Developing a Path to Data Dominance
Strategies for Digital Data-Centric Enterprises
- 2023
- Book
- Authors
- Arthur Langer
- Arka Mukherjee
- Book Series
- Future of Business and Finance
- Publisher
- Springer International Publishing
About this book
Most existing companies struggle currently because they lack the tools and strategies to move product departments into independent platforms that can be retrofitted to form dynamic new products based on consumer demands. This book provides managers and professionals with the necessary approaches for designing software and hardware architectures to support data platform organizations. Specifically, it demonstrates how to automate the decomposition of existing platforms into smaller parts that can be reused to form new variations. This task requires significant analysis and design methodologies and procedures to create an infrastructure based on data as opposed to products. These new knowledge bases allow data-centric professionals to pursue actions that can better predict and respond to the unexpected.
Featuring case examples from companies such as Lego, FedEx, General Electric (GE), Pfizer, P&G and more, this book is appropriate for C-level executives engaged in the digital transformation of their firms; entrepreneurs of digital platform companies; and senior software engineers that need to design Internet of Things (IoT) devices and integrate them with block chain and multi-cloud architectures. In addition, this book is also useful for graduate-level coursework in data science.
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Table of Contents
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Frontmatter
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1. Introduction to Data Dominance
Arthur Langer, Arka MukherjeeThe chapter 'Introduction to Data Dominance' delves into the rapidly evolving value of data over the past decade, highlighting how data-driven strategies can lead to exponential growth and disruption in business and society. It discusses the core definitions of data dominance, the technological advancements driving this shift, and the challenges that come with it. The text emphasizes the need for new leadership and organizational structures to harness the power of data effectively. It also provides insights into how data-dominant companies like Amazon and Facebook have achieved unprecedented growth through strategic data use. The chapter concludes by outlining the future of data dominance and the transformative changes it brings to industries and society.AI Generated
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AbstractThis chapter provides readers with an overview of the book, specifically describing the fundamentals of data as dominant and how to maximize data toward strategic advantage over competitors. Sections cover the reasons why certain companies have achieved “hyper” growth while others are limited to linear expansion. This concept is explained further by presenting the “hockey stick” graph that shows how specific digital companies pull away from their competitors by establishing a data-enriched infrastructure that captures market share over time.The chapter also discusses the evolution of data architecture and its relationship to achieving data dominance by investing in IoT, distributed cloud, and mobility infrastructures. Data dominance is also associated with the s-curve and its relation to predicting product obsolescence and its effect on leadership style. -
2. The Digital Data-Centric Enterprise: Case Studies
Arthur Langer, Arka MukherjeeThis chapter delves into the complexities and successes of businesses transitioning from traditional product-focused models to data-driven platforms. It examines case studies such as Pfizer, Lego, and FedEx, which successfully navigated digital transformations, and GE Digital, which faced significant challenges. The chapter emphasizes the importance of a hybrid model, rapid platform conversion, and combating competitive imitators. Additionally, it discusses the role of intrapreneurial leadership and multiple S-Curve journeys in maintaining market relevance, as exemplified by P&G. The chapter concludes by highlighting the critical factors for successful digital transformation and the risks associated with failure to adapt.AI Generated
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AbstractThis chapter provides readers with an understanding and case studies of companies that were launched as data platforms from their inception. We also provide cases of legacy companies that successfully and unsuccessfully attempted digital transformation to become a data platform. Our objective is to provide readers with more context of best practices, how the s-curve affects decision making, and how companies can disrupt themselves by initiating accelerated growth strategies. -
3. The Product Strategy
Arthur Langer, Arka MukherjeeThe chapter 'The Product Strategy' delves into the intricacies of developing an effective product strategy, emphasizing the importance of a growth-oriented approach and understanding the business capability landscape. It outlines four fundamental steps: establishing a growth-oriented product strategy, comprehending the business capability landscape, creating digital twins, and linking the product strategy to the data strategy. The chapter also discusses the vision for differentiation, strategic themes, environmental factors, and company factors, all of which are essential for creating a robust product strategy. Additionally, it explores the creation of digital twins, which are virtual representations of physical objects or processes, and their benefits in predicting failures and optimizing performance. The chapter concludes by highlighting the importance of hyper-automation and its role in streamlining business processes. This detailed exploration makes the chapter a valuable resource for professionals seeking to enhance their product strategy and data management skills.AI Generated
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AbstractCreating a product strategy requires multiple steps that start with growth opportunities and integrating it with integrating data assets. This chapter describes the four essential steps required to launch new data products that can exploit market opportunities. We also advocate using the Business Capability Matrix as the foundation to address the digital touchpoints, understand the digital experience, offer an effective commerce journey, ensure supply chain capabilities, and build supporting infrastructure. We also introduce the concept of hyper-automation as the basis for supporting AI and ML. -
4. Data Strategy for Exponential Growth
Arthur Langer, Arka MukherjeeThe chapter begins by emphasizing the need for organizations to become data-centric to achieve exponential growth. It delves into the concept of data strategy and how it can be used to drive business transformation. The author discusses the challenges and opportunities of data-centricity, highlighting the importance of having a well-defined data strategy that aligns with business objectives. The chapter also covers the historical evolution of databases and enterprise data modeling, providing a thorough understanding of the foundational elements required for building a robust data strategy. Additionally, it explores the concept of exponential product portfolios and how companies can leverage data to grow their market share rapidly. The author emphasizes the need for continuous testing and failure-fast approaches to identify successful products and enhance competitive advantage. The chapter concludes by discussing the importance of prioritization and automation in data strategies to achieve exponential growth, making it a valuable resource for professionals looking to transform their organizations through data-driven strategies.AI Generated
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AbstractThis chapter explains the importance of becoming a data-centric organization. We posit that data centricity is the core of successful digital transformation and dependent more on a culture that uses data assets as the basis for operating all aspects the business. Indeed, the key for success is to have the right social culture that consistently explores hyper-automation in every aspect from creations to delivery. We also provide a history of data storage and how enterprise data modeling established the existing repository of information in most legacy companies. These sections focus on educating the reader on the intricacies of how data have been collected and stored in a logical model. The chapter then expands on how to transform these traditional databases into a more robust data environment that can support a scalable data strategy. -
5. Organizing the Data Ecosystem
Arthur Langer, Arka MukherjeeThe chapter 'Organizing the Data Ecosystem' delves into the intricate process of creating digital products by selecting and assembling data components through APIs and user interfaces. It highlights the challenges faced by data-centric companies in maintaining data integrity and alignment between their physical and logical business operations. The author discusses the evolution of data storage systems and the importance of standardizing naming conventions to ensure data interoperability. The chapter also introduces the concept of business semantics and knowledge graphs as advanced methods for managing and linking data assets, enabling the creation of trusted and comprehensive data products. Additionally, it explores systematic approaches to data organization, emphasizing the need for automated tools and standardized processes to achieve a well-managed data environment.AI Generated
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AbstractBuilding on the concepts introduced in Chap. 4, we provide a methodology on how to design digital products that use data assets in a way that can allow businesses to become more dominant in their markets. The chapter covers typical data architecture techniques, approaches, and challenges to building a robust and dependable data asset library. We focus on how to move data from the real or physical world and map that data to a logical model that was introduced in Chap. 4. Further methods of identifying related data assets are discussed including using a semantic model to remove data ambiguity and linking data into knowledge graphs which create intelligent repositories that can support business users to apply AI searches and ML algorithms. -
6. Building Data-Centric Products
Arthur Langer, Arka MukherjeeThe chapter delves into the innovative approach of data-centric product development, where data is considered the primary asset. It introduces the concept of digital twins, which are virtual representations of physical objects or processes, and discusses their applications in various industries. The integration of enterprise data assets with product strategy is highlighted, emphasizing the importance of a data fabric architecture that optimizes data management and access. The chapter also explores the use of minimal viable products (MVPs) as a strategic methodology for quick product development and feedback gathering. Additionally, it covers the significance of knowledge graphs and active metadata in enhancing data interoperability and decision-making. The text provides a detailed roadmap for building data-centric products, making it an essential read for professionals seeking to leverage data for more efficient and scalable solutions.AI Generated
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AbstractThe purpose of this chapter is to provide guidance on how to operationalize a data-centric environment. This requires the decomposition of many legacy applications to what is known as a functional primitive program that provides reusable functionality. In other words, these functional primitive programs represent the “Lego” parts we discussed earlier. We also provide more detail on how to design digital twins which represent the logical replication of what individuals do physically. Our emphasis is that these reusable functional primitive Lego parts must be closely coupled with the data they need to accomplish their tasks, both input data and output results. We examine the digital business technology integration layer that depicts the components of data-centric products and how they interface with the data fabric infrastructure. The data fabric provides the necessary administrative functions to support for the products to complete their operations. The chapter also discusses the minimal viable product (MVP) which is a valuable way to test-launch and test new products and services. -
7. Culture: Friction in Scaling the Product Portfolio
Arthur Langer, Arka MukherjeeThe chapter delves into the cultural and structural challenges of transitioning to a data-driven platform, emphasizing the need for new thinking and organizational structures. It discusses the failure of GE Digital's transformation and the evolution of organizational learning theories. The text explores the role of Communities of Practice (CoP) in fostering organizational resilience and the importance of data-centric mindsets. It also addresses political process barriers, diversity in people and ideas, and the unique attributes of Generation Y and Z in the digital workforce. The chapter concludes by highlighting the resilience of data platform companies in the face of unexpected disruptions and the proliferation of data in the digital economy.AI Generated
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AbstractBecoming a data platform company is not limited to transforming just the technical infrastructure. Indeed, digital transformation requires the evolution of management and staff. Many organizations have failed to become digital because they could not transform their culture. Many of these challenges were presented in Chap. 2 particularly with the GE case study. This chapter offers a roadmap of how to approach cultural transformation which includes dealing with: political barriers, lack of diversity in people and ideas, finding the right employees, assimilation of different generations of staff, lack of operational resilience, and fear of change. -
8. Alignment: Data Strategy Management and Leadership
Arthur Langer, Arka MukherjeeThe chapter 'Alignment: Data Strategy Management and Leadership' delves into the crucial role of mindset in the digital age, highlighting the need for organizations to evolve beyond traditional structures. It introduces a best practice approach for aligning organizations to launch and execute data strategy plans, drawing on principles for data-dominant firms and a Product Management Framework. The framework includes five decision points: Product Planning, Development, Introduction, Lifecycle Management, and Leadership. The chapter also discusses the importance of cross-departmental collaboration, intrapreneurial leadership, and the challenges of digital transformation in legacy companies. It concludes by emphasizing the need for great executive leadership to make all components work together effectively.AI Generated
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AbstractThis chapter defines and describes a management and leadership approach to executing a data strategy plan. We cover various principles that all managers should prescribe and explain Gartner’s Product Management Framework as an example. Therefore, the chapter provides a play book that can be followed during the life cycle of each phase from planning to execution. We also delve into a number of product leadership protocols including managing cross-departmental collaborations and measuring business unit performance and executive leadership techniques. Using Reis’ research on lean startups, we define the role of an “intrapreneur” to manage the Build-Measure-Learn model. -
9. Effects of Wireless Communication and IoT on Data Aggregation
Arthur Langer, Arka MukherjeeThe chapter delves into the transformative effects of 5G wireless communication and IoT on data aggregation and platform design. It discusses the technical impact of 5G on data architects, including improved data rates, reduced latency, and enhanced IoT battery life. The text also explores the expansion of IoT devices and the decentralization of data centers, emphasizing the importance of edge computing. Additionally, it highlights the indirect effects of 5G on web development, connectivity, and AI/ML applications, presenting a comprehensive overview of the new capabilities and challenges in the 5G era. The chapter concludes by discussing the need for a performance-focused methodology and the role of data architects in this evolving landscape.AI Generated
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AbstractIt is important to understand how 5G wireless affects data architecture. This chapter reviews 5G’s technical impact on performance and data collection, discusses how the market will likely react to wireless performance, and ways 5G technology can be leveraged to support data platforms. We also address how the wireless revolution increases performance in a mobile environment, and raises security concerns while lowering latency. The Internet of Things (IoT) represents the physical devices that will collect increased amounts of data for AI and ML processing. The chapter discusses how IoT will increase real-time processing as well as its ability to reduce unscheduled network failure. We also provide direction to data architects when considering alternative communication models that depend on business needs and industry requirements. -
10. Blockchain Data Architecture and Cyber Security
Arthur Langer, Arka MukherjeeThe chapter delves into the architecture of blockchain data and its pivotal role in bolstering cyber security, particularly for IoT systems. It traces the evolution of blockchain from linked list data structures, highlighting its unique design that tracks all transactions and updates all members of the chain. The text explores various types of blockchain proofs, such as Proof of Work and Proof of Stake, which enhance security by preventing transaction modifications. It also discusses the challenges of blockchain, including latency and performance issues, and the need for constant architectural changes to combat adaptive threats. The chapter further examines the growth and potential of blockchain technology, particularly in industries like banking, healthcare, and property records. It provides a balanced view, acknowledging both the advantages, such as accuracy in verification and decentralization, and the disadvantages, like increased costs and scalability challenges. The text also offers insights into the role of data architects in designing secure applications and the importance of integrating security at multiple levels to reduce risk. It concludes by emphasizing the dynamic nature of cyber security and the need for continuous architectural change to protect against evolving threats.AI Generated
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AbstractThis chapter presents an overview of the architecture of blockchain and its role in the mobile networks of the future. It defines each type of blockchain and shows how blockchain maximizes security using a ledger-based design. We also highlight the advantages of blockchain and its importance in designing data platform architecture and why data architects need to participate in cybersecurity design of data platforms. These new roles and responsibilities will require data architects to develop new skillsets. -
11. Transforming Legacy Systems to Data Platforms
Arthur Langer, Arka MukherjeeThe chapter 'Transforming Legacy Systems to Data Platforms' delves into the complexities of modernizing legacy systems, which are often perceived as old and antiquated applications operating on mainframe computers. It defines legacy systems broadly and discusses the relationship between legacy systems and packaged software systems, especially in the context of IoT devices, blockchain products, and cloud computing. The author provides guidelines on determining whether to replace, enhance, or leave legacy systems as is, and offers detailed procedures for each approach. The chapter also covers the integration of third- and fourth-generation legacy systems with modern data architectures, emphasizing the importance of separating data and processes. Additionally, it addresses the challenges of converting legacy data formats and screens, and the use of gateways for phased migration. The chapter concludes with a step-by-step template for legacy migration, including data and application integration, and the importance of acceptance testing.AI Generated
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AbstractThis chapter outlines the process of interfacing data platforms with pre-existing applications systems called legacies. Issues of product fulfillment, connectivity of legacy databases and processes, and integration of multiple systems architecture are covered. The objective of Chap. 10 is to set out a detailed pathway to ultimately converting legacy systems within the architecture required for building robust data platforms. We recognize that existing systems cannot be converted overnight and that data architects need to build “legacy links” that keep both the old and new systems functioning, while feeding a central repository of data.This chapter also provides the necessary processes, recommended procedures, and reporting techniques that support higher rates of project success. Many projects have suffered because the management was not able to appropriately manage the contracted vendors. Organizations make the mistake of assuming that outsourced development and management is a safeguard for successful project completion. They must understand that third-party vendors are not a panacea for comfort and that rigorous management processes must be in place in order to ensure a successful project. -
12. Conclusions
Arthur Langer, Arka MukherjeeThis chapter concludes a comprehensive guide on the importance of data dominance in building new data platform systems. It emphasizes the critical role of data platform architecture in driving hyper-automation and revenue growth, and the necessity of finding and defining all data within an organization. Cross-business interfaces are highlighted as crucial for achieving an enterprise-level platform that supports all business aspects. The chapter also underscores the importance of decomposing large applications into reusable 'Lego Parts' for a successful product strategy. Emerging technologies like 5G, IoT, blockchain, and Cloud are identified as central architectural components. Additionally, the chapter explores the challenges of integrating legacy systems, the vital role of cybersecurity, and the need for a data-centric workforce. Case studies of both successful and failed digital transformations are presented, with a focus on the cultural shift required for success. The chapter also addresses political challenges and the evolution of wireless communication, emphasizing the need for new leadership and product management frameworks to navigate these complexities. Overall, the chapter provides a business pathway to achieving success in the data platform world, offering valuable insights into the benefits and challenges of this transformative journey.AI Generated
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AbstractThe purpose of this book was to provide an understanding and roadmap for the reader on the importance of data dominance when building new data platform systems. As discussed throughout the book, we emphasized several key issues that appear to be critical components in every stage of developing and implementing a data strategy: -
Backmatter
- Title
- Developing a Path to Data Dominance
- Authors
-
Arthur Langer
Arka Mukherjee
- Copyright Year
- 2023
- Publisher
- Springer International Publishing
- Electronic ISBN
- 978-3-031-26401-6
- Print ISBN
- 978-3-031-26400-9
- DOI
- https://doi.org/10.1007/978-3-031-26401-6
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