Process Context, and Separating Signal from Noise
- February 26, 2014
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So buzzword collision is just one of those things that is going to happen a lot with an area like BPM. Every time something gets interesting (Social Media!) someone will ask the question, how does that intersect or support or undermine BPM? Or the inverse, how does BPM support or diminish or intersect with this new buzzword-compliant technology?
And so it is with Big Data and BPM. Recently BPM.com asked the question: “Is Big Data Useless without Process?”
My answer in the discussion was as follows, and I wanted to share it here:
I see a lot of focus on Big Data, and the massive amounts of data out there. Saw a quote of something like 25B pieces of data a company collects every day that they want to analyze.
So, here’s a thought. The issue isn’t having and dealing with lots of data. The issue is separating signal from noise. wheat from chaff.
Data is useless without context, no matter how much or how little you have. Process is one kind of context, and some might call it useful…
The challenge to data and information is that we have too much. Maybe we should figure out how to process more. Or maybe we should figure out how to collect more relevant information at the source, and to store less irrelevant data to begin with. Focus is more important than data volume. Quality more important than quantity.
This is all motherhood and apple pie. And it is obvious when you consider that a lifetime of video is uploaded to YouTube every minute of every day. And yet, there are only a few videos worth spending your time watching. As the amount of video grows, you can be even choosier about what you spend your time watching…
Context is everything when you’re trying to make sense of a world with too much data. Process is a really valuable form of context for businesses – it isn’t the only lens through which to analyze your data, but a really valuable one. And through process, one might do a smarter job of collecting the data that matters, rather than just catching everything that comes across the wire.
There were other good thoughts in the discussion – data being noun, process being verb, for example. Or Chris Taylor’s response:
“…I also know that too much of the spend on Big Data has lacked roots in how organizations actually operate, meaning that we end up with insight that isn’t useful or insight that can’t be used. If organizations could first identify what process needs to be improved, scrapped or created because of the output of crunching large (or small, but otherwise very important) digitized information, they would be far better off.”
One thing is clear – the BPM community isn’t just strong technically – it is filled with people who understand how to run businesses, and they get the value of data, but also the value of the context that process brings with it. I think we’re just at the beginning of figuring out how to deal with the volumes of data companies can collect. One of the concepts of lean is to avoid overproduction. Surely storing random data that has no business value (or very little business value) is a classic case of overproduction. Even processing/filtering that data may represent overproduction.