With such a huge variety of asset types, from different eras and from different OEM, there is a considerable challenge when retrofitting an asset to extract data.
Data quality, from different types of data sources, are a must, to ensure that it can be unquestionably used when information is made available to be analysed, visualised, and reported on, both to the users and when it is fed to more complex analytics or reporting tools, for example, with the use of Machine Learning, Deep Learning and Artificial Intelligence.
IoT and Big Data usually come hand-in-hand. Most IoT projects tend to generate large quantities of data, which needs to be properly managed and structured to make its usage practical and responsive. Badly structured databases can drive end-users away from using the tools to access information. Data system scalability is obligatory!
Security and Cybersecurity: from physical access and the unlawful remote access to devices, are real risks that pose a challenge and should be considered throughout any project. Specific IT system standards and good practices should apply and be put into place to ensure that any security risks are mitigated.
Different projects = different requirements. For those who have been following the digitalisation solutions market in recent times, it has become clear that there was a flood of offers with proposals that rarely cover 100% of users’ needs or wants. Although we see consolidation (through acquisitions and partnerships initiatives) some projects require expert and niche solutions, originating from a multi-vendor solution integration challenge.