AMPLIFY VOL. 37, NO. 6
Analytics play a crucial role in generating economic value and fostering innovation by providing organizations with valuable insights (including information about customers, environmental scanning, and more) and data-driven decision-making capabilities.1 It helps organizations identify trends, patterns, and opportunities that can lead to increased efficiency, cost savings, and revenue generation. By leveraging analytics, organizations can optimize their operations, improve customer experiences, develop innovative products and services, and gain a competitive edge.
Despite investment in analytics being a top priority for many organizations, a 2023 Statista survey found that only 37% of organizations said their efforts to improve data quality had been successful, highlighting an ongoing challenge faced by organizations across industries.2 On average, between 60% and 73% of all data within an organization goes unused for analytics.3
In this issue of Amplify, we delve into the ways organizations are developing analytical capabilities that lead to valuable insights and create business value. We also explore the shift from being a data-driven organization to a data-centric one. The latter places data science at its core; data is a primary, permanent asset used as the starting point to determine organizational action.4
As we explore this shift, it becomes evident that organizations that exploit analytics (and data in general) tend to view it as more than a means to an end — they harness it to create a data-centric culture, establish synergies within and across functions, and deepen relationships with myriad stakeholders.
In This Issue
This issue of Amplify delves into the insights of organizations that have used analytics to generate economic value and foster innovation in products, processes, and services.
Bill Schmarzo opens the issue with a thought-provoking article about how companies can unleash business value and economic innovation through AI. Drawing on the seminal work of Adam Smith (author of The Wealth of Nations), Schmarzo explains that “the essence of economics is the creation, consumption, and distribution of wealth — or value” as a baseline for economic innovation. The author brilliantly balances his extensive industry experience and published works to highlight cultural empowerment as a way to foster an inclusive environment to demonstrate value. He identifies 10 critical characteristics of cultural empowerment in the context of leveraging AI and generative AI (GenAI) for economic innovation. Schmarzo also offers the “Thinking Like a Data Scientist” methodology to help business leaders maximize AI to create new sources of customer, product, service, and operational value. The article concludes with an example of integrating GenAI with the methodology to create an economic innovation force multiplier.
Next, Hossein Sahraei, Ramila Peiris, Luc Nguyen, and Olivier Moureau describe how global healthcare company Sanofi transitioned from reactive modes of data analytics (descriptive, diagnostic) to a proactive approach through prescriptive analytics. The authors, who are part of Sanofi's process data science team, provide a refreshing account of their experiences, challenges, and successes, beginning with an acknowledgment that digital transformation goes beyond adopting new technologies to fundamentally change how organizations operate, think, and innovate. They highlight the importance of developing a growth mindset, challenging established norms, and seeing uncertainty as a catalyst for innovation. The authors also explain how the organizational strategy prioritized practicality (an approach based on business needs and limitations), scalability (a framework that can be used in different areas with minimal effort), and sustainability (manageable execution, maintenance, and updates) in product design and deployment. The article reports on the economic value of empowering decision makers, along with benefits such as increased job satisfaction and helping workers maintain a healthy work-life balance.
In our third article, Antoine Harfouche explains how AI and big data analytics enable smart farming, focusing on the hydroponic forage market. With a current market value of more than US $5 billion, hydroponic systems that leverage technologies like AI, Internet of Things, satellite imagery, and data analytics can optimize environmental controls, improve resource management, and enhance crop resilience. He also outlines the advantages and disadvantages of several such technologies. By combining data, including genomic (epigenomics, transcriptomics, metabolomics), phenomic (plant height, leaf shape, angle, growth trajectory), and environmental (weather and soil, solar radiation, relative humidity), AI can enhance predictive accuracy and decision-making in breeding programs to enhance climate resilience. Harfouche explains the importance of the data value chain, which consists of data capture, data storage, data transformation, data analysis, data interpretation, and feedback. These stages are then instantiated into a framework to demonstrate how AI and big data analytics can be used to improve hydroponic cultivation and improve the sustainability of hydroponic farming. The article concludes with a call for increased collaboration among researchers, farmers, and policy makers to harness these technologies to create a sustainable and secure food production system for the future.
Next, Enjoud Alhasawi, Denis Dennehy, Yogesh Dwivedi, Guoqing Zhao, and Sean Coffey highlight a growing concern about how supply chain disruptions negatively impact both developed and developing countries. The authors provide insights from practitioners at four companies located in Ireland and Kuwait that operate in large, complex agri-food supply chains. They focus on understanding how AI enables resilience in agri-food supply chains. Building on the four dimensions of supply chain resilience (readiness, responsiveness, recovery, and adaptability), the authors show how the companies used robotics and expert systems to mitigate the threat of supply chain disruptions. Drawing on secondary data, they acknowledge that other functions of AI (machine learning, machine vision, natural language processing, and speech recognition) can be applied to various elements of the supply chain, including forecasting, optimization of processes, supplier selection, automation, and decision support for configuration, design, and planning. Anticipating that future supply chain disruptions will threaten the global agri-food sector, the authors call for concerted efforts between industry, the public sector, and academic researchers to build more resilient supply chains.
Finally, Daniel J. Rees, Roderick A. Thomas, Victoria Bates, and Gareth Davies wrap up the issue by examining the transformational impact that healthcare-related technologies (e.g., AI, wearable sensors, clinical and genetic data) have on the healthcare and pharmaceutical industries. These technologies can potentially transform healthcare business processes, resulting in faster, more efficient decision-making, human-error reduction, and accelerated product development cycles that can lead to faster product launches. The authors gained insights from 48 senior managers in healthcare and pharmaceutical organizations to both identify best practices and understand the challenges related to using healthcare-related technologies and data-centric decision-making to deliver value to stakeholders. Best practices, such as governance (memorandum of understanding), incentives (monetary and nonmonetary), scalability, and collaboration between pharmaceutical makers and technology companies, are identified as key enablers. Such practices enable stakeholders to mitigate challenges like culture (trust, reputation, time, risk aversion), governance (contracts), and scalability. The article concludes with recommendations to ensure the right individuals choose tools and processes that can lead to successful partnerships and transformational initiatives for the benefit of patients, society, and the wider economy.
Analytics is a powerful tool for driving growth, fostering innovation, and creating economic value in today’s increasingly data-centric business world. We hope the articles in this issue advance your understanding of how organizations from various industries are identifying use cases for analytics as a catalyst for economic value and innovation and provide you with a blueprint for guiding your teams, organizations, and stakeholders to successfully navigate turbulent environments.
References
1 Dennehy, Denis, Bill Schmarzo, and Mouwafac Sidaoui. “Organising for AI-Powered Innovation Through Design: The Case of Hitachi Vantara.” International Journal of Technology Management, Vol. 88, No. 2-4, November 2021.
2 “State of Data and Analytics Investment at Companies Worldwide in 2023.” Statista, 14 March 2024.
3 Gualtieri, Mike. “Hadoop Is Data’s Darling for a Reason.” Forrester, 21 January 2016.
4 “Data Centricity.” Egnyte, accessed June 2024.
