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I hate to break it to you, but your data is dumb. In the renewable energy industry, we collect a ton of data, but most of it is without context. This not only leaves our insights into projects pretty rudimentary but also limits our ability to optimize our asset management processes. This makes you less efficient than you could be.


Time is money – ok a tired cliché but relevant nonetheless. Inefficient use of data and spending time simply searching for information is the time wasting equivalent of using the index card catalogue at the library instead of searching online for a book. (If you don’t get the reference google the Dewey decimal system) You can't afford to ignore the time you lose trying to find information. But if you’re reading this, you know that, and you’re looking for ways to improve. For that we need better asset intelligence.

Asset intelligence means understanding your data, it's limitations, it's context, and it's location, framing it so you can put it to use for business decision making. There’s nothing efficient about trying to know it all  – and asset intelligence is less about what you keep in your head, and more about the strategies you use to find and manage information when you need it.


Your data is dumb, I’ve said this already, but I also want talk about some of the other basic truths we don’t discuss about data.

Your data is not always right. It's not always there, and it's definitely not perfect.

'All measuring devices have error rates, and all data processing does as well. And errors add up.'

Where is your data coming from? Do you know? Are you pulling from meters, loggers, SCADA or is there a Data Acquisition System or a digital historian solution providing you site-level data?  Whether you've got five projects or five hundred, you’re collecting data and processing data from dozens of devices, all with different potential errors. This isn’t a renewables problem, necessarily, this is a basic measurement problem.  All measuring devices have error rates, and all data processing does as well. And errors add up. So with all these devices and processing, your errors becomes your context. Without that context you may not see what the data's trying to tell you.

Project data isn’t just technical information, and it’s important to remember this. To truly gather information and insights, we also have to give it business context. Power purchase rates, maintenance schedules, repair and replacement costs, and even legal requirements all help frame our data so that we can do something meaningful with it.

Let me give you a simplified example; Two meters record the energy output of a site, they are located less than a few hundred centimeters apart (I’d have said “a meter apart’ but…).  The meters are the exact same make and model; the only difference is that one meter is read by the utility, and the other by you. One month, the utility’s results from these readings differ from your own by about 1.8%. Now the first question: is that difference significant? Of course it is, you say, my margins are so thin now that every little point counts. But what if;

  • The meter tolerances are 1% each
  • The value of that 1.8% difference is $400/month
  • The cost to send someone to look into it is $1200  
  • The meter hasn’t been calibrated in 3 years
  • Someone is going to site in 20 days

Now we have some real context and insight. With this information, we can draw conclusions and quickly make decisions. 

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