Data Storage Management
Data retention, backup, and disaster recovery are essential to the sustainability of agency asset databases and the continuity of critical, data-informed TAM business processes. Examine and quantify risks and select tiered data storage solutions that align with agency risk tolerance and budget.
Conceptual examples of key components of a disaster recovery plan are provided.
Source Systems and Master Data
Identify and designate source systems for essential agency business information (such as agency assets, financials, contracts, or employees). Create master data from source data to provide a single source of truth for reporting. Protect source data integrity and ensure that changes to source data are reflected in replicated or derivative data sets.
Asset Identifiers and Linear Referencing for Data Linkages
Asset identification and linear referencing schemes are vital to agency database integration. New and existing TAM databases should be structured to provide these standardized data linkages. This practice will enable integration of asset and non asset data for TAM analysis and decision-making.
Establishing these data linkages typically requires programming, however more and more commercial software tools are providing end user utilities to help automate development, decreasing reliance on specialized skillsets and staff.
Across the enterprise, asset and non-asset data are commonly stored in different systems. A data warehouse is a central repository of integrated data that supports efficient reporting and analysis. Data is typically loaded into a data warehouse through automated routines that run on a set frequency based on end user requirements.
The following terms are used within this Section.
Cloud storage has emerged as a useful tool to address rapidly growing data storage needs. In the cloud storage model, physical data storage is managed by external service providers (e.g. Amazon Web Services or Microsoft Azure). Cloud storage often provides lower cost and less maintenance but requires additional data access and security considerations that must be addressed.
A data lake is a single repository of different databases in native form, typically used for data exploration, rather than routine analysis. Consider the end use and such details as storage, security, agility and end user sophistication when implementing a data lake.
A disaster recovery plan documents a set of policies and procedures that support the recovery of data and data infrastructure in the event of a natural or human-made disaster. In the context of asset management, the disaster recovery plan should consider necessary access to data related to “life line” assets such as evacuation routes, utilities and communications.
A linear referencing system is a method of spatial referencing the locations of physical features along a linear element. The features are described in terms of measurements from a fixed point, such as a mile marker or station along a road. Each feature is located by either a point (e.g. a signpost) or a line (e.g. a no-passing zone). A well governed LRS helps ensure spatial relationships between assets held in different databases can be viewed and analyzed.
A few pages that cover the most important steps of the plan and key contacts.Scope and Purpose
An introduction that describes the purpose of the plan and the scope for which is covers along with documentation of authority and approvals and frequency of review and updates required for the plan.Roles and Responsibilities
Descriptions of the key roles and responsibilities of each member of the disaster recovery team and any limitations based on governance and approval thresholds.Response Procedures
Documented processes to be initiated and followed including assessment of the situation, any damages and notifications required based on severity.Documentation Requirements
Clear direction on the documentation of activities that must occur if the plan is activated.