Business analytics

About: Business analytics is a research topic. Over the lifetime, 3593 publications have been published within this topic receiving 84601 citations. The topic is also known as: Business Analytics & business analytics.

Analysis of requirements for Big Data Adoption to maximize IT Business Value

Abstract: The measurement of Information Technology success is critical for any organization. Understanding value of the Information Technology solutions is the need of every management, researchers and practitioners for taking the efficient decisions. Resource based view and contingency theory have been used by the researchers to measure IT Business Value (ITBV). Various studies have examined the relationship between the Information technology and organizational performance. Understanding and measuring ITBV is ongoing research area. Business Analytics is the area of deriving the knowledge from raw data using statistical and quantitative techniques, predictive models to make efficient and evidence based business decisions. Business Analytical Systems have provided positive impact on the decision making process of the organization. Researchers have used process and variance models to explain how organizations use business analytics to create business value. Understanding the business value becomes more challenging in the context of the Big Data. The business value depends on the relationship between business and IT objects. Big Data make this relationship more complex and hence possess challenges for knowing the business value. The objective of this paper is to define the requirements for Big Data Adoption so that organizations can maximize their IT Business Value.

Analysis of Data Quality Aspects in Data Warehouse Systems

Abstract: Data quality is a critical factor for the success of data warehousing projects. If data is of inadequate quality, then the knowledge workers who query the data warehouse and the decision makers who receive the information cannot trust the results. In order to obtain clean and reliable data, it is imperative to focus on data quality. While many data warehouse projects do take data quality into consideration, it is often given a delayed afterthought. Even QA after ETL is not good enough the Quality process needs to be incorporated in the ETL process itself. Data quality has to be maintained for individual records or even small bits of information to ensure accuracy of complete database. Data quality is an increasingly serious issue for organizations large and small. It is central to all data integration initiatives. Before data can be used effectively in a data warehouse, or in customer relationship management, enterprise resource planning or business analytics applications, it needs to be analyzed and cleansed. To ensure high quality data is sustained, organizations need to apply ongoing data cleansing processes and procedures, and to monitor and track data quality levels over time. Otherwise poor data quality will lead to increased costs, breakdowns in the supply chain and inferior customer relationship management. Defective data also hampers business decision making and efforts to meet regulatory compliance responsibilities. The key to successfully addressing data quality is to get business professionals centrally involved in the process. We have analyzed possible set of causes of data quality issues from exhaustive survey and discussions with data warehouse groups working in distinguishes organizations in India and abroad. We expect this paper will help modelers, designers of warehouse to analyze and implement quality warehouse and business intelligence applications.

Business Analytics for Managers

Abstract: Introduction.- Exploring & Discovering Data.- Data Modeling I - Basics.- Data Modeling II - Making Models More Flexible.- Data Modeling III - Making Models More Selective.- Data Modeling IV - Fine Tuning Your Model.- Introduction to the Statistical Software R.

The role of business analytics in the controllers and management accountants’ competence profiles: An exploratory study on individual-level data

Abstract: In recent years, the rise of big data has led to an obvious shift in the competence profile expected from the controller and management accountant (MA). Among others, business analytics competences and information technology skills are considered a “must have” capability for the controlling and MA profession. As it still remains unclear if these requirements can be fulfilled by today’s employees, the purpose of this study is to examine the supply of business analytics competences in the current competence profiles of controlling professionals in an attempt to answer the question whether or not a skills gap exists.,Based on a set of 2,331 member profiles of German controlling professionals extracted from the business social network XING, a text analytics approach is conducted to discover patterns out of the semi-structured data. In doing so, the second purpose of this study is to encourage researchers and practitioners to integrate and advance big data analytics as a method of inquiry into their research process.,Apart from the mediating role of gender, company size and other variables, the results indicate that the current competence profiles of the controller do not comply with the recent requirements towards business analytics competences. However, the answer to the question whether a skills gap exist must be made cautiously by taking into account the specific organizational context such as level of IT adoption or the degree of job specialization.,Guided by the resource-based view of the firm, organizational theory and social cognitive theory, an explanatory model is developed that helps to explain the apparent skills gap, and thus, to enhance the understanding towards the rationales behind the observed findings. One major limitation to be mentioned is that the data sample integrated into this study is restricted to member profiles of German controlling professionals from foremost large companies.,The insights provided in this study extend the ongoing debate in accounting literature and business media on the skills changes of the controlling and MA profession in the big data era. The originality of this study lies in its explicit attempt to integrate recent advances in data analytics to explore the self-reported competence supplies of controlling professionals based on a comprehensive set of semi-structured data. A theoretically founded explanatory model is proposed that integrates empirically validated findings from extant research across various disciplines.