Developing a Path to Data Dominance

Inhaltsverzeichnis

1. Frontmatter

2. 1. Introduction to Data Dominance

   Arthur Langer, Arka Mukherjee

   Abstract

   This 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.

3. 2. The Digital Data-Centric Enterprise: Case Studies

   Arthur Langer, Arka Mukherjee

   Abstract

   This 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.

4. 3. The Product Strategy

   Arthur Langer, Arka Mukherjee

   Abstract

   Creating 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.

5. 4. Data Strategy for Exponential Growth

   Arthur Langer, Arka Mukherjee

   Abstract

   This 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.

6. 5. Organizing the Data Ecosystem

   Arthur Langer, Arka Mukherjee

   Abstract

   Building 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.

7. 6. Building Data-Centric Products

   Arthur Langer, Arka Mukherjee

   Abstract

   The 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.

8. 7. Culture: Friction in Scaling the Product Portfolio

   Arthur Langer, Arka Mukherjee

   Abstract

   Becoming 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.

9. 8. Alignment: Data Strategy Management and Leadership

   Arthur Langer, Arka Mukherjee

   Abstract

   This 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.

10. 9. Effects of Wireless Communication and IoT on Data Aggregation

    Arthur Langer, Arka Mukherjee

    Abstract

    It 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.

11. 10. Blockchain Data Architecture and Cyber Security

    Arthur Langer, Arka Mukherjee

    Abstract

    This 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.

12. 11. Transforming Legacy Systems to Data Platforms

    Arthur Langer, Arka Mukherjee

    Abstract

    This 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.

13. 12. Conclusions

    Arthur Langer, Arka Mukherjee

    Abstract

    The 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:

14. Backmatter