Introduction

Drawing from his experience, Nigel Vaz’s guide aimed at the leaders of companies in the transformation process, shows how they can maintain their ability to compete and be recognised. Vaz heads up Publicis Sapient, is a digital strategy specialist, and his focus is on consulting major global organisations in their transition to the digital world. Based on his experience in helping his clients identify emerging trends in technology to transform their businesses, such as AI, automation, and cloud computing. Besides technology, he emphasized the importance of leadership and organizational culture in shaping the digital competence business model. The book intended to provide a step-by-step framework that companies can follow to execute digital business transformation effectively. Vaz uses real-world examples and case studies to elaborate on the practical potential of steps involved in reinventing businesses and achieving competitive advantage. Given building a business digital modelely useful. The disruptive potential for current transformation been more threatening to traditional companies. Here is the Vaz’s framework guiding companies to build effective digital SPEED (S: strategy; P: product; E: engineering; E: experience; D: data).

Key Concepts & Takeaway

Vaz outlines a framework for digital transformation consisting of strategy, product, engineering, experience, and data. Businesses that emphasize these five areas build the foundations for agile transformation. We will expand on these ideas, giving examples and use cases to illustrate how they work in business practice.

Strategy: From competition to customer-centricity

Vaz underlines that the source of strategic thinking can no longer be competitors but must be customers. This is about understanding and fulfilling customer needs rather than responding to competitors. Your strategy should inform where to play (which markets to enter), how to win (how to succeed), and define any investments or decisions you might need to make for new product development or service delivery. For example, the real-world case in action was Netflix. They started off by competing with Blockbuster and similar rental-by-post businesses. They then shifted into streaming by looking at customers as they watched, getting additional data such as viewing times and behaviors, launching additional content, and continually adapting to changing customer tastes. Subsequently, Netflix became the leader of digital media while many of its competitors were left behind, clinging to outdated business models instead. For consumer businesses that have potential by mining AI-driven insights, firms such as retailers chart buying trends and tailor marketing to customers to transition from competing on speed to an investment and intimate connections with clients.

Product: Shifting from a project to a product mindset

Vaz argues that we need to move from a project thinking mode, where we think in a linear way about timelines and budgets, to a product mindset, where we think about a cycle of continual improvement, speed, quality, and value. This means looking at a product as something we build over time and refining to meet a customer's need rather than an end product. For instance, AWS does not treat its offerings, such as cloud services, as one-off products. All their products improve and iterate consistently, with new features often released daily to weekly and customer feedback incorporated progressively. Over time, the manufacturing company will shift from a product launch mindset, tied to the project, to one that develops for continuous improvement of production over time – constantly updating, enhancing the user experience and adding greater value to internal teams and customers.

Experience: Multidisciplinary and iterative

Multidisciplinary teams should design the customer experience and work iteratively to deliver continuous improvement. Be able to innovate and accelerate the pace at which experience exists. The other side of the discovery coin is an overarching efficiency, says Vaz. The secret is speed: "It is not so much about creating a new experience; it is about creating a new experience and then continually updating it to respond to what the market demands." For example, consider Tesla, one of the benefits of an "over-the-air" (OTA) software update is that it will continuously improve the driving experience of Tesla owners, from adding new self-driving features to increasing the battery life of the car. In the banking industry, companies can apply a multidisciplinary approach and combine data science, design, and software engineering to deliver a digital banking experience, such as releasing continuous flows of features to the app based on user feedback, thereby providing an experience that helps streamline and personalize mobile banking for customers.

Engineering: Breaking silos and embracing cloud and microservices

The key is to treat technology as a source of growth, not a cost center. Spotify's product is built using a microservices architecture: small, autonomous teams focus on different pieces of the product so that updates can be deployed at any time without having to take down the service for long periods, all with a programmable platform that is continuously being updated. Companies such as Netflix and AWS have broken up the retail functions related to the sale of products – such as product recommendations, payments and customer reviews – into microservices. Once separated, if an e-commerce company wants to introduce a new feature, like a new payment gateway, they don’t need to touch the rest of the site’s infrastructure, it could constantly implement new features without disrupting the rest of the website.

Data: treating data as a critical intangible asset

Data is not mere information, it is a precious asset of the business, creating business value for clients and sharpening the decisions. For instance, Google prominently uses its data as an intangible asset, providing a robust infrastructure for extracting, storing, and processing large data. Google keeps improving its search algorithms and ad-targeting models based on user behavior. In the healthcare industry, it could use connected data to infer an individual's needs, predict when to perform interventions, and provide the right resources at the right time and place. With real-time data and machine learning models, hospitals can improve lives through better patient care.