Addressing big data variety using an automated approach for data characterization

The creation of new knowledge from manipulating and analysing existing knowledge is one of the primary objectives of any cognitive system. Most of the effort on Big Data research has been focussed upon Volume and Velocity, while Variety, “the ugly duckling” of Big Data, is often neglected and difficult to solve. A principal challenge with Variety is being able to understand and comprehend the data. This paper proposes and evaluates an automated approach for metadata identification and enrichment in describing Big Data. The paper focuses on the use of self-learning systems that will enable automatic compliance of data against regulatory requirements along with the capability of generating valuable and readily usable metadata towards data classification. Two experiments towards data confidentiality and data identification were conducted in evaluating the feasibility of the approach. The focus of the experiments was to confirm that repetitive manual tasks can be automated, thus reducing the focus of a Data Scientist on data identification and thereby providing more focus towards the extraction and analysis of the data itself. The origin of the datasets used were Private/Business and Public/Governmental and exhibited diverse characteristics in relation to the number of files and size of the files. The experimental work confirmed that: (a) the use of algorithmic techniques attributed to the substantial decrease in false positives regarding the identification of confidential information; (b) evidence that the use of a fraction of a data set along with statistical analysis and supervised learning is sufficient in identifying the structure of information within it. With this approach, the issues of understanding the nature of data can be mitigated, enabling a greater focus on meaningful interpretation of the heterogeneous data.

Interactive Dashboard Design for Manager, Data Analyst and Data Scientist Perspective

With large amounts of unstructured data being produced every day, organizations are trying to extract as much relevant information as possible. This massive quantity of data is collected from a variety of sources, and data analysts and data scientists use it to create a dashboard that provides a complete picture of the organization's performance. Dashboards are business intelligence (BI) reporting tools that collect and show key metrics and key performance indicators (KPIs) on a single screen, enabling users to monitor and analyse business performance at a glance. An objective assessment of the company's overall performance, as well as of each department, is provided. If each department has access to the dashboard, it may serve as a springboard for future discussion and good decision-making. The goal of this article is to explain in detail the implementation of Dashboard and how it works, which will serve as a blueprint for building an effective dashboard with respect to best practices for dashboard design.

Data analytics in education: are schools on the long and winding road?

Purpose This study aims to investigate the organisational structure to exploit data analytics in the educational sector. The paper proposes three different organisational configurations, which describe the connections among educational actors in a national system. The ultimate goal is to provide insights about alternative organisational settings for the adoption of data analytics in education. Design/methodology/approach The paper is based on a participant observation approach applied in the Italian educational system. The study is based on four research projects that involved teachers, school principals and governmental organisations over the period 2017–2020. Findings As a result, the centralised, the decentralised and the network-based configurations are presented and discussed according to three organisational dimensions of analysis (organisational layers, roles and data management). The network-based configuration suggests the presence of a network educational data scientist that may represent a concrete solution to foster more efficient and effective use of educational data analytics. Originality/value The value of this study relies on its systemic approach to educational data analytics from an organisational perspective, which unfolds the roles of schools and central administration. The analysis of the alternative organisational configuration allows moving a step forward towards a structured, effective and efficient system for the use of data in the educational sector.

PERBEDAAN DATA ENGINEER, DATA SCIENTIST DAN DATA ANALYST

Seseorang yang ahli dalam keterampilan analisis data hanyalah keterampilan dasar seorang insinyur data. Keahlian statistik digunakan untuk memproses data baca dan tag, serta untuk mengkategorikan data. Karena erat kaitannya dengan pemodelan yang dibuat untuk menguji algoritma pada level data scientist. Model yang dibuat pada fase data scientist digunakan sebagai alat dalam fase business intelligence. Pada tahap akhir ini, eksekusi yang akan dilakukan harus memberikan dampak positif dan keuntungan yang besar bagi sebuah instansi.

Game Data Science

This book is aimed at giving readers an introduction to the practical side of game data science and thus can be used a textbook for game analytics or game user research class or as a reference to self learners and enthusiasts. Game data science is a term that we use to denote a process composed of methods and techniques by which an analyst or a data scientist can make sense of data to allow decision makers in a game company to make informed decisions. This process involves: statistical analysis, visualization, abstraction of low-level data, machine learning and sequence data modeling. The book introduces different methods borrowing from different fields including human computer interaction, machine learning, and data science, focusing on methods and techniques used by both industry and researchers within the field of games. The book examples and case studies specifically focus on gameplay log data. The book takes a practical stance on the subject by discussing theoretical foundation, practical approaches, and delves deeply into the different techniques proposed and used through labs, examples, and comprehensive surveys of various case studies from both industry and academia. Topics range from simple approaches to more advanced ones. No prior knowledge is required. The book is developed to be self contained and can be used as a good way to introduce the reader to data science and how it is applied to the filed of games.

Accounting in a Social Context

Accounting permeates all of society. Accounting information is not homogenous and varies not just from company to company but from user to user, meaning that the use of such accounting information is actually a social phenomenon within an organization. Accounting cannot therefore be understood simply in terms of its functional properties but more as a socially constructed set of actions taking place within the organization, the landscape of which is constantly transforming. Digital technologies in the form of big data and artificial intelligence (AI) are expanding the organization’s data eco-system forcing the accountant to develop their digital technology skillset and forge links with the data scientist, the incumbent custodian of these growing data streams. Meanwhile, a rapidly expanding sustainability agenda is broadening the organization’s biophysical landscape leading to even more data flows and creating the need for management accounting and control systems which will help organizations to behave in an environmentally sustainable and socially responsible manner. This chapter explores each of these issues and calls for a deeper understanding of the relationship between accounting and big data, AI and sustainability.