A Critique of Trudy Curtis, 2018. Improving Trust in Data

Author : Trudy Curtis Title of article- Improving Trust in Data

Title of Journal : Digital Energy Journal, issue 75, Nov- Dec 2018, pp.14-15

The Statement of the problem centers around the fact that despite huge sums of money spent acquiring analytics, companies are still not able to achieve their objectives due to poor quality of data

The purpose is to highlight first, the need to improve data quality to achieve its analytic objectives and trust among people, while emphasizing on creating the standards required to build that trust, and in my opinion,  to create awareness of the existence of and make executives of companies to identify with the PPDM of which the author is its CEO.

While the main ideas and propositions are clear, the article did not mention any tool that may help convert old legacy data and documents from their draft formats into usable data supported by new innovative tools. a lot of the problems encountered in the oil and gas industries today centers around front-end engineering design (FEED) data which these new analytics does not support or synchronize with. For example, millions of data created before  Yr. 2000 were in formats in which the tools to access has been retired and no longer supported having been replaced by new IT systems. It becomes an enormous challenge as some oil and gas plant installations have field life of between 30-50 years and still require those project pre-commissioning, commissioning and execution data

Critique the author uses the opportunity at the last PPDM’s London Luncheon to admonish the oil and gas sectors to develop meaningful and data quality standards, with an objective she says could be described as “turning data management from a “Me” world, where companies only do work which is in their interests to do, to a “We” world, where the industry works together to keep data well managed, and treat data as a strategic business asset”. Here, I see the author as trying to sell a product / service rather than  making a case to drive data quality, since one can only trust what one is sure of.

Her assertion for the need for consistency of data managers and data scientists who would have a good understanding of the data they are working with, and, often encounter problems with data is highly supported, however, there is an urgent need to advance and professionalize the skill pool of the data managers who often times are not regarded as key contributors in the oil and gas production line despite the important role they play across the project phases. Her definitions of the three mechanisms for building trust in data resonates with me, and it will be a double win situation to have global standards for managing data such as the DEP (Design Engineering Practice), ISO 8000 (International Standard for Data Quality) and the EIS (Engineering Information Specification) to benchmark organizations’ data practice against established standards.

In conclusion, there are several other challenges facing data and how they are managed, such as theft, privacy, creating then right topology and architecture around how we map data for indexing and storage. Data can only be properly managed when the right people, process and technology are aligned.

References

Curtis, T. 2018. Improving Trust in Data. Digital Energy Journal, issue 75, Nov- Dec 2018, pp.14-15

Kwon, O., Lee, N. and Shin, B., 2014. Data quality management, data usage experience and acquisition intention of big data analytics. International journal of information management, 34(3), pp.387-394.

 Tayi, G.K. and Ballou, D.P., 1998. Examining data quality. Communications of the ACM, 41(2), pp.54-57. Weidema, B.P. and Wesnaes, M.S., 1996.

Data quality management for life cycle inventories—an example of using data quality indicators. Journal of cleaner production, 4(3-4), pp.167-174.