A Quote by Arthur Conan Doyle
It is a capital mistake to theorize before one has data.
Related Quotes
It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.
It is a capital mistake to theorize before you have all the evidence. It biases the judgment.
The biggest mistake is an over-reliance on data. Managers will say if there are no data they can take no action. However, data only exist about the past. By the time data become conclusive, it is too late to take actions based on those conclusions.
Today, I think a CFO needs to be more of an operating CFO: someone who's using the financial data and the data of the company to help drive strategy, the allocation of capital, and the management of risks.
It is clear as you look at the team why Data Point Capital has so quickly become one of the premier venture capital firms. I look forward to adding to the firm's very bright future.
[Sovereignty] would break the American monopoly, but it would also break Internet business, because you'd have to have a data center in every country. And data centers are tremendously expensive, a big capital investment.
In reality, the labourer belongs to capital before he has sold himself to capital.
The financial doctrines so zealously followed by American companies might help optimize capital when it is scarce. But capital is abundant. If we are to see our economy really grow, we need to encourage migratory capital to become productive capital - capital invested for the long-term in empowering innovations.
Before, companies and startups had to lay up all this capital for data centers and servers, and take your scarce resource, which in most companies is engineers, and have them work on the undifferentiated heavy lifting of infrastructure. What the cloud has done is completely flipped that model on its head so that you only pay for what you consume.
I think it's often easier to theorize in the official codes of theory rather than to theorize lightly through scene, object, story, and incident in ways that keeps alive the sensual serendipities of language. This is not a question of being for or against theory, but rather of being suspicious of orthodoxies that concede, in advance, that what passes for theory must be signaled by a narrowing of diction, sentence rhythms, and sensual awareness. I'm in favor of surprise.
There is so much information that our ability to focus on any piece of it is interrupted by other information, so that we bathe in information but hardly absorb or analyse it. Data are interrupted by other data before we've thought about the first round, and contemplating three streams of data at once may be a way to think about none of them.
Big data has been used by human beings for a long time - just in bricks-and-mortar applications. Insurance and standardized tests are both examples of big data from before the Internet.
It is not a mistake to commit a mistake, for no one commits a mistake knowing it to be one. But it is a mistake not to correct the mistake after knowing it to be one. If you are afraid of committing a mistake, you are afraid of doing anything at all. You will correct your mistakes whenever you find them.
Thus even supposedly unadulterated facts of observation already are interfused with all sorts of conceptual pictures, model concepts, theories or whatever expression you choose. The choice is not whether to remain in the field of data or to theorize; the choice is only between models that are more or less abstract, generalized, near or more remote from direct observation, more or less suitable to represent observed phenomena.
We should have companies required to get the consent of individuals before collecting their data, and we should have as individuals the right to know what's happening to our data and whether it's being transferred.
One of the myths about the Internet of Things is that companies have all the data they need, but their real challenge is making sense of it. In reality, the cost of collecting some kinds of data remains too high, the quality of the data isn't always good enough, and it remains difficult to integrate multiple data sources.
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