The amount of unstructured machine data generated by the Internet of Things (IoT) is growing as more devices and applications become connected. KPMG’s 2018 global technology industry innovation survey found that the IoT will drive the greatest business transformation in the next three years, according to the tech leaders surveyed. What is holding this back is how difficult it is to work with IoT data in context and consistency across the business.
A European study by 451 Research found that 54% of respondents ranked machine data as extremely important to them in meeting their business objectives, while a new report from Research and Markets has recently stated that “IoT data itself will become extremely valuable,” as the analytics of this data will drive product change and development, and will help identify unmet demand and supply gaps. Each of these potential use cases for data involve making it useful for different teams as part of their decision making.
So, the question becomes how to derive all that potential value from the machine data created by IoT devices, networks and services? On the surface, getting more out of your data seems easily achievable. Gathering data is easy, but what’s challenging is the ability to take that data and effectively use it to improve the operations and security of your business.
In reality, achieving this takes effort and perseverance. Doing it repeatedly is even harder, yet it’s here that the results can be extremely valuable – not just for developers or IT teams, but for the whole business, from support and marketing through to senior business managers. To make this work, reporting and using your data has to be a scalable process.
To start with, IoT data has to be looked at in conjunction with other sources of application data, logs and metrics to create value. In order to get value from it as part of that repeatable process, it has to be put into context over time and available automatically. Each set of data has to be parsed and analysed, not just individually, but as part of that larger picture.
The process of taking this raw data – from logs, application metrics, infrastructure reports and through to specific device data – and turning it into actionable insights involves an awful lot of automation if you want to run at scale. This is essential if you want to be able to create a stream of continuous intelligence for the business.
Following on from getting that accurate picture of current performance across all the moving parts involved, you can start analysing how you define and deliver value around your activities.
