In the first part of this topic, I described how the new technology enabled me to do more than I could, previously, but how it caused new problems.
In this 2nd installment, I will talk about the issues of using the information.
In the days of film, I would have returned from vacation with a few rolls of film and probably less than 50 pictures. Even then, friends and relatives probably wouldn’t be eager to view quite that many.
Now, with over 1000 pictures, you can bet your life than no-one will come near me until I pick through them and pick out the most interesting ones to put into a collection.
Issue: In my example, I’m manually determining which photos to share. This is quite a lot of work. But in your laboratory information system, your goal is that you will not be manually picking through information. Unlike my example where I am the “expert” and can decide which “information” is important, that is an unsatisfactory way to plan to use your automated system.
My example: I saved everything that I wanted to save. I didn’t hold back. I have pictures of every rock and cloud that looked interesting. My method of labeling the data is rudimentary. Sharing data takes manual and expert intervention for each collection of photos (data) that I want to share.
Your system: If you save everything, even with a more sophisticated system of labeling than mine, data can end up difficult to identify and use. Your volume of data will probably prohibit relying on a manual system of experts reviewing the data. Unfortunately, some companies still rely on this way of sifting through their data because their plans to include everything don’t address the real problem.
The real problem: Even if you don’t know ahead of time what data you will or will not need to use at a future time, saving everything is not a solution to that problem. Additionally, that mentality – that merely saving everything solves the problem – will leave you with lots of data that no-one can figure out how to mine. And, just as in my own example, a new tool or technology doesn’t solve the problem for you. If you have a good data strategy, a new tool can make mining the data easier, but it can’t replace a good strategy.