Those who rule perfect data…

A Passionate About OSS article last month spoke of how the investment strategy of a $106 billion VC fund has changed my thinking on our OSS‘ most valuable asset. Masayoshi Son is quoted in that article as follows:

“Those who rule data will rule the entire world. That’s what people of the future will say.”

But one question keeps coming back to me… if you’re ruling poor quality data, will you rule nothing whatsoever?

Along the same lines, the old adage, “practice makes perfect,” is not very helpful if you’re not practicing in a constructive way. A better (albeit somewhat impossible) variant on the adage would be “PERFECT practice makes perfect.”

Let me share an example. There is a product that is completely ground-breaking in its ability to automate and optimise designs of large-scale network roll-outs – designs that include outside plant and access network technologies. In bake-offs with some of the best available network designers, this product and its algorithm consistently beats the humans by far more than 25% (when measured by capital costs, implementation time and various other metrics).

Its one challenge in taking over the world and automating every future network design is having a base set of data that is so perfect that no re-design work is required. For example, if the base data says a duct route is available and has capacity for inserting a cable, then the product assumes it can use the duct in its optimal design. But when the field techs arrive at site, they find the duct is too badly damaged to use or already filled to capacity with other cables that can’t be overhauled. A new optimal design has to be calculated to consider the lack of availability of that duct.

The tool still gives great results, even after all the manual intervention, but perfect source data would give breathtaking results.

So I’d look to make one small tweak to Masayoshi Son’s quote. “Those who rule PERFECT* data will rule the entire world. That’s what people of the future will say.”

* whereby perfect means as high in quality as realistically possible.

So, perhaps those expensive data audits and cumbersome data quality processes will have a far greater ROI (Return on Investment) in future than any of us could ever estimate.

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