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CURATOR
A pinboard by
Hazrizam Ab Rahim

Senior Application Administrator, Universiti Teknologi Malaysia

PINBOARD SUMMARY

How data quality management system important to the sustainability of oil and gas projects.

Data quality is essential to the sustainable of database management for oil and gas projects in Malaysia. This not only help to reduced waste but also ensure the life-cycle of database management sustainable for a long period. Revisit or recycle of data can be very efficient and in intelligent manner to avoid any data redundancy, data corrupt, mishandling of data and data losses. In Malaysia, Oil and Gas project are having a huge challenges nowadays with the lower prices of crude oil in market. Players start to initiate new method to ensure more efficient processes are being executed with the objectives of running at more cost effective budget. The interception of industry 4.0 initiatives such as Internet Of Things (IoT) and BigData can ensure players remain competitive in market.

3 ITEMS PINNED

The data quality improvement plan: deciding on choice and sequence of data quality improvements

Abstract: With the rapid growth in the amount of data generated worldwide, ensuring adequate data quality (DQ) is increasingly becoming a challenge for companies: data are, among others, required to be timely, complete, consistent, valid, and accessible. Given this multidimensionality, DQ improvements (DQIs) need to be purposefully chosen and –as there can be path dependencies– arranged in an optimal sequence. Thus, this research contributes to performing the complex multidimensional task of ensuring adequate DQ in an economically reasonable manner by providing a formal decision model for identifying an optimal data quality improvement plan (DQIP). This DQIP comprises both an economically reasonable selection and execution sequence of DQIs based on existing interrelationships between different DQ dimensions. Furthermore, a comprehensive Monte Carlo simulation provides insights in implications to put the decision model into operation. For practitioners, the decision model enables efficient allocation of resources to DQIs. The model also gives advice on how to sequence DQIs and attracts attention to the complex problem context of DQ in order to support valid managerial decisions. With the rapid growth in the amount of data generated worldwide, ensuring adequate data quality (DQ) is increasingly becoming a challenge for companies: data are, among others, required to be timely, complete, consistent, valid, and accessible. Given this multidimensionality, DQ improvements (DQIs) need to be purposefully chosen and –as there can be path dependencies– arranged in an optimal sequence. Thus, this research contributes to performing the complex multidimensional task of ensuring adequate DQ in an economically reasonable manner by providing a formal decision model for identifying an optimal data quality improvement plan (DQIP). This DQIP comprises both an economically reasonable selection and execution sequence of DQIs based on existing interrelationships between different DQ dimensions. Furthermore, a comprehensive Monte Carlo simulation provides insights in implications to put the decision model into operation. For practitioners, the decision model enables efficient allocation of resources to DQIs. The model also gives advice on how to sequence DQIs and attracts attention to the complex problem context of DQ in order to support valid managerial decisions.

Pub.: 14 Jan '17, Pinned: 27 Jul '17