mmon browser. 7. Value: Perhaps, the attribute that drives many of the basic decisions on the previous 6Vs is the value of the data. For instance, what is the incremental value in storing the last 2 years, 5 years, or 10 years of a given data set? Could the same value be garnered with stor- ing summary data after 2 years? The challenge in deciding whether to keep or discard any data is to assess the value of its' use for applications and insights known today compared to those that could become possible in the future. 8.2.2 Data QUALITY Data quality has been recognized by the International Standardization Organization as an impor- tant topic from the early 1990s. While the rigor around what level of data quality is needed ulti- mately depends on the application, a variety of authors have written guidelines around data quality such as the 2011 ISO 8000 data quality standards [Wang10], and those summarized by Cai and Zhu [Cai15]. Table 8.2 summarizes some of the attributes that should be considered around data quality. While the elements in Table 8.2 may not be exhaustive, it is important to address these areas throughout all phases of a big data project, to understand how they need to be treated to ensure use- ful actions and insights are realized. Building the Network of the Future TABLE 8.2 Important Data Quality Attributes for Big Data Systems Availability Usability Reliability Relevance Presentation/Quality Accessibility-Can Documentation-Is it Accuracy-Is the Fitness-Can enough Readable-data you get the data? understood how the data accurate data be accessed, and is content and format data are collected? enough for the use it useful for the intended are clear and intended? purposes (e.g., understandable indicators, prediction, classification)? Timeliness-Is it the Credibility-Has a Integrity-Do the time period reliable person data values follow a needed? documented? standardized data model/type? Authorization-Is Metadata-Are the Completeness- one authorized to data fields defined? Does the data have use the da