From the Preface
Data management is an emerging and disruptive subject. Datafication is everywhere. This transformation is happening all around us: in smartphones, TV devices, ereaders, industrial machines, self-driving cars, robots, and so on. It’s changing our lives at an accelerating speed.
As the amount of data generated skyrockets, so does its complexity. Disruptive trends like cloudification, API and ecosystem connectivity, microservices, open data, software as a service (SaaS), and new software delivery models have a tremendous effect on data management. In parallel, we see an enormous number of new applications transforming our businesses. All these trends are fragmenting the data landscape. As a result, we are seeing more point-to-point interfaces, endless discussions about data quality and ownership, and plenty of ethical and legal dilemmas regarding privacy, safety, and security. Agility, long-term stability, and clear data governance compete with the need to develop new business cases swiftly. We sorely need a clear vision for the future of data management.
This book’s perspective on data management is informed by my personal experience driving the data architecture agenda for a large enterprise as chief data architect. Executing that role showed me clearly the impact a good data strategy can have on a large organization. After leaving that company, I started working as the chief data officer for Microsoft Netherlands. In this exciting new position, I’ve worked with over 50 large customers discussing and attempting to come up with a perfect data solution. Here are some of the common threads I’ve identified across all enterprises:
- An overarching data strategy is often missing or not connected to the business objectives. The discussion mostly pivots to technology trends and engineering discussions. What is needed is business engagement: a good strategy and well-thought-out data management and analysis plan that includes tangible value in the form of business use cases. To make my point: the focus must be put on usage and turning data into business value.
- Enterprises have difficulties in interpreting new concepts like the data mesh and data fabric, because pragmatic guidance and experiences from the field are missing. In addition to that, the data mesh fully embraces a decentralized approach, which is a transformational change not only so for the data architecture and technology, but even more for organization and processes. This means the transformation cannot only be led by IT; it’s a business transformation as well.
- Enterprises find it difficult to comprehend the latest technology trends. They’re unable to interpret nuances or make pragmatic choices.
- Enterprises struggle to get started: large ambitions often end with limited action; the execution plan and architecture remain too high-level, too conceptual; top-down commitment from leadership is missing.
These experiences and my observations across a range of enterprises inspired me to write this second edition of Data Management at Scale. You may wonder what motivated me and why this book is worth reading, over the first edition—let’s take a closer look.
