Let me put my cards on the table. I talk to Amazon's Alexa in the kitchen. I can control my heating, lights and blinds from anywhere in the world, much to my wife's annoyance. The television is connected to the WiFi, and we are debating giving up the cable subscription. The burgeoning Internet of Things provides us tools that can both change behaviour and, subjectively, make things better.
But not everyone buys into this march of technology. Many are put off by concerns around security, some see it as an intrusion, and for others it is the fault line between the world they knew and the world as it is now, a world where many of the old rules and old skills are giving way to new models of interaction, and a requirement to learn new things.
As an aside, I recently had a conversation with a more senior member of my family who was lamenting the decline of letter writing. "Why can't people just write letters like they used to?" I tried explaining that emails were more immediate, and messaging apps quicker still, but my relative kept coming back to the fact that people do not write. In their perception an email or a brief message simply did not count.
In my view, while the tools may have changed, the underlying process has not. The need and desire for effective communication remains, irrespective of the tools deployed. The fact that one can now communicate with an immediacy that only a few years ago was the stuff of science fiction is something to be welcomed, not feared.
We see much of the same reaction to technology in the workplace. Focusing on maintenance / MRO, for example, the potential for an ever increasing volume and granularity of data is manifest. The challenge though is how that data is captured, transformed into usable information, and in turn how that information is used to drive effective decision making and the delivery of organisational strategy.
From my perspective this points to three sets of activity:
Firstly the data collection strategy needs to be clearly defined - what should be collected, from where, how, when and why. An example might be real-time equipment telemetry, indicating run time, temperature, vibration and so on.
Secondly, we need to articulate how data will be transformed and presented. Data from disparate sources might need to be consolidated and displayed in an easily consumable format. For example, component-level telemetry might be brought together to provide a picture of the health of an overall system, which when combined with historical performance information helps drive prediction and planning, i.e. we need a tool.
Thirdly, we need to consider what is then done with this information once it has been collected and presented to the user. Our focus is on why the tool is required, and how that tool is used to drive operational performance improvement. For example, there is little benefit in providing early notification of plant potential issues, picked up through equipment telemetry, if nothing is done about it.
This final point around adoption is critical. While I do not underestimate the challenges surrounding data acquisition, aggregation and the development of tools, often the biggest issue is user acceptance and the effective use of tools as they become available. The reality is that adoption of new ways of working, whether they be technology driven or not, is a function of the value that the user perceives. If there is little of no perceived value, then adoption will likely fail.
As managers and leaders we need to work with the users to define the benefits. To do this we need to start with the organisational objectives, define how the business will work (operating model / processes etc.), and only then think about what tools are required and how they will be delivered. To do anything else risks a failure to adopt, and a failure to deliver the expected benefits.
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Published in Linkedin on July 12, 2017 by
