An OSS data input quandary

In writing or in life, people who are successful focus on the input (putting in). Those who aren’t successful focus on the output (taking out).”
Paraphrasing Tucker Max during a podcast with James Altucher.

In the post that follows, you’ll notice that I’m putting a completely different context on Tucker’s quote above, which is a new spin on the old adage of, “you get out what you put in.” This got me wondering whether this is true in the world of OSS. I know from personal experience that the more effort you put into building an OSS, the more you get out from it. In my years of consulting, it’s also been obvious that the customers who put the most into an assignment also get the best results (with the opposite also generally being true too). But the part I was wondering about was actually the information you put into an OSS.

Is it true that if you focus on what data you’re putting in to your OSS, it just follows that you’ll also get great insights coming out? I’m not so sure on this one. You may’ve seen an earlier post on “Minimum Viable Data (MVD)” that discusses the merits of small data (as well as the possibilities of big data – if you know how to make use of it).

If we break the input down, we have two lines of thought then – highly targeted, streamlined data versus gather as much as you can, then figure out what to do with it. With the first, the MVD approach, you almost need to design input with the end in mind. With the second, the thinking isn’t so much on the input, but asking the right questions of the data to get great answers. The second is more flexible and due to statistical techniques, can possibly overcome poor quality data.

This leads to the question – since an OSS is only as good as its data and data usage, is it always true that if you put garbage in, you get garbage out (GIGO)? Is that where the “input” focus should always be?

When it comes to data, do you think Tucker Max is right that, “the successful focus on the input whereas those who aren’t successful focus on the output (taking out)?” Have you developed great techniques for dealing with rubbish data? I’d love to hear your thoughts on this one.

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