The disruptive nature of innovations and technological advancements present potentially huge benefits, however, it is critical to take caution because they also come with challenges. This author holds fast to the school of thought which suggests that every organisation or society should properly evaluate innovations and their attendant challenges from a strategic perspective, before adopting them, or else could get blindsided by the after effects. Big Data is one of such innovations, currently trending within industry and academia. The instinctive nature of Organizations compels them to constantly find new ways to stay ahead of the competition. It is for this reason, that some incoherencies exist in the field of big data. While on the one hand, we have some Organizations rushing into implementing Big Data Projects, we also have in possibly equal measure, many other organisations that remain sceptical and uncertain of the benefits of "Big Data" in general and are also concerned with the implementation costs. What this has done is, create a huge focus on the area of Big Data Implementation. Literature reveals a good number of challenges around Big Data project implementations. For example, most Big Data projects are either abandoned or do not hit their expected target. Unfortunately, most IS literature has focused on implementation methodologies that are primarily focused on the data, resources, Big Data infrastructures, algorithms etc. Rather than leaving the incoherent space that exists to remain, this research seeks to collapse the space and open opportunities to harness and expand knowledge. Consequently, the research takes a slightly different standpoint by approaching Big Data implementation from a Strategic Perspective. The author emphasises the fact that focus should be shifted from going straight into implementing Big Data projects to first implementing a Big Data Strategy for the Organization. Before implementation, this strategy step will create the value proposition and identify deliverables to justify the project. To this end, the researcher combines an Alignment theory, with Digital Business Strategy theory to create a Big Data Strategy Framework that Organisations could use to align their business strategy with the Big Data project. The Framework was tested in two case studies, and the study resulted in the generation of the strategic Big Data Goals for both case studies. This Big Data Strategy framework aided the organisation in identifying the potential value that could be obtained from their Big Data project. These Strategic Big Data Goals can now be implemented in Big data Projects.

<div> <p>E-commerce organizations, these days, need to keep striving for constant innovation. Customers have a massive impact on the performance of an organization, so industries need to have solid customer retention strategies. Various big data analytics methodologies are being used by organizations to improve overall online customer experience. While there are multiple techniques available, this research study utilized and tested a framework proposed by Laux et al. (2017), which combines Big Data and Six Sigma methodologies, to the e-commerce domain for identification of issues faced by the customer; this was done by analyzing online product reviews and ratings of customers to provide improvement strategies for enhancing customer experience. </p> <p>Analysis performed on the data showed that approximately 90% of the customer reviews had positive polarity. Among the factors which were identified to have affected the opinions of the customers, the Rating field had the most impact on the sentiments of the users and it was found to be statistically significant. Upon further analysis of reviews with lower rating, the results attained showed that the major issues faced by customers were related to the product itself; most issues were more specifically about the size/fit of the product, followed by the product quality, material used, how the product looked on the online portal versus how it looked in reality, and its price concerning the quality.</p> </div> <br>

With the growing business impact of distributed big data analytics jobs, it has become crucial to optimize their execution and resource consumption. In most cases, such jobs consist of multiple sub-entities called tasks and are executed online in a large shared distributed computing system. The ability to accurately estimate runtime properties and coordinate execution of sub-entities of a job allows a scheduler to efficiently schedule jobs for optimal scheduling. This thesis presents the first study that highlights spatial dimension, an inherent property of distributed jobs, and underscores its importance in efficient cluster job scheduling. We develop two new classes of spatial dimension based algorithms to<br>address the two primary challenges of cluster scheduling. First, we propose, validate, and design two complete systems that employ learning algorithms exploiting spatial dimension. We demonstrate high similarity in runtime properties between sub-entities of the same job by detailed trace analysis on four different industrial cluster traces. We identify design challenges and propose principles for a sampling based learning system for two examples, first for a coflow scheduler, and second for a cluster job scheduler.<br>We also propose, design, and demonstrate the effectiveness of new multi-task scheduling algorithms based on effective synchronization across the spatial dimension. We underline and validate by experimental analysis the importance of synchronization between sub-entities (flows, tasks) of a distributed entity (coflow, data analytics jobs) for its efficient execution. We also highlight that by not considering sibling sub-entities when scheduling something it may also lead to sub-optimal overall cluster performance. We propose, design, and implement a full coflow scheduler based on these assertions.