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2019 | Book

Aligning Business Strategies and Analytics: Bridging Between Theory and Practice Read chapter Read first chapter

Aligning Business Strategies and Analytics

Bridging Between Theory and Practice

Editors: Murugan Anandarajan, Teresa D. Harrison

Publisher: Springer International Publishing

Book Series : Advances in Analytics and Data Science

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

This book examines issues related to the alignment of business strategies and analytics. Vast amounts of data are being generated, collected, stored, processed, analyzed, distributed and used at an ever-increasing rate by organizations. Simultaneously, managers must rapidly and thoroughly understand the factors driving their business. Business Analytics is an interactive process of analyzing and exploring enterprise data to find valuable insights that can be exploited for competitive advantage. However, to gain this advantage, organizations need to create a sophisticated analytical climate within which strategic decisions are made. As a result, there is a growing awareness that alignment among business strategies, business structures, and analytics are critical to effectively develop and deploy techniques to enhance an organization’s decision-making capability. In the past, the relevance and usefulness of academic research in the area of alignment is often questioned by practitioners, but this book seeks to bridge this gap.
Aligning Business Strategies and Analytics: Bridging Between Theory and Practice is comprised of twelve chapters, divided into three sections. The book begins by introducing business analytics and the current gap between academic training and the needs within the business community. Chapters 2 - 5 examines how the use of cognitive computing improves financial advice, how technology is accelerating the growth of the financial advising industry, explores the application of advanced analytics to various facets of the industry and provides the context for analytics in practice. Chapters 6 - 9 offers real-world examples of how project management professionals tackle big-data challenges, explores the application of agile methodologies, discusses the operational benefits that can be gained by implementing real-time, and a case study on human capital analytics. Chapters 10 - 11 reviews the opportunities and potential shortfall and highlights how new media marketing and analytics fostered new insights. Finally the book concludes with a look at how data and analytics are playing a revolutionary role in strategy development in the chemical industry.

Table of Contents

Frontmatter
Chapter 1. Aligning Business Strategies and Analytics: Bridging Between Theory and Practice
Abstract
In this chapter, we discuss the current gap between academic training and the needs within the business community, the potential for this gap to widen, and the role joint work between academic and industry experts can play in bridging this gap. We highlight the particular case of business analytics, calling attention to the current business landscape and the need for strong training of future employees, grounded in both rigorous theoretical background and links to the practical applications. The chapter concludes by emphasizing the particular contributions of each chapter and making a case for this type of work to be among the first of many steps in creating more meaningful dialogue between higher education and business practitioners.
Murugan Anandarajan, Teresa D. Harrison

Business Analytics in Practice

Frontmatter
Chapter 2. Cognitive Computing: Impacts on Financial Advice in Wealth Management
Abstract
Cognitive computing is a form of problem-solving that incorporates machine learning, big data, data mining, natural language processing, machine vision, robotics, and other strands of artificial intelligence. Cognitive computing solutions can be used as sole or partial solutions to augment decision-making. The financial services industry is in a state of transformation, driven by the convergence of rapid changes in financial service technologies (fintech) – including cognitive computing, the digitization of the consumer, the emergence of younger investors (millennials), increased regulatory scrutiny (DOL regulation), and continued fee compression for products and services. Cognitive computing offers a disruptive opportunity in the financial services industry by not only empowering the financial intermediary but also by delivering increased engagement and value to the consumer.
This study examines how the use of cognitive computing to improve financial advice can provide value for the financial intermediary and the end consumer. For the intermediary, the study will assess how cognitive computing can augment and supercharge the expertise of the financial advisor, enabling the advisor to deliver improved advice. For the consumer, the study will assess how cognitive computing can deliver high-quality, accurate advice comparable to that of a human advisor.
Russ Kliman, Bay Arinze
Chapter 3. Living or Dying in the Mashup of American Financial Services: Literate Does Not Mean Competent
Abstract
Clients are aging, passive investing is gaining favor, and client objectives are growing in detail and diversity. At the same time, advisors themselves are reaching retirement age with no clear succession plan in place. Technology disruption waves are transforming accounting, insurance, and estate planning, but it is the investment advisor who needs to adapt now or face extinction. We examine the risks facing the entire industry by focusing on the small independent advisory firm’s role and place in the ecosystem. We suggest that technology will accelerate the growth of the financial advising industry at the same time that the wealth accumulated by older generations migrates slowly to younger generations. We underscore the natural advantage of the small independent investment advisor as the trusted partner to a client’s life-cycle decision-making. We suggest that the small independent advisor can only survive and thrive through reinvention. The preferred trust relationship will remain between human client and human advisor but will be heavily machine augmented with analytics and big data, delivered via the cloud.
Elven Riley, Mark Schild
Chapter 4. Improving Fleet Management Strategy and Operational Intelligence with Predictive Analytics
Abstract
The fleet management industry is comprised of fleet management providers that help ensure an organization’s vehicles remain on the road supporting core business functions efficiently, safely, and at the lowest total operating cost. Recent technology advancements in the data analysis space coupled with enriched data domain have made it possible for analytics to be applied strategically for fleet management solutions. One of the latest and game changing services to enter the fleet management market in recent years is in the IoT (Internet of Things) space, specifically, vehicle telematics services. Collating pure telematics information with other information from other areas such as maintenance, fuel, and driver performance can improve fleet management. Using the case study of ARI, this paper explores the application of advanced analytics to various facets of fleet management and ARI’s experience in aligning analytics with its business strategy. The paper also outlines the steps needed to implement a telematics and analytics strategy in organizations and the importance of bridging the gap between theory and practice.
Bill Powell, Suresh Chandran
Chapter 5. Aligning Data Analytics and Supply Chain Strategy in the Biopharmaceutical Industry
Abstract
Much has been written recently about the important role that data and analytics will play in improving productivity and profitability of companies in the biopharmaceutical industry. Data analytics will be a source for value creation and sustained competitive advantage for companies as new technologies like the Internet of Things and digitization of supply chain play a role in transitioning this industry into a more customer-centric model. This paper provides an overview of the status of the pharmaceutical industry and role that data analytics plays in supply chain management. The objective of this paper is to provide a use case example of implementation of a supply chain blueprint model including specifics of technology platforms, planning and optimization tools, and value stream mapping that have enabled tremendous cost savings at AstraZeneca. Lessons learned from experience with consulting to other companies in the biopharmaceutical space in the area of data analytics and strategy are outlined. The importance of fostering a two-way dialogue between members of the business community and educators and introducing new programs like the future leaders program and Supply Chain Boards in bridging the gap between theory and practice through meaningful partnerships is also discussed.
Mark Holder, Amit Devpura, Anthony Lee, Suresh Chandran

Methodological Issues in Business Analytic

Frontmatter
Chapter 6. Importance of Project Management in Business Analytics: Academia and Real World
Abstract
Project management constitutes a powerful lever as organizations face increasing pressure to manage projects to budget, on time, and deliver more insights, in less time and with rapidly increasing amounts of data. This is critical especially in business analytics, with more than75% of organizations planning big data investments over the next several years. But the manipulation of massive amounts of data presents challenges – budgetary, time constraints, execution, proper manager skillsets, and such like. These challenges have cramped recent project rollouts, as only 37% of organizations have deployed big data projects in the past year; this suggests that filling the gap between data and insight remains a substantial hurdle as well as evolving need of project management for such projects. This chapter offers real-world examples of how project management professionals tackle big data challenges in a rapidly evolving, data-rich environment. Simultaneously, it establishes a bridge between business and academia as they both recognize the joint necessity to develop highly trained project managers to utilize the powerful and cutting edge analytical tools available to create value.
Samir Shah, Alexander Gochtovtt, Greg Baldini
Chapter 7. A Review and Future Direction of Business Analytics Project Delivery
Abstract
Business analytics is a core competency critical to organizations to stay competitive; however, many organizations are challenged at business analytics delivery, and these projects have a high rate of failure. The volume, variety, and velocity of the big data phenomenon and the lack of current methodologies for delivering business analytics projects are the primary challenges. Applying traditional information technology project methodologies is problematic and has been identified as the largest contributing factor for business analytics project failure. Business analytics projects focus on delivering data insights as well as software delivery. Agile methodologies focus on the ability to respond to change through incremental, iterative processes. Agile methodologies in software delivery have been on the rise, and the dynamic principles align with the discovery nature of business analytics projects. This article explores the big data phenomenon, its impact on business analytics project delivery, and recommends an agile framework for business analytic project delivery using agile methodology principles and practices.
Deanne Larson
Chapter 8. Aligning Operational Benefits of Big Data Analytics and Organizational Culture at WellSpan Health
Abstract
Our goal in this chapter is to demonstrate the operational benefits that can be gained by implementing real-time, big data analytics in a healthcare setting and the concomitant influence of organizational culture on adoption of the technology. Benefits include improving the quality and accuracy of clinical decisions, processing health records efficiently, streamlining workflow, and improving patient satisfaction. We demonstrate these benefits by investigating patient-physician interactions in a large medical practice at WellSpan Health, and we compare the observed workflow with a modified one made possible with a big data, real-time analytics platform. By comparing these two states, we illuminate the lost opportunity and the value left on the table by legacy behaviors and processes. In addition, we uncover organizational characteristics that create a climate for cultural modification and initial acceptance of big data, real-time analytics in a change-resistant organization. The combination of academic research and practitioner implementation shows that optimization of clinical operations is a key first step toward gaining user acceptance of big data technologies.
Gloria Phillips-Wren, Sueanne McKniff
Chapter 9. HR Analytics: Human Capital Return on Investment, Productivity, and Profit Sensitivity: A Case of Courtyard Marriott Newark at the University of Delaware
Abstract
The objective of this case study is to apply human capital analytics, more specifically, human capital return on investment, human resources productivity, and compensation efficiency at the Newark Courtyard Marriott Hotel, University of Delaware, and investigate if such analytics adds new outlooks beyond the usual metrics used by lodging enterprises. The study presents quantitative metrics and trend analysis for a 3-year period at this business unit. In addition, the case study provides measures that help management to identify and address inefficiencies, as well as the productivity of its human capital. The study also highlights the benefits of Bridging Practice and Theory.
Ali A. Poorani, William A. Sullivan

Aligning Strategies and Business Analytics

Frontmatter
Chapter 10. Delivering Internal Business Intelligence Services: How Different Strategies Allow Companies to Succeed by Failing Fast
Abstract
This chapter reviews opportunities and issues propelling and limiting the success of business intelligence and analytics services for a company’s internal use. We describe three strategies for providing these services internally (on-premises, cloud, and hybrid) and explore issues of importance in the shaping of current demand and of future offerings by web-based providers. It also discusses opportunities for the development of academic curricula to offer better training to graduate and improve recruiting outcomes for organizations and for the development of more relevant academic research to address topics of current and strategic importance to the firm.
Rubén A. Mendoza
Chapter 11. Aligning Analytics with Marketing Strategy: Using Analytics to Drive Marketing Strategy with New Media Applications
Abstract
This chapter discusses how changes in the business and technological landscape affect marketing practice in industry and academic research. One difference is that marketers have turned to new media and digital marketing tools to understand better their customers and how they interact with their brand. Another development has been the steady convergence between published marketing theory in the academic literature and practitioner research. The chapter also draws attention to how new media marketing and analytics has fostered new insights about the customer journey, such as the creation of the loyalty loop and the need for alignment in marketing strategy. The implications for analytics education are also examined in the chapter with recommendations for curricula shifts and training as they relate to higher demand for and a shortage of qualified graduates. The chapter concludes with a case study, “Einstein Health System: Erectile Dysfunction,” which provides a straightforward illustration of the potential benefits for an organization to align their analytical methods with their marketing strategy.
Lawrence Duke, Atif Ashraf
Chapter 12. Aligning Data Analytics and Strategy in the Chemical Industry
Abstract
Data and analytics are playing a revolutionary role in the chemical industry. This paper provides an overview of the challenges confronting the chemical industry and the opportunities to transform the industry by aligning data analytics and strategy. We look at various facets of the chemical industry and outline the role of data analytics in production and research strategies, as well as in marketing and customer service strategies. Using the case study of DuPont, we provide an example of how applying data and analytics to its precision agricultural technology increased yields and improved productivity. The chemical industry is also successfully implementing analytical techniques used by a variety of other industries such as retailing and finance to create value through differentiation and rethinking customer offerings. We also describe the opportunities that big data and analytics offer the industrial Internet of Things (IoT) strategy to drive performance and growth. Finally, we outline the limitations of data analytics and opportunities for future research in this area and discuss the importance of industry and academia working together to leverage the power of data and analytics in the chemical industry.
Suresh Chandran, Rahul Kasat
Backmatter
Metadata
Title
Aligning Business Strategies and Analytics
Editors
Murugan Anandarajan
Teresa D. Harrison
Copyright Year
2019
Electronic ISBN
978-3-319-93299-6
Print ISBN
978-3-319-93298-9
DOI
https://doi.org/10.1007/978-3-319-93299-6

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