Introduction

In the first white paper of this three-part series on data ownership, I referred to data as an enterprise asset and something that needs to be robustly managed on an enterprise-wide level. This means maintaining data quality while ensuring that data remains clearly understood as it flows between applications and processes during business operations. I also discussed how data ownership is about enterprise data management, and that key requirements for enterprise data management included the need to:

• Establish a common suite of technologies for end-to-end data management
• Dedicate IT personnel to enterprise data management
• Establish policies for data governance

In this second white paper on data ownership, I want to look more in-depth at the first of those key requirements, specifically how an enterprise-wide standard suite of technology can help companies solve the problem of data ownership and enterprise data management.

Corporate Nirvana

Over the years, as systems have rolled out and we have moved from one era of computing to another, the job of managing data has not gotten any easier. In fact, most companies would say that it is getting progressively harder. Looking back to the 1990’s, it was the client/server computing and open systems era that spawned the distribution of applications and databases onto stand-alone servers across the enterprise. I recall one executive commenting that they understood the need to distribute the application logic, “but whose idea was it to fracture the data and scatter it to the wind?”

In operational systems, the client/server and open systems era gave rise to duplication of functionality and fractured subsets of operational data across multiple operational systems and data stores. This in turn caused inefficiencies and overspending in many operational processes. While we have known about this problem for years, we have not been very successful in dealing with it. Some companies purchased enterprise application integration (EAI) products and asynchronous message queuing products to try to keep data synchronized. While this had had some success, applications with no application programming interfaces (APIs) could not be integrated with EAI software, and so batch-file update processing continues to be needed.

However, the arrival of the Internet has made business executives realize that there is potentially a way back to data simplicity. If the web can allow everyone to gain access to common processes, common application services and common information via a web browser irrespective of their location, then why do companies need multiple versions of the same process, multiple applications with duplicate application functionality, and multiple versions of fractured operational data? Corporate nirvana would be to share common operational data across applications and access common services via the Web. It would also be better if core business entity master data was maintained in one place, with changes forwarded to applications that need this data.

This realization that there is a possible way out of the fractured complexity of operational systems has sparked a massive demand for operational data consolidation and integration, as well as master data management. Add to this the need for compliance, and you quickly see why companies are also pushing for common understanding of data with common data names and common data definitions, and also raising the priority of data quality to get trusted and accurate information.

Corporate nirvana would look something like Figure 1 (below) where operational applications share common operational data and use common services to maintain master data such as customer, product and employee attributes. In addition, the operational data and the historical data in business intelligence (BI) systems would be based on common data names and data definitions (metadata) so as to guarantee common understanding. Enterprise data management (EDM) services are available to request data integration, data quality checks and metadata.