Data standards are the predetermined merits that govern how data is managed, used, represented, formatted, defined, transmitted, structured and labeled. They refer to technical specifications or registered agreements that describe how data should be stored or exchanged in different systems, so that various problems and obstacles can be eliminated. Data standards define the approach and practices for developing, approving, and instituting compliance with standards of representation, access to data, and data distribution. All too often, the same areas have been surveyed and the same data collected over and over again in slightly different ways.
Several components can be brought together to describe a more complete “data standards package”, and a system or initiative that defines a large but unified collection of components that will be used and packaged together for a wide variety of purposes is referred to here as a data standards framework. In addition, data standards are important for maintaining consistency within sets of codes and also allow data and code to be reused in different projects and systems. In addition, they allow the reuse of data elements, reducing redundancy and improving reliability, while reducing costs. It is important for business representatives to take an active role through data governance when considering and approving data standards.
When data standards cannot be physically implemented, they should be included in the documentation of the implemented environment (see Managing Metadata) with cross-references between the standard and the existing system architecture to ensure the traceability of critical attributes throughout the life cycle of patient demographics (see Data Lifecycle Management). Basically, data access standards are useful for creating, retrieving, modifying, or updating valuable information from a service or application. Several components come together to create a data standards package, and a large number of components packaged together is called a data standards framework. We want to bring together leading expert voices from the world of data, sanitation and technology to oversee the creation of a data standard for urban sanitation data.
Some of the common components of data standards include the data type, the identifiers, the format, the schema, or the application programming interface (API). In this case, “the data standards are not intended to describe the minimum qualification criteria that data must meet, but rather to describe the technical specifications that allow the collection and exchange of consistent and interoperable data in specific environments.” Industry standards for data architecture are established to improve interoperability vertically along the supply chain, as well as horizontally between peer organizations. For organizations that create their own systems, a standards-based approach will avoid many potential problems when it comes to integrating data, migrating legacy systems and redesigning existing data warehouses. Imagine if sanitation organizations working in the same city could share data points and create a map of sanitation needs in a city.