What this book covers
Chapter 1, Where’s My Data and Who’s in Charge?, addresses a common challenge for any enterprise: gaining clarity about its own data, how it is managed and stored, who is responsible for it, and how to make data discoverable.
Chapter 2, We Make Data Business Ready, is where we look at creating a data office, which involves appointing a dedicated data team to take care of enterprise data, make it highly qualitative and business-ready, and enable the core functions of data management.
Chapter 3, Thousands to One – From Locally Siloed to Globally Centralized, looks at how to simplify and streamline data usage and discoverability across an enterprise by executing an end-to-end inventory of data domains and connecting previously disparate data with respective business processes.
Chapter 4, Reactive! Proactive? Predictive, explores the maturity curve for various data services and data management operations – from the reactive and immediate approach for business issues to having a proactive catalog of services and predictive in-flight data corrections.
Chapter 5, Mastering Your Data Domains and Business Ownership, helps you to understand the various data domains across an enterprise and how they could be effectively combined and interrelated, as well as properly owned and responsibly consumed by both data and business teams.
Chapter 6, Navigating the Strategic Data Dilemma, navigates the golden balance between the scalability and cost efficiency of outsourcing work with the natural flexibility of working with in-house data teams, covering the various pitfalls and benefits.
Chapter 7, Unique Data IP Is Your Magic, explores how to develop and advance your company’s data know-how and how data supports the business processes, which is often referred to as the data’s Intellectual Potential (IP). We look at effective ways to protect your data IP and use it for continuous business success.
Chapter 8, The Pareto Principle in Action, covers the famous 80/20 principle, also known as the Pareto principle. It can inspire highly effective frameworks and methodologies and was used to advance the state of data at Microsoft, helping to build innovative, multi-billion-dollar-revenue data products.
Chapter 9, Data Mastering and MDM, covers Master Data Management (MDM). MDM is undoubtedly the cornerstone of modern data management and data governance, but ultimate success with it depends on the underlying business cases and implementation strategy. We will learn about this in this chapter.
Chapter 10, Data Mesh and Data Governance, looks at the Data Mesh approach, which was a much-needed innovation in the recent data architecture space, enabling a federated, de-centralized approach to data governance, ownership, and management. A significant part of this impressive win is attributed to business enablement through change management.
Chapter 11, Data Assets or Data Products?, looks at having a data-product-focused strategy, which is another great example of non-stop innovation with data. To truly unleash the power of product thinking, the relationship between raw data assets and data products must be understood. This chapter helps with that.
Chapter 12, Data Value, Literacy, and Culture, answers questions such as, What makes data distinctively valuable? How can we monetize data and drive immense revenue increases and business growth using existing data? These questions are tackled through the lens of data literacy and data-driven culture.
Chapter 13, Getting Ready for GenAI, talks about how data is everything in AI, as it plays a critical strategic role in any successful AI deployment. Unpacking this notion leads us to identify specific prerequisites for succeeding in a business transformation with data and AI.
