Master data management (MDM) has been very challenging for enterprises, especially for large organizations who have many existing data warehouses, data marts, and operational data stores, with constantly integration or divesture of business, applications, and network environments. Without effective MDM solution, same data may be collected in different format in different way from different systems, and later distributed to many different operation units, which in turn complicates the data inconsistency and reconciliation issue with data being used in variety of systems, and causes issue of inconsistent management reporting and decision-making.
MDM makes the whole organization able to identify reliable data sources, collect and store data with consistency, distribute and share the master data with integrity through the enterprise systems. For enterprise security management, the most important aspects include asset management, vulnerability management, configuration management, identify and authentication management, information governance, among the others. An effective MDM requires enterprise architecture to enable and enforce the consistent data for all the enterprise applications and environment. It requires the logic data model to be defined and maintained through the information life cycle and distributed consistently for any change. Logic data models need to define data dimensions, and each dimension defines required attributes with consistent data definition. For distribution, systems can provide SOAP web service or REST SOA interface through enterprise service bus (ESB) to provide real-time data distribution, but in many cases, data dump is commonly used due to its simplicity.
Asset inventory should include static assets inventory system that serves as standard control list, and dynamic assets discovery to verify the discrepancy of live environment and rogue devices. Considering the inventory tools may be limited to collect certain data fields in vendor specific format, it is preferable to normalize the data with NIST common platform enumeration (CPE) for system and application representation, or at least normalize the application name, product name, OS and application version and patch notation. Same concept goes for business, organization, location, and people contact info. It needs to be noted that hosts may be multi-homes, with one host having multiple interface and potentially have multiple DNS hostnames if registered with DNS. This requires one more layer of modeling for multi-homed network devices. A well maintained DNS system will also help identify single host for multiple interface IPs. Countermeasure modeling is an important aspect of vulnerability and risk management, as it will help prioritize the risk remediation effort, and reduce un-necessary resource-intensive patching instances so that true risk can be remediated first.
Logical data model design tools help with modeling process, most known products are Sybase PowerDesigner, CA data modeler, and E/R data architect. I personally found PowerDesigner and E/R data architect is easier to use, and E/R data architect license cost is the lowest. The normalized data can be stored in Oracle database, which provides multi-dimensional analytical processing (OLAP) capability.
In all, effective MDM starts with understanding of corporate master data requirement and modeling, followed by effective system architecture, tools, and process for DNS management, inventory collection, data transformation and normalization, interoperable schema and data redistribution. Ensuring consistency and correlation among corporate data vaults (warehouses, marts, stores) using standard are key to the reusability of those master data sets, as any new operation process or system can aggregate necessary data sets and meaningfully correlate them for specific purpose. For this purpose, NIST security content automation protocol (SCAP) should be adopted by vendors and industries so that the security data can be standardized and interoperable among external feeds and internal systems.