Introduction

So far in this three-part series on data ownership, I have discussed what data ownership is, why it is important, what the key requirements of enterprise data management (EDM) are and how companies can address the data management problem by standardizing on a suite of technologies, which I referred to as an EDM suite. In this, the third and final paper in this short series, I want to look at what needs to be done from a strategy perspective to be able to establish personnel and procedures for enterprise data management, and what needs to be done in order to leverage the technologies available in an EDM suite to get maximum return on investment.

Enterprise Data Management Strategies

In the first paper of this series, we outlined three key requirements for enterprise data management. These requirements are:

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

Having looked at the first of these already in the second paper, we now turn our attention to organizational structure and data governance – concepts that are fundamental to any data management strategy.

Organizational Structures for Enterprise Data Management

One of the key appointments any company can make to help get their data under control is the position of a Chief Data Architect. This is often a position overlooked in IT and sometimes not well understood by business. If it does exist, this person must have a business mandate to cause change so that data can be brought under control. Fundamentally, the job of a Data Architect is to understand how data is used in business on an enterprise-wide basis and to formally define the data used. This individual is also responsible for setting policies and procedures for the use of that data, for maintaining data quality, and for ensuring a common consistent understanding of what data means. Ideally, a Data Architect should have extensive experience in the vertical industry that he or she works so that they can clearly discuss data in the context of its business use. Data Architects must also have expertise in data management skills such as:

• Implementing data standards and establishing policies for developers and business users, including defining standard enterprise-wide data vocabularies
• In-depth understanding of the relational model and navigating XML schemas
• Data modeling and modeling techniques such as normalization and star schema multi-dimensional modeling, as well as some fluency in the use of data modeling tools
• Logical and physical database design
• Data profiling and defining rules for data content cleanup
• Understanding of the requirements that regulations and legislation impose on data for the purposes of compliance

Ideally, data architects should have an enterprise-wide remit in the sense that they need to operate across all lines of business when managing data. This is especially important in setting strategy and patterns (best practices) around specific data management processes such as:

• Master data management
• Data profiling and data monitoring
• Data migration and consolidation
• Data replication and change data capture
• Data synchronisation
• Data federation
• Data warehousing and data aggregation
• Data security
• Taxonomy design

Many companies are starting to create centralized IT expertise in business integration by creating Integration Competency Centers so that IT professionals responsible for different types of integration are able to coordinate their work. The data architect is at the center of data management, data quality and data integration and should be a key member of any integration competency center initiative. Figure 1 shows five levels of business integration. Data and metadata integration (and management) underpin and are a key piece of any business integration initiative.