Data Architecture

W.H. INMON DANIEL LINSTEDT Corporate data – the vista of information across the entire corporation. There are many different types of data found in the corporation. The book lays out one perspective of data and describes – at a very high level – how that data is used (and is not used) in the decision-making process of the corporati on. Big Data – what is it and how can it enhance decision making in the corporation. There are different definitions of Big Data. This book takes a very pragmatic view of Big Data then discusses some salient characteristics of it. The most salient characteristic – one not discussed by the vendors – is that of the difference of repetitive and non-repetitive Big Data. Profound differences between repetitive and non-repetitive Big Data is herein called the “great divide.” This book is worth buying for no other reason than simply to understand this “great divide” and its implications to the decision-making ability of the corporation. Data warehouse – the need for corporate integrity of data. One day, corporations awoke to the fact having data was not the same thing as having believable data. They awoke to discover the meaning of “data integrity.” That was the day the enterprise data warehouse (EDW) was born. With an EDW, corporations had the bedrock data on which to make important PREFACE xix and trustworthy decisions. Prior to the EDW, corporations had plenty of data, but the data was not believable. Data vault – the need for managing the change of data over time. Data warehouses evolved over time. The ultimate in the evolution of data warehousing was the discipline and structure known as the “data vault.” There were and are many reasons to have the data vault as the backbone of the systems that require integrity. Operational systems – the need to run the corporation’s dayto- day business. For all the needs of managing very large data volumes and for data integrity requirements, there is (and will continue to be) a need to have systems that run and enhance the day-to-day operations of the organization. Architecture – how the different types of data and the different needs for data are fitted together, in a holistic and a cohesive way. It is one thing to recognize the different needs of perspectives of data in the corporation. It is another thing to envision how the different types of data fit together in a cohesive, holistic manner. on. Big Data – what is it and how can it enhance decision making in the corporation. There are different definitions of Big Data. This book takes a very pragmatic view of Big Data then discusses some salient characteristics of it. The most salient characteristic – one not discussed by the vendors – is that of the difference of repetitive and non-repetitive Big Data. Profound differences between repetitive and non-repetitive Big Data is herein called the “great divide.” This book is worth buying for no other reason than simply to understand this “great divide” and its implications to the decision-making ability of the corporation. Data warehouse – the need for corporate integrity of data. One day, corporations awoke to the fact having data was not the same thing as having believable data. They awoke to discover the meaning of “data integrity.” That was the day the enterprise data warehouse (EDW) was born. With an EDW, corporations had the bedrock data on which to make important PREFACE xix and trustworthy decisions. Prior to the EDW, corporations had plenty of data, but the data was not believable. Data vault – the need for managing the change of data over time. Data warehouses evolved over time. The ultimate in the evolution of data warehousing was the discipline and structure known as the “data vault.” There were and are many reasons to have the data vault as the backbone of the systems that require integrity. Operational systems – the need to run the corporation’s dayto- day business. For all the needs of managing very large data volumes and for data integrity requirements, there is (and will continue to be) a need to have systems that run and enhance the day-to-day operations of the organization. Architecture – how the different types of data and the different needs for data are fitted together, in a holistic and a cohesive way. It is one thing to recognize the different needs of perspectives of data in the corporation. It is another thing to envision how the different types of data fit together in a cohesive, holistic manner.