A Quote by Clayton Christensen
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.
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The whole enterprise of teaching managers is steeped in the ethic of data-driven analytical support. The problem is, the data is only available about the past. So the way we've taught managers to make decisions and consultants to analyze problems condemns them to taking action when it's too late.
The whole enterprise of teaching managers is steeped in the ethic of data-driven analytical support. The problem is, the data is only available about the past. So the way weve taught managers to make decisions and consultants to analyze problems condemns them to taking action when its too late.
With too little data, you won't be able to make any conclusions that you trust. With loads of data you will find relationships that aren't real... Big data isn't about bits, it's about talent.
... negative feelings are not true feelings at all; rather, they are your thoughts about something, based always on the previous experience of yourself and others. You will not find Truth in your past data, only past data that is based on other past data that is based on other past data, and so forth. Forget your "past experience" and look directly at the experience you are having. Right Here, Right Now. There is your Truth.
Disruptive technology is a theory. It says this will happen and this is why; it's a statement of cause and effect. In our teaching we have so exalted the virtues of data-driven decision making that in many ways we condemn managers only to be able to take action after the data is clear and the game is over. In many ways a good theory is more accurate than data. It allows you to see into the future more clearly.
Scientific data are not taken for museum purposes; they are taken as a basis for doing something. If nothing is to be done with the data, then there is no use in collecting any. The ultimate purpose of taking data is to provide a basis for action or a recommendation for action. The step intermediate between the collection of data and the action is prediction.
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.
Any time scientists disagree, it's because we have insufficient data. Then we can agree on what kind of data to get; we get the data; and the data solves the problem. Either I'm right, or you're right, or we're both wrong. And we move on. That kind of conflict resolution does not exist in politics or religion.
By the time it becomes obvious that a technology will have truly disruptive impact, it is often too late to take action. This is one reason why we are such advocates of using theory to try to analyze industry change. Conclusive evidence that proves that a company needs to take action almost never exists. In fact, the data can fool management, lulling them into a false sense of security.
Biases and blind spots exist in big data as much as they do in individual perceptions and experiences. Yet there is a problematic belief that bigger data is always better data and that correlation is as good as causation.
We are ... led to a somewhat vague distinction between what we may call "hard" data and "soft" data. This distinction is a matter of degree, and must not be pressed; but if not taken too seriously it may help to make the situation clear. I mean by "hard" data those which resist the solvent influence of critical reflection, and by "soft" data those which, under the operation of this process, become to our minds more or less doubtful.
A data scientist is that unique blend of skills that can both unlock the insights of data and tell a fantastic story via the data.
Facebook collects a lot of data from people and admits it. And it also collects data which isn't admitted. And Google does too. As for Microsoft, I don't know. But I do know that Windows has features that send data about the user.
Data!data!data!" he cried impatiently. "I can't make bricks without clay.
I'm going to say something rather controversial. Big data, as people understand it today, is just a bigger version of small data. Fundamentally, what we're doing with data has not changed; there's just more of it.
People believe the best way to learn from the data is to have a hypothesis and then go check it, but the data is so complex that someone who is working with a data set will not know the most significant things to ask. That's a huge problem.
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