The Life-Cycle of Live Data
With an eye on integration of embedded devices with the Cloud, Raima CTO Wayne Warren differentiates between live, actionable information and data with ongoing value, and argues that to realise the true power of the Cloud, businesses must utilize the power of the collecting and controlling computers on the edge of the grid.
The rise of the Cloud has presented companies of all sizes with new opportunities to store, manage and analyze data – easily, effectively and at low cost. Data management in the Cloud has enabled these companies to reduce their in-house systems costs and complexity, while actually gaining increased visibility on plant and processes. At the same time, third party service organisations have emerged, providing data dashboards that give companies ‘live real time control’ of their assets, often from remote locations, as well as historical trend analysis.
Consider, for example, a company at a central location with a key asset in an entirely different or isolated location. It may be advantageous to monitor key operational data to ensure the equipment itself is not trending towards some catastrophic fault, and some performance data to ensure output is optimal. That might be relatively few sensors over all, and perhaps some diagnostics feedback from onboard control systems. But getting at that data directly might mean setting up embedded web servers or establishing some form of telemetry, and then getting that data into management software and delivering it in a means that enables it to be acted upon.
How much easier to simply provide those same outputs to a Cloud-based data management provider, and then log-in to a customized dashboard that provides visualization and control, complete with alarms, actions, reports and more? And all for a nominal monthly fee. Further, with virtually unlimited storage in the Cloud, all data can be stored, mined, analyzed and disseminated as reports that provide unprecedented levels of traceability (important to many sectors of industry) and long term trend analysis that can really help companies to boost performance and, ultimately, improve profitability.
As our data output increases, it might seem reasonable to expect that the quality of information being returned from the Cloud should improve as well, enabling us to make better operational decisions that improve performance still further. And to an extent, this is true. But there is also danger on that path, because as we move into an era of ‘big data’, it is becoming increasingly difficult to pull meaningful, ‘actionable information’ out from the background noise.
Where once a data analyst might simply have been interested in production line quotas and the link to plant or asset uptime, today they may also be interested in accessing the data generated by the myriad of automated devices along the production line, because that raw data may well hold the key to increased productivity, reduced energy consumption, elimination of waste, reduction in down time, improved overall equipment effectiveness, and ultimately a better bottom line.
And we really are talking about huge amounts of data. The rise of the ‘Internet of Things’ and machine-to-machine (M2M) communications, combined with the latest GSM networks that deliver high-speed, bi-directional transfer without the limitations of range, power, data size and network infrastructure that held back traditional telematics solutions, has seen data transmission increase exponentially in the last few years. As of 2012, across the globe over 2.5 exabytes (2.5x10exp18) of data were being created every day, and it is certainly not unusual for individual companies to be generating hundreds of gigabytes of data.
Importantly, different types of data will have different lifecycles, and this impacts on how that data needs to be managed.
