What Is a Data Strategy?

Technology is constantly changing, and new technologies are regularly being introduced. Business has been revolutionized by various technologies over the past few decades-namely, data science/artifical intelligence (AI) and blockchain. New technologies bring many opportunities with them, but they can also pose challenges for businesses, such as creating new and updated business models and finding the best adoption and digital transformation strategies. This books offers a guide to data science/AI and blockchain, two of the most relevant and important technologies for businesses today. Various topics are covered, including data science processes, AI-based businesses, and types of blockchain. Simple, straighforward descriptions of the technologies are provided along with more thorough explanations on how to effectively use them. By utilizing data science/AI and blockchain, businesses can stay competitive and be a catalyst for positive change. Chapter 3 builds on the previous chapter and explores data strategies. A data strategy should be constantly updated, cover technical and organizational aspects, and ultimately point toward and work with the business strategy. Data strategies can offer additional revenue and a competitive advantage to companies. Guidelines for how to create a data strategy are provided, including starting with the business strategy, building data use cases, understanding data architecture and data governance, and managing AI projects. The data science hierarchy of needs model is described. CRISP-DM is a process used for planning data science projects; its six main phases are business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Tesseract Academy's 2-actor process is another popular process in data science and focuses on the problem definition, data management, the solution to the problem, and creating value through actionable insights. Several case studies are offered throughout the chapter.