| Abstrakt: |
The growing ICT skill shortages that have been plaguing the economy for years are likely to get worse. To combat this, there is a growing trend in the industry towards self-service data preparation, where the key objective is to build intelligent systems that enable business analysts and data scientists to prepare ad-hoc data sets themselves without needing help from IT staff. Systems and applications built by non-ICT professionals can raise issues. Once these end-user systems find their way into the daily operational 'life' of corporations, they can become integrated parts of key processes. Before long, it is adopted as part of vital production process outcomes. The ICT department may find itself eventually burdened by having to support something they never built, tested, or approved of as being part of their suite of production systems let alone corporate strategy. Whether this plays out or not, a more immediate concern is raised regarding the data integrity and process integrity of such 'self-serving' systems. How are these addressed by those operating outside the sphere of ICT? When it comes to diagnostic condition monitoring and data analytics, as well as decision-making in the engineering management environment, the size of data involved in decision making are significant, making it even more vital that data analytics and business insights can be gained fast using ICT capability that supports technical professionals in these business disciplines. Since 2013-2020, engagement with Universities, ASX listed companies, the Australian Federal Government, State Government, Australian Government Agencies, private companies, businesses, and individuals, many times as an auditor, has also assisted the researcher with gaining the insights and disciplines required for dealing with these types of challenges. The paper addresses the issue of quality control in the form of data integrity and process integrity for end-user (self-serving) built systems. It demonstrates how these challenges can be tackled by referring to one such self-service model that has addressed this quality control issue: The Adaptive Analytics Model. [ABSTRACT FROM AUTHOR] |
