The Linkage to Business Goals in Data Science Projects

Modern data analytics equips businesses to make data-driven decisions by revealing patterns and insights that enhance strategic planning, operational efficiency, and process optimization. Its applications encompass personalized marketing through customer segmentation, predictive modelling for fraud detection, and enhancing security. A significant methodology in this realm is the Cross-Industry Standard Process for Data Mining (CRISP-DM), where the Business Understanding phase aims to ensure data science projects align with overarching business goals. However, challenges arise when these business objectives are ambiguous, ill-defined, or evolving. The complexity of data analytics projects underscores the need for domain expertise and robust collaboration between data scientists, business stakeholders, and domain experts. The imperative is to bridge the technical and business perspectives, manage expectations, and define project scopes. The short paper at hand addresses the question how data analytic goals can systematically align with business objectives in data science projects. By incorporating methods from Enterprise Architecture Management, we propose a structured approach for goal determination in data science projects, ensuring business and data mining objectives are seamlessly integrated

Investigating the Role of Enterprise Architecture in Big Data Analytics Implementation: A Case Study in a Large Public Sector Organization

Big Data Analytics (BDA) offers capabilities that can support a wide range of business areas across an organization. Organizations are increasingly turning to Enterprise Architecture (EA) to manage BDA implementation complexities. Through a case study in a large public sector organization, how EA supports various stages of BDA implementation is examined. The findings show that EA can address BDA challenges through 18 specific roles, which are categorised into four domains: Strategy (6 roles), Technology (4 roles), Collaboration (3 roles) and Governance (5 roles). While EA appears to have the most prominent role in strategy planning process, our study also identifies factors that can lead to the ineffectiveness of EA roles, such as frequent changes in business strategy. This study offers important implications to research and practice in EA and BDA implementation

Data and Predictive Analytics Use for Logistics and Supply Chain Management

Purpose The purpose of this paper is to explore the social process of Big Data and predictive analytics (BDPA) use for logistics and supply chain management (LSCM), focusing on interactions among technology, human behavior and organizational context that occur at the technology’s post-adoption phases in retail supply chain (RSC) organizations. Design/methodology/approach The authors follow a grounded theory approach for theory building based on interviews with senior managers of 15 organizations positioned across multiple echelons in the RSC. Findings Findings reveal how user involvement shapes BDPA to fit organizational structures and how changes made to the technology retroactively affect its design and institutional properties. Findings also reveal previously unreported aspects of BDPA use for LSCM. These include the presence of temporal and spatial discontinuities in the technology use across RSC organizations. Practical implications This study unveils that it is impossible to design a BDPA technology ready for immediate use. The emergent process framework shows that institutional and social factors require BDPA use specific to the organization, as the technology comes to reflect the properties of the organization and the wider social environment for which its designers originally intended. BDPA is, thus, not easily transferrable among collaborating RSC organizations and requires managerial attention to the institutional context within which its usage takes place. Originality/value The literature describes why organizations will use BDPA but fails to provide adequate insight into how BDPA use occurs. The authors address the “how” and bring a social perspective into a technology-centric area

Designing Business Analytics Solutions - A Model-Driven Approach

The design and development of data analytics systems, as a new type of information systems, has proven to be complicated and challenging. Model based approa- ches from information systems engineering can potentially provide methods, techniques, and tools for facilitating and supporting such processes. The contribution of this paper is twofold. Firstly, it introduces a conceptual modeling framework for the design and development of advanced analytics systems. It illustrates the framework through a case and provides a sample methodological approach for using the framework. The paper demonstrates potential benefits of the framework for requirements elicitation, clarification, and design of analytical solutions. Secondly, the paper presents some observations and lessons learned from an application of the framework by an experienced practitioner not involved in the original development of the framework. The findings were then used to develop a set of guidelines for enhancing the understandability and effec- tive usage of the framework

A decision-making framework for aligning business analytics with business objectives

Throughout this thesis, we discuss the impact of Business Analytics on the organizational decision-making process with the objective of designing a framework that provides the organization with extra-knowledge on how to implement and sustain their analytics. First, we develop the concept of capability using the resource-based view and the IT literature to define what is a Business Analytics capability. We then define the key capabilities that provide the organization with a competitive advantage. Moreover, we investigate the role of governance and alignment as well as the impact of the concepts on the decision making effectiveness. To provide an insight on the adjustment to be made in order to increase the organization Business Analytics performance, we emphasise the role of alignment between Information Technology governance, corporate governance, data governance and Business Analytics governance. Thereafter we create the framework based on academic and empirical research and apply this framework throughout a case study. Based on this case study we provide an academic recommendation to the investigated organization. This thesis highlights the importance of the creation of a Business Analytics governance. Also, the research provides a framework linking Business Analytics with decision making successfulness

Big data for monitoring educational systems

This report considers “how advances in big data are likely to transform the context and methodology of monitoring educational systems within a long-term perspective (10-30 years) and impact the evidence based policy development in the sector”, big data are “large amounts of different types of data produced with high velocity from a high number of various types of sources.” Five independent experts were commissioned by Ecorys, responding to themes of: students' privacy, educational equity and efficiency, student tracking, assessment and skills. The experts were asked to consider the “macro perspective on governance on educational systems at all levels from primary, secondary education and tertiary – the latter covering all aspects of tertiary from further, to higher, and to VET”, prioritising primary and secondary levels of education

What can AI do for you?

Simply put, most organizations do not know how to approach the incorporation of AI into their businesses, and few are knowledgeable enough to understand which concepts are applicable to their business models. Doing nothing and waiting is not an option: Mahidar and Davenport (2018) argue that companies that try to play catch-up will ultimately lose to those who invested and began learning early. But how do we bridge the gap between skepticism and adoption? We propose a toolkit, inclusive of people, processes, and technologies, to help companies with discovery and readiness to start their AI journey. Our toolkit will deliver specific and actionable answers to the operative question: What can AI do for you

Multichannel in a complex world

The proliferation of devices and channels has brought new challenges to just about every organisation in delivering consistently good customer experiences and effectively joining up service provision with marketing activity, data and content. A good multichannel strategy and execution is increasingly becoming essential to marketers and customer experience professionals from every sector. This report seeks to identify the key issues, challenges and opportunities that surround multichannel and provide some best practice insight and principles on the elements that are key to multichannel success. As part of the research for this report, we spoke to six experienced customer experience and marketing practitioners from large organisations across different sectors. In Multichannel Marketing: Metrics and Methods for On and Offline Success, Akin Arikan (2008) said: ‘Because customers are multichannel beings and demand relevant, consistent experiences across all channels, businesses need to adopt a multichannel mind-set when listening to their customers.’ It was clear from the companies interviewed for this report that it remains challenging for many organisations to maintain consistency across so many customer touchpoints. Not only that, but the ability to balance consistency with the capability to fully exploit the unique attributes of each channel remains an aspiration for many. The proliferation of devices and digital channels has added complexity to customer journeys, making issues around the joining up of customer experience and the attribution of value of key importance to many. Whilst senior leaders within the organisations spoken to seem to be bought in to multichannel, this buy-in was not always replicated across the rest of the organisation and did not always translate into a cohesive multichannel strategy. A number of companies were undertaking work around customer journey mapping and customer segmentation, using a variety of passive and actively collected data in order to identify specific areas of poor customer experience and create action plans for improvement. Others were undertaking projects using sophisticated tracking and tagging technologies to develop an understanding of the value and role of specific channels and to provide better intelligence to the business on attribution that might be used to inform future investment decisions. A consistent barrier to improving customer experience is the ability to join up many different legacy systems and data in order to provide a single customer view and form the basis for delivery of a more consistent and cohesive multichannel approach. Whilst there remain significant challenges around multichannel, there are some useful technologies allowing businesses to develop better insight into customer motivation and activity. Nonetheless, delivery of seamless multichannel experience remains a work-inprogress for many

The Master’s Program in Information Systems: A Survey of Core Curricula in AACSB-Accredited Business Schools in the United States

This paper investigates the core curricula of Information Systems (IS) master’s programs. It examines all 532 AACSB-accredited business schools in the United States and identifies 74 IS master’s programs. MSIS 2016 and other curricular models and studies are used in a research framework to survey core courses. The top three required courses are Data, Information, and Content Management, Systems Development and Deployment, and Project and Change Management. One unexpected result is that Business Intelligence/Analytics/Data Mining is now the fourth most popular core course, while Business Continuity and Information Assurance is the fifth. The results are compared to those of a 2012 study to examine IS master curricula’ change over the last decade. Based on actual data on core courses being offered, a new IS master’s curriculum model is developed