Any decent executive wants to have things in control. But information systems, and particularly their inter-dependencies, have already grown beyond our control. Seeking full control is a fool’s game. The recipe of optimal realistic control is simple but not always easy to execute:
A reasonable approach to test company’s business critical information systems such as ERP depends on the visibility to product development. If the system is developed in-house there's likely a good visibility to actual development teams’ work and quality assurance can be involved in the very early phases of product development. Involving QA early can be referred to as shift-left. On the other hand, a system used as a cloud-based Software-as-a-Service (SaaS) without any visibility to development is restricted also from the testing point of view. Only production version testing might be possible, often referred to as shift-right.
1 - AI-augmented test analysis and optimization
Testing by itself is done to validate the software solution being developed and guarantee a certain agreed upon level of quality for the product and to make sure no major regression happens in the software.
Machine Learning methods will allow us to bring a larger amount of statistical analysis tools to bear on all the test and telemetry our software pipeline is producing. We will see a surge of different software vendors bringing solutions for different kinds of dashboards and visualizations to provide a way to analyze the validity of our software and provide metrics for just about anything.
The third and final blog of the series builds on the link of employee and customer experience and CX metrics development to emphasise the importance of customer focus in organisational practices and paradigms.
Soon you will go through as many as 5,000 ERP related version updates per year. How can you manage them all, and how to ensure the quality of your software when systems are constantly changing?
Many are interested in the formation and improvement of customer experience, but its intricacies perplex how this could be managed. The simple answer seems to be more data. Statements such as “If you can’t measure it, you can’t improve it” and “Knowledge is power” have valuable kernels of advice, but faulty interpretations have led to masses of unused data due to sheer volume or a lack of meaning. This second post follows on the first and concentrates on methods to find the right customer experience metrics, how to utilise those indicators to drive better business, and a concrete approach called Value Creation Model to make sense of customer experience.