API Bull 1178-2017 pdf download.Integrity Data Management and Integration.
4 Managing pipeline integrity data historically involved the rather manual process of populating data within spreadsheets or disparate databases. Transitioning to an enterprise database to manage large pipeline integrity data sets provides an operator with several advantages, including the following: — Improved auditing and traceability: When spreadsheets are created, the logic and judgment that is applied while an individual is manipulating data is not captured, or easily understood. In most cases, this logic exists only in the mind of the individual who created the spreadsheet, which may result in compliance risk. — Improved t racking of dat a c orrections: P ropagating corrections t o da ta er rors ac ross multiple dependent s preadsheets, or bac k t o t he original data s ources, i s difficult a nd may po tentially introduce further errors. — Improved s afeguards against h uman er ror: H uman er rors, s uch as versioning er rors a nd corruption errors, can compromise the i ntegrity of da ta entry. D atabases and t heir as sociated graphical interfaces facilitate the implementation of quality rules and constraints that mitigate the potential for human error. — Improved r esource utilization: D atabases m ay provide i mproved ef ficiency o ver dat a management that uses disparate spreadsheets.
— Improved data security: Server-based data may be more difficult to access and propagate than individual files, which can be easily transferred to local or portable drives. — Improved s calability: Spreadsheets and s mall-scale dat abases m ay ha ve s ize l imitations t hat enterprise databases do not have. Data Quality Oversight 5 General 5.1 The as pects of data qu ality listed in 5.2.3 are examples of el ements an oper ator m ay consider when developing a data m anagement system, but the extent to which they are relevant varies d epending on asset complexity a nd organizational s tructure. As with an y s ystem, continuous improvement i s a c ore principal. T he s ystem i n this context is often referred to as a geographic i nformation s ystem ( GIS) or database, but i t m ay be a compilation of app lications and databases, with a map-based visualization being just one aspect of the solution. Data management planning considerations include the identification of key objectives to be achieved, as well as the strategies and policies that assist in achieving those objectives. Objectives 5.2 Core Objective 5.2.1 The core objective of a data management system is to achieve the highest degree of data quality possible for the intended purpose, while also doing the following: — promoting the efficient use of resources; — providing easier access to critical information for qualified employees; — ensuring t hat d ata i s protected an d preserved i n a ccordance w ith b usiness, l egal, and p olicy requirements; and — communicating with ot her s ystems us ing a common frame of r eference f or br oader analysis capabilities. Data Quality Criteria 5.2.2 Stakeholders s hould c learly define what i s m eant b y dat a q uality t o m ake the dat a fit for purpose in supporting the intended processes. The data should meet the following criteria: — Be def ect f ree: D ata s hould c onform t o t he di mensions of dat a qua lity ( outlined bel ow), as applicable.
5.2.3 In addition, a data quality definition could consider the following dimensions 1 of quality: — Accuracy: The data represents reality. — Completeness: All needed data is available. — Consistency: The data is free of internal conflicts. — Precision: The data is as exact as is needed. — Granularity: The data is kept and presented at the right level of detail to meet the needs. — Timeliness: The data is as current as needed and is retained until no longer needed. — Integrity: T he da ta i s s tructurally s ound. T his connectivity is frequently referred to as topology within the geomatics community.