Era Of Data Analytics Research Articles

The Impact of Web Analytics Tools on the Performance of Small and Medium Enterprises

In the era of data analytics, the use of web analytics tools is widely spread among Small and Medium Enterprises (SMEs), especially in the e-commerce sector, to analyze web usage data and measure website performance due to the several features it provides. Most web analytics studies were conducted based on marketing rather than a managerial point of view. Therefore, there is a necessity to study the impact from a managerial perspective by exploring the impact on financial and non-financial performance and the decision-making process. This study assessed the impact of web analytics tools on the overall performance of SMEs, targeting Saudi SMEs and considering the lack of web analytics studies in this sector in Saudi Arabia. The sample involved eight Saudi SMEs who participated by answering interview questions, which were analyzed using thematic analysis. The findings demonstrated four main themes: the current web analytics tools used by Saudi SMEs, the motivations behind their use, and their impact on SMEs' performance and decision-making. The results showed that these tools positively affect the overall performance of SMEs. Google Analytics (GA) was the tool most used by the sample. Seven motives were discovered that encourage SMEs to use web analytics tools. This study also addressed the common Key Performance Indicators (KPIs) used by the participants. Concerning decision-making, it was found that web analytics tools affected and supported SMEs' decisions. Furthermore, this study evaluated the role of Zid, the Saudi e-commerce platform for data analytics and performance measurement.

Reconsidering insurance discrimination and adverse selection in an era of data analytics

This article demonstrates how replacing age- and gender-based pricing variables with telematics data in auto insurance risk classification systems minimises insurance discrimination and increases cream skimming adverse selection. The study explains how incorporating telematics data into insurance pricing schemes reduces pricing heterogeneity, consistent with the anti-discrimination objective of Aristotelian equality. It also describes how anti-discrimination prohibitions of age- and gender-based insurance pricing can result in regulatory adverse selection; traditional adverse selection, where asymmetric information favouring applicants can result in an overpopulation of high-risk drivers in risk pools; and cream skimming adverse selection, where asymmetric information favouring telematics-based insurers supports premium discounts that attract safer drivers, prompting an underpopulation of low-risk drivers among non-telematics insurers. The study explains how insurers minimise their vulnerability to cream skimming by quickly entering the pricing “arms race” with their own telematics-based products and how incorporating telematics data increases the efficiency of risk classification systems.

Business Intelligence and Service Oriented Architecture – Improving IT Investments

Information Technology does not solve business problems. Rather, IT can be used as an enabler to meet goals, targets and position the organization in the minds of the customer. A Service Oriented Architecture (SOA) can enable higher level services from more primitive business processes. The result is a flexible, agile organization. However, in some cases, without a business model, an SOA can be implemented but the result is islands of incompatible services. In the era of data analytics, the evolution of business intelligence has assumed a more dominant role. Proponents argue that Business Intelligence subsumes SOA. Other practitioners argue that Business Intelligence (BI) and SOA are distinct from each other as each aims to deliver different perspectives to an organization. This paper challenges this view by developing a framework where both SOA and BI are pivotal to delivering the business model. The analytics, business intelligence and data mining handshake with the SOA, allowing decision makers to plan, coordinate and control resources to meet the goals, targets and performance measures set for the organization. Business Intelligence and SOA need not operate on different levels for an organisation. In an era where companies are trying to go from barely surviving IT expenses towards developing an IT portfolio, the more options for technologies supporting asset data management, warehousing and mining to interact and co-exist are necessary. Putting aside arguments of Business Intelligence vs. SOA, we propose that they can handshake and bring out the collaborative synergies to enable organizational decision making to address the competitive challenges in today’s global marketplace.