partitioning strategy to distribute data evenly.* Redis represents an in-memory data structure store used as a database, cache and message broker. It supports data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperlogs and geospatial indexes with radius queries. Redis stores all data in RAM, allowing lightning fast reads and writes. It runs extremely efficiently in memory and handles high-velocity data, needing simple standard servers to deliver millions of operations per second with submillisecond latency. …

At this time big data, with large data volumes that continue to grow and become complex, is a challenge and opportunity for governments and organizations. This is said to be a challenge considering that the increase in data volume will produce a lot and variety of information, thus requiring efficient ways of storing and managing data. Big data is said to be an opportunity considering that the magnitude of the study results and proper information management will open up new opportunities to recognize the potential for improving services in various government and organizational sectors. Big data can open up opportunities to increase knowledge about various aspects of people's lives so that it can encourage the creation of new trends and innovations in various fields, improve and optimize operations, and determine future conditions so that governments and organizations can produce appropriate basic products. In the future, big data will change the way governments and organizations run their operations, creating challenges and opportunities for decision-makers. Even so, in the future big data can be utilized more optimally for the benefit of the wider community. In the future, big data has great potential to be used in many sectors to improve people's welfare. By using the right methods, big data can be very valuable in improving the welfare and quality of life of the wider community. Therefore, to be able to implement big data to improve the welfare and quality of life of the wider community, it is necessary to understand the challenges and opportunities of big data in the development of digital technology.

With the development of strategic goals of “The Belt and Road”, cross-border e-commerce has developed prosperously in China. The application of big data has been a vital strategic measure for cross-border e-commerce in “Internet+” era. Based on the motivation of many cross-border e-commerce enterprises to apply big data in marketing, this paper researches the procedures and fruits of applying big data into the accurate marketing of cross-border e-commerce combining the qualitative methods and literature review. It concluded that applying big data to cross-border marketing not only provides consumers' convenience but also enterprises' benefits of various aspects. Therefore, the research on the application of big data in accurate marketing of cross-border e-commerce bears practical significance.

The volume of the coming data in HEP is growing. The volume of the data to be held for a long time is growing as well. Large volume of data – big data – is distributed around the planet. The methods, approaches how to organize and manage the globally distributed data storage are required. The distributed storage has several examples for personal needs like own-cloud.org, pydio.com, seafile.com, sparkleshare.org. For enterprise-level there is a number of systems: SWIFT - distributed storage systems (part of Openstack), CEPH and the like which are mostly object storage. When several data center’s resources are integrated, the organization of data links becomes very important issue especially if several parallel data links between data centers are used. The situation in data centers and in data links may vary each hour. All that means each part of distributed data storage has to be able to rearrange usage of data links and storage servers in each data center. In addition, for each customer of distributed storage different requirements could appear. The above topics are planned to be discussed in data storage proposal.

Chapter 16 - Harnessing big data to strengthen evidence-informed precise public health response

The advent of information and communication technologies (ICT) ushers a cost-effective prospect to take care of large volumes of complex data, commonly known as “big data” in the supply chain operational environment. Big data is being generated today by web applications, social media, intelligent machines, sensors, mobile phones, and other smart handheld devices. Big data is characterized in terms of the velocity, volume, and variety with which it produces along the supply chain. This is due to recent advances in telecommunication networks along with centralized and decentralized data storage systems, which are processed thanks to modern digital computational capabilities. There is a growing interest in the use of this large volume of data and advanced analytics for diverse types of business problems in supply chain management (SCM). Such decision-support software applications employ pure mathematical techniques, artificial intelligence techniques, and sometimes uses both techniques to perform analytical operations that undercover relationships and patterns within supply chain generated big data. This chapter proposes a framework for the utilization of big data in SCM decision making. The framework is based on the SCOR (supply chain operations reference) model, which is endorsed by Supply Chain Council (SCC). The proposed framework is influenced by the enterprise potential of augmented reality and virtual reality in supply chain applications, and it identifies key categories of big data analytics applications for the key businesses of SCOR model. Finally, the chapter highlights research issues to extract insight from big data sources for enterprise decision making.

As the volume of data generating is increasing day by day in this internet world, the term Big Data is becoming a very popular buzzword in today's market. Big Data is used in various sectors of the internet world. In this paper an effort is made to demonstrate that even the healthcare industries are stepping into Big Data pool to take all benefits from its various advanced tools and technologies. The paper presents the review of various research efforts made in healthcare domain using Big Data concepts and methodologies. The thought of Big Data can be used for better health planning. Its methodologies can be used for healthcare data analytics which helps in better decision making to increase the business value and customer interest and to provide eHealth services among various healthcare stakeholders by using messaging standards like Health Level?, Digital Imaging and Communications in Medicine (DICOM), Health Insurance Portability and Accountability (HIPAA), message broker etc. Big Data techniques can be applied to develop systems for the early diagnosis of disease, understand connection between HATS (HIV/AIDS Tuberculosis and Silicosis) and also to develop integrated data analytics platforms. After presenting these many positive progresses of Big Data on healthcare, the paper also presents the hurdles faced by healthcare systems in using Big Data technologies. Further the paper includes the list of various Big Data tools, few case studies, few applications which are worth implementing using Big Data in healthcare followed by the concluding remarks.

Big data has broken through the public imagination, has revolutionised the process through which business find innovative ways, and has transformed the data into valuable information that will shape business intelligence and gain business insights to make better decisions. The purpose of this study is to review the development of big data, architecture, and the use of big data in marketing analytics. From the analysis of literature reviews, a big data in marketing analytics model has been proposed. In using big data in marketing, marketers need balanced analytics and then identify opportunities for improvement based on reporting or analysing past and present big data to predict and influence the future.

Big data has broken through the public imagination, has revolutionised the process through which business find innovative ways, and has transformed the data into valuable information that will shape business intelligence and gain business insights to make better decisions. The purpose of this study is to review the development of big data, architecture, and the use of big data in marketing analytics. From the analysis of literature reviews, a big data in marketing analytics model has been proposed. In using big data in marketing, marketers need balanced analytics and then identify opportunities for improvement based on reporting or analysing past and present big data to predict and influence the future.

In this study, we attempt to assess the value of the term Big Data when used by researchers in their publications. For this purpose, we systematically collected a corpus of biomedical publications that use and do not use the term Big Data. These documents were used as input to a machine learning classifier to determine how well they can be separated into two groups and to determine the most distinguishing classification features. We generated 100 classifiers that could correctly distinguish between Big Data and non-Big Data documents with an area under the Receiver Operating Characteristic (ROC) curve of 0.96. The differences between the two groups were characterized by terms specific to Big Data themes—such as `computational’, `mining’, and `challenges’—and also by terms that indicate the research field, such as `genomics’. The ROC curves when plotted for various time intervals showed no difference over time. We conclude that there is a detectable and stable difference between publications that use the term Big Data and those that do not. Furthermore, the use of the term Big Data within a publication seems to indicate a distinct type of research in the biomedical field. Therefore, we conclude that value can be attributed to the term Big Data when used in a publication and this value has not changed over time.

Big data is the data set which contains variety of data which increases on a daily basis in an organisation and also it has large volume of data with high velocity. It is the complex form of data which is usually identified from new set of data sources. It is widely used in most of the companies as it will have latest data which is useful to solve critical issues in the business or an organisation. BIG data has such large volume of data that it cannot be handled by simple data handling software and need latest technology software to handle such large volumes of data. The amount of data present in big data is so complex and large in volume that it needs skilled professionals to manage the data and requires efficient efforts to maintain it. Small businesses cannot afford to implement BIG data in their business due to its complex nature and also the cost factor is higher as compared to traditional data software. The paper will explain the various categories of big data, its importance, strategies and implementation process.

Big Data- Is a Turnkey Solution