In many enterprises, discrete information systems that were developed over time for specific functions in an uncoordinated fashion have traditionally hampered high-level decision-making. When internal systems are organized in a silo fashion, top management typically struggles to spot correlations among individual departmental reports that could indicate areas for cross-departmental or divisional improvements. Historically, discrete data collection systems fail to present big picture perspectives since any actionable information on trends is constrained, as if perceived through horse blinders.
Enterprises that implement M2M in any part of their business can expect a veritable tidal wave of incoming data. But unless it can be presented in useful context and be actionable, it adds no value. To get the full picture, the massive amount of data must be presented in meaningful, multidimensional trends.
Consequently, enterprises large and small today must rely on what is called “Big Data Analytics” for informed decision-making. The massive streams of dynamic M2M data can now be stored in the cloud and called upon to provide invaluable insight on a full range of variables. Extracting these virtual needles from the massive haystack of data can shed light on typically imperceptible correlations, and lead to informed decision-making.
In real life, the correlation of massive data sets might inform a manufacturer of a greater product failure rate under variables like temperature, time of day or geography. This would signal the need for modifications in product development and greater customer satisfaction.
M2M is an invaluable means to obtain insights on performance and reliability. When products with embedded M2M capability are now marketed, they can be bundled with revenue-generating value added services like preventive maintenance monitoring. The M2M-capable mechanism is able to send alerts warning of impending failure so that repair can be performed prior to costly downtime. Knowing in advance which part needs to be replaced, a dispatch center could direct a technician who has the specific replacement part on board his service vehicle to make the call – saving any costly downtime for the customer, as well as making more efficient use of service resources.
Analytics now enable managers to spot normally unseen trends across asset classes like delivery vehicles and onboard payloads to look at overall utilization and how to maximize efficiency. When analytics engines are integrated with M2M implementations, statistically meaningful trends can now be spotted, with far reaching implications for any business.
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