Data Standardization: Technical Barriers and the Role of Government - Part 4

Data standardization involves setting standards related to the data value chain. The most commonly used data standard is the application programming interface (API)，which are computer protocols that define how software components communicate with each other. Moreover, the API alleviates the problem of insufficient data by describing the types of data that can be retrieved, how to retrieve it, and share the format of it. They can also include associated metadata that describe the attributes or semantics of data, enabling users to share the meaning of different data points.

Data standardization may reduce barriers for users to use data. First, it can regenerate metadata uncertainty by requiring data semantics to follow some specifications and rules. Secondly, data standards can also reduce the obstacles of data conversion. Finally, data standardization can reduce the problem of data shortage. Of course, data standardization is only a way to solve the obstacles of data sharing or integration, and may incur high costs.

In addition, data standardization affects what can be learned from data, whether or not the data is shared. Structured query language (SQL) uses simple syntax to access the database. The relational model based on this standard relies on data tables and significantly limits the ability of users to query complex queries. Competitors propose a way to overcome these limitations. However, despite the potential of market participants, they did not invest, partly because the major database suppliers, Oracle and IBM, had huge sunk costs in SQL. In contrast, the vendor made simple, low-cost changes and renamed the relational database of its database objects. Therefore, the data industry has never fully adopted the better standards of competitors.