Academic literature on the topic 'Business Analytics'

Die Digitalisierung führt zu einer grundlegenden Veränderung von Entscheidungsprozessen in Unternehmen. Unter dem Stichwort Business Analytics wird das Potenzial datengestützter Entscheidung im Management diskutiert. Der Beitrag grenzt Business Analytics von Big Data ab, legt Möglichkeiten der Datenanalyse und -aufbereitung dar und zeigt Grenzen in der Anwendung auf.

Subject. The article addresses the analytical support for change management in conditions of orientation of economic entities to sustainable development and digital transformation of business. Objectives. The purpose is to identify modern features of business analysis, the content of the main types of business analytics, and the impact of digitalization processes on analytical activities development. Methods. The study draws on research methods, like analysis, synthesis, comparison, generalization, abstraction, systemic, strategic, and risk-oriented approaches. Results. The paper reveals the content, areas and tasks of business analysis in accordance with the standard of business analysis adopted by the International Institute of Business Analysis in November 2022. It assesses innovations in the field of regulation of business analysis at the global level; defines the specifics of business analysis and underpins prerequisites for its improvement under new challenges and large-scale digitalization; considers the interrelation of business analysis and business analytics, presents the systematization of the main types of analytics. Conclusions. The analysis of trends in the development of analytical activities of business entities enabled to reflect priority areas of business analytics, and perform their comparative assessment. The paper unveiled the emerging trend of divergence of business analysis and business analytics as separate types of analytical activity, and identified Business Intelligence, Big Data Analytics, and Data Science as the most promising areas of business analytics.

The main aim of this paper is to identify and specify factors that influence business analytics. A factor in this context refers to any significant characteristic that defines the environment in which business analytics and business in general are conducted. Factors and their understanding are essential for the quality of final business analytics solutions, given their complexity and interconnectedness. Factors play an extremely important role in analytic thinking and business analysts’ skills and knowledge. These factors determine effective approaches and procedures for business analytics, and, in some cases, they also aid in the decision to delay a business analytics solution given a situation. This paper has used the case study method, a qualitative research method, due to the need to carry out investigation within the actual business (company) environment, in order to be able to fully understand and verify factors affecting analytics from the viewpoint of all stakeholders. This study provides a set of 15 factors from business, company, and market environments, including their importance in business analytics.

In the age of digital transformation, businesses face an escalating challenge in managing cyber threats. The paper “Utilizing Business Analytics for Cybersecurity: A Proposal for Protecting Business Systems Against Cyber Attacks” delves into an innovative approach where the power of business analytics is harnessed to bolster cybersecurity defenses. An exhaustive exploration elucidates how data, a seemingly intangible asset, can be transformed into actionable insights that preemptively detect, mitigate, and counteract cyber threats. The discourse emphasizes the convergence of two distinct domains: business analytics and cybersecurity. This union is demonstrated to be synergistic, enhancing the capabilities of traditional cybersecurity methods. Predictive analytics forecast potential threats, behavioral analytics discern anomalies in user activities, and network analytics spotlight vulnerabilities in real-time. Moreover, the iterative nature of these analytical processes ensures a proactive and evolving defense mechanism. The paper underscores the myriad benefits of this integration, including efficient resource allocation, enhanced incident response, and the cultivation of an organizational culture centered on continuous learning. While the advantages are manifold, challenges are inherent. Issues related to privacy, data quality, and the necessity for regular model updates are discussed in depth. Furthermore, a detailed framework is proposed, guiding businesses in seamlessly incorporating business analytics into their cybersecurity strategies. From data collection and validation to model deployment and continuous monitoring, each stage is meticulously crafted to ensure maximum efficacy. In summation, the paper serves as both an enlightening exploration and a clarion call for businesses. In an era where threats evolve rapidly, the amalgamation of business analytics with cybersecurity presents a formidable solution, ensuring robust and resilient defenses.

To understand and be successful with analytics, it is important to be precise in understanding what analytics means, the different targets or approaches that companies can take to using analytics, and the drivers that lead to the use of analytics. For companies that use advanced analytics, the keys to success include a clear business need; strong, committed sponsorship; a fact-based decision making culture; a strong data infrastructure; the right analytic tools; and strong analytical personnel in an appropriate organizational structure. These are the same factors for success for business intelligence in general, but there are important nuances when implementing advanced analytics, such as with the data infrastructure, analytical tools, and personnel. Companies like Amazon.com, Overstock.com, Harrah’s Entertainment, and First American Corporation are exemplars that illustrate concepts and best practices.

Business analytics and decision science have emerged as pivotal domains in enhancing strategic business decision-making processes. This review delves into various techniques that organizations employ to optimize their operations and achieve competitive advantages. At the forefront of strategic decision-making is data analytics, where vast amounts of data are analyzed to extract valuable insights. Descriptive analytics provides a historical perspective by examining past data trends, enabling businesses to understand their performance over time. This retrospective analysis serves as a foundation for predictive analytics, which utilizes statistical models and machine learning algorithms to forecast future trends and outcomes. By leveraging predictive analytics, organizations can anticipate market shifts, customer preferences, and potential risks, thereby making informed decisions. Prescriptive analytics uses predictive models to guide strategic decision-making, utilizing optimization algorithms and simulation tools to identify optimal actions. Decision science integrates analytical techniques with human judgment, focusing on consumer behavior and psychological factors to tailor marketing strategies and product offerings. Additionally, artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing strategic decision-making by automating complex tasks and providing real-time insights. Natural language processing (NLP) algorithms analyze unstructured data sources, such as customer reviews and social media posts, to extract valuable information and sentiment analysis. This enables businesses to gauge customer satisfaction levels and identify areas for improvement promptly. Decision trees, regression analysis, and clustering techniques are widely used in business analytics to segment customers, identify patterns, forecast sales trends, evaluate alternatives, assess risks, and optimize resource allocation. In conclusion, business analytics and decision science offer a plethora of techniques that empower organizations to make informed, data-driven decisions. By leveraging descriptive, predictive, and prescriptive analytics, along with AI and ML technologies, businesses can navigate complex environments, capitalize on opportunities, and mitigate risks effectively. This review underscores the importance of integrating analytical techniques with human expertise to achieve strategic objectives and sustainable growth.