Posted on August 9, 2007  Comments (4)

Marissa Mayer speech at Stanford on innovation at Google (23 minutes, 26 minutes question and answers). She leads the product management efforts on Google’s search products- web search, images, groups, news, Froogle, the Google Toolbar, Google Desktop, Google Labs, and more. She joined Google in 1999 as Google’s first female engineer. Excellent speech. Highly recommended. 9 ideas:

(inside these are Marissa’s comments) [inside these are my comments]

1. Ideas come from anywhere (engineers, customers, managers, executives, external companies – that Google acquires)
2. Share everything you can (very open culture)
3. You’re Brilliant. We’re Hiring [Google Hiring]
4. A license to pursue dreams (Google 20% time)
5. Innovation not instant perfection (iteration – experiment quickly and often)
6. Data is apolitical [Data Based Decision Making - this is true but as an operating principle requires people that really understand data. See: Data can't lie.
7. Creativity loves Constraints [process improvement and innovation]
8. Users not money [the opposite of what business school's teach business case]
9. Don’t kill projects morph them

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Posted on August 9, 2007  Comments (6)

Many people state that data can lie. Obviously data can’t lie.

There are three kinds of lies: Lies, damn lies and statistics – Mark Twain

Many people don’t understand the difference between being manipulated because they can’t understand what the data really says and data itself “lying” (which, of course, doesn’t even make sense). The same confusion can come in when someone just draws the wrong conclusion from the data that exists (and them blames the data for “lying” instead of themselves for drawing a faulty conclusion). The data can be wrong (and the data can even be made faulty intentionally by someone). Or someone can draw the wrong conclusion from data that is correct. But in neither case is the data lying. It is also common to believe the data means something other than what it does (therefore leading to a faulty conclusion).

For a very simple example, believing if the average height for adults in the USA is 5 feet 9 inches that half the people must be taller and half the people must be shorter. You could then draw the conclusion that half the adults must be shorter than 5 feet 9 inches. But that is not what an average height means (it is basically what median means, though if you want to get technical, it doesn’t mean exactly that). You might draw the conclusion that the average height of an adult in California is 5 feet 9 inches but that is not supported by only the data that says what the height of an average adult in the country is. The same hold for drawing the conclusion that 5 feet 9 inches is the average height of a women. Now in this simple examples, hopefully people can see the faulty reasoning but such reasoning often goes on without consideration.

In a great speech by Marisa Meyer she speaks of Google makes decisions using data and that data is apolitical. One benefit of this, she says, is that Google makes decisions on what the data supports not political considerations. The belief that basing decision on what the data supports leads to better decisions can seem false for those that accept the quote about 3 types of lies (or those that see there is some weakness to this point if those supposedly basis decisions on data don’t really understand how to do so).
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