Don’t Get the Cart before the Horse: There Are No Shortcuts to Prescriptive Analytics

Davenport [5] argues that the most important component for putting big data into action within an organization is talent management, and this opinion is widely shared among academics. We interviewed the chief purchasing officers (CPOs) of 15 major corporations and found that they did not feel it was problematic to find the right people for data analytics teams, and did not feel it was difficult to get resources to support data analytics efforts. Instead, they were frustrated by data issues such as granularity, accuracy, and integration. They also were intimidated by what they perceived to be the requirements for prescriptive analytics, and generally had not progressed beyond descriptive analytics. This article summarizes the roadblocks that the CPOs encountered as they attempted to move from descriptive to predictive to prescriptive analytics, and presents a set of steps which must be followed if organizations are to move up the analytics hierarchy

How prescriptive analytics influences decision making in precision medicine

Failure of the old model of medical decision making, “one-size-fits-all”, has encouraged the healthcare/medicine landscape to take advantage of big data and analytics for tailoring the treatments[1], based on individual patient’s differences in gen, environment, and lifestyle [2]. Whereas literature has demonstrated a strong contribution to the adoption of healthcare analytics over patient’s data, for better decision making [3], understanding the level and the degree that each type of analytics influences decision making, is crucial for addressing the type of problems [4]. While descriptive, diagnostic, and predictive analytics generate knowledge for decision support systems, prescriptive analytics recommends a proactive decision[5]. This study aims to highlight the influential and effective role of prescriptive analytics for fulfilling precision medicine which is defined as an emerging approach in medical decision making .FCT – Fundação para a Ciência e Tecnologia within the Projects Scope: DSAIPA/DS/0084/201

Big data educational portal for Small and Medium Sized Enterprises (SMEs)

Big Data refers to the massive amount of data generated from IT systems, sensors, and mobile devices. The values of big data are achieved by descriptive, predictive and prescriptive analytics. Small and Medium Sized Enterprises (SMEs) play a significant role in contributing to economic development. Big data is seen as a strategic and innovative tool for SMEs to stay competitive in the marketplace. However, there is lack of research in studying the value of big data to SMEs. Moreover, due to the shortage of quality learning platforms, SMEs have limited understanding of the potential benefits big data offers their businesses. This research aims to propose an educational portal of big data for SMEs by incorporating the pedagogy aspects. The research is underpinned by design science research. The portal contributes theoretically and methodologically by deriving the design knowledge of such portal and practically by increasing big data knowledge among SMEs

HadoopSec: Sensitivity-aware Secure Data Placement Strategy for Big Data/Hadoop Platform using Prescriptive Analytics

Hadoop has become one of the key player in offeringdata analytics and data processing support for any organizationthat handles different shades of data management. Consideringthe current security offerings of Hadoop, companies areconcerned of building a single large cluster and onboardingmultiple projects on to the same common Hadoop cluster.Security vulnerability and privacy invasion due to maliciousattackers or inner users are the main argument points in anyHadoop implementation. In particular, various types of securityvulnerability occur due to the mode of data placement in HadoopCluster. When sensitive information is accessed by anunauthorized user or misused by an authorized person, they cancompromise privacy. In this paper, we intend to address theapproach of data placement across distributed DataNodes in asecure way by considering the sensitivity and security of theunderlying data. Our data placement strategy aims to adaptivelydistribute the data across the cluster using advanced machinelearning techniques to realize a more secured data/infrastructure.The data placement strategy discussed in this paper is highlyextensible and scalable to suit different sort of sensitivity/securityrequirements

HadoopSec: Sensitivity-aware Secure Data Placement Strategy for Big Data/Hadoop Platform using Prescriptive Analytics

Hadoop has become one of the key player in offeringdata analytics and data processing support for any organizationthat handles different shades of data management. Consideringthe current security offerings of Hadoop, companies areconcerned of building a single large cluster and onboardingmultiple projects on to the same common Hadoop cluster.Security vulnerability and privacy invasion due to maliciousattackers or inner users are the main argument points in anyHadoop implementation. In particular, various types of securityvulnerability occur due to the mode of data placement in HadoopCluster. When sensitive information is accessed by anunauthorized user or misused by an authorized person, they cancompromise privacy. In this paper, we intend to address theapproach of data placement across distributed DataNodes in asecure way by considering the sensitivity and security of theunderlying data. Our data placement strategy aims to adaptivelydistribute the data across the cluster using advanced machinelearning techniques to realize a more secured data/infrastructure.The data placement strategy discussed in this paper is highlyextensible and scalable to suit different sort of sensitivity/securityrequirements

An Empirical Investigation of Big Data Analytics: The Financial Performance of Users versus Vendors

In the age of digitisation and globalisation, businesses have shifted online and are investing in big data analytics (BDA) to respond to changing market conditions and sustain their performance. Our study shifts the focus from the adoption of BDA to the impact of BDA on financial performance. We explore the financial performance of both BDA-vendors (business-to-business) and BDA-clients (business-to-customer). We distinguish between the five BDA-technologies (big-data-as-a-service (BDaaS), descriptive, diagnostic, predictive, and prescriptive analytics) and discuss them individually. Further, we use four perspectives (internal business process, learning and growth, customer, and finance) and discuss the significance of how each of the five BDA-technologies affect the performance measures of these four perspectives. We also present the analysis of employee engagement, average turnover, average net income, and average net assets for BDA-clients and BDA-vendors. Our study also explores the effect of the COVID-19 pandemic on business continuity for both BDA-vendors and BDA-clients

Prescriptive Control of Business Processes - New Potentials Through Predictive Analytics of Big Data in the Process Manufacturing Industry

This paper proposes a concept for a prescriptive control of business processes by using event-based process predictions. In this regard, it explores new potentials through the application of predictive analytics to big data while focusing on production planning and control in the context of the process manufacturing industry. This type of industry is an adequate application domain for the conceived concept, since it features several characteristics that are opposed to conventional industries such as assembling ones. These specifics include divergent and cyclic material flows, high diversity in end products’ qualities, as well as non-linear production processes that are not fully controllable. Based on a case study of a German steel producing company – a typical example of the process industry – the work at hand outlines which data becomes available when using state-of-the-art sensor technology and thus providing the required basis to realize the proposed concept. However, a consideration of the data size reveals that dedicated methods of big data analytics are required to tap the full potential of this data. Consequently, the paper derives seven requirements that need to be addressed for a successful implementation of the concept. Additionally, the paper proposes a generic architecture of prescriptive enterprise systems. This architecture comprises five building blocks of a system that is capable to detect complex event patterns within a multi-sensor environment, to correlate them with historical data and to calculate predictions that are finally used to recommend the best course of action during process execution in order to minimize or maximize certain key performance indicators

How do top- and bottom-performing companies differ in using business analytics?

Purpose Business analytics (BA) has attracted growing attention mainly due to the phenomena of big data. While studies suggest that BA positively affects organizational performance, there is a lack of academic research. The purpose of this paper, therefore, is to examine the extent to which top- and bottom-performing companies differ regarding their use and organizational facilitation of BA. Design/methodology/approach Hypotheses are developed drawing on the information processing view and contingency theory, and tested using multivariate analysis of variance to analyze data collected from 117 UK manufacture companies. Findings Top- and bottom-performing companies differ significantly in their use of BA, data-driven environment, and level of fit between BA and data-drain environment. Practical implications Extensive use of BA and data-driven decisions will lead to superior firm performance. Companies wishing to use BA to improve decision making and performance need to develop relevant analytical strategy to guide BA activities and design its structure and business processes to embed BA activities. Originality/value This study provides useful management insights into the effective use of BA for improving organizational performance