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December 9, 2017

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What makes a good M&D analyst?

March 9, 2018

Monitoring & diagnostics (M&D) programs take several forms - brick & mortar, virtual, distributed (multiple locatins), etc. (a future blog post).  Regardless of the form, the key to success is having good analysts that can effectively identify and communicate early warning findings to site personnel that can take action. This short blog post highlights some of the key traits, skills, and experience that a good M&D analyst must have.

 

First, a good analyst must be humble.  When a potential early warning is found, a humble analyst knows that it might be a real indication but to make the determination other people must be relied upon to make assessments using diverse experiences. The reason for this is monitoring small deviations is much more difficult than monitoring for larger, obvious deviations (such as a variable approaching a known limit).  Humbleness is the key to good teamwork, and M&D analysts must have this quality for long-term success.

 

Second, a good analyst must have data analysis experience and training.  For example, some prior training on using statistics and plotting tools is essential.  This will help an analyst know how to extract key features out of raw and processed data.  To be a good analyst, one must also be knowledgeable about basic process operation - for example how to determine if the motor is on or off, if the unit is ramping up or shutting down, if temperature is going up because of ambient conditions or because of a potential anomaly.  This takes time, and experience is the only answer to being knowledgeable about process operation. 

 

Third, a good analyst is also good communicator.  If an analyst does not communicate when needed, the anomalies might go unnoticed by the equipment/system owner and the value of the M&D program will not be realized.  However, there are effective M&D programs where some analysts are not comfortable communicating directly with an experienced component or system expert (usually due to lack of experience), but a more experienced analyst takes on this role (as an in-between).  Sometimes this role is filled by the M&D supervisor (a future blog post).

 

Fortunately, teams with multiple people - some having heavy experience and some having heavy data analytics backgrounds - collectively fill the requirements of a good analyst.  In other words, the whole is greater than the sum of its parts.

 

 

Comments or questions?  Write me at aaron.hussey@int-analytics.com.

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