how_can_we_forecast_when_we_do_not_have_a_lot_of_data
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| how_can_we_forecast_when_we_do_not_have_a_lot_of_data [2016/10/27 15:38] – created hpsamios | how_can_we_forecast_when_we_do_not_have_a_lot_of_data [2020/06/04 09:15] (current) – Removed LINKBACK hans | ||
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| ====== How Can We Forecast When We Do Not Have a Lot of Data? ====== | ====== How Can We Forecast When We Do Not Have a Lot of Data? ====== | ||
| - | or "How much data do we need to generate a useful metric?" | + | > "How much data do we need to generate a useful metric?" |
| Many people think that you need a lot of data in order to get statistically useful data. For example, we hear a lot about "Big Data" and how we can mine huge volumes of data to produce interesting information. Many of us assume that in order to do something meaningful, and with any accuracy at all, we also will need a lot of data. It turns out that in many cases you really do not need much data to produce useful and interesting results. | Many people think that you need a lot of data in order to get statistically useful data. For example, we hear a lot about "Big Data" and how we can mine huge volumes of data to produce interesting information. Many of us assume that in order to do something meaningful, and with any accuracy at all, we also will need a lot of data. It turns out that in many cases you really do not need much data to produce useful and interesting results. | ||
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| In other words, by the sixth Sprint, we only have a 20% concern that the next Sprint’s velocity is going to be outside the 15 – 30 point range and we are 80% sure it will be in that range. | In other words, by the sixth Sprint, we only have a 20% concern that the next Sprint’s velocity is going to be outside the 15 – 30 point range and we are 80% sure it will be in that range. | ||
| - | That’s a pretty small amount of data to generate a lot of understanding of the data we have. And the thinking approach can be applied to all kinds of metrics. | + | That’s a pretty small amount of data to generate a lot of understanding of the data we have. And the thinking approach can be applied to all kinds of metrics |
| {{tag> | {{tag> | ||
| - | ~~LINKBACK~~ | ||
| - | ~~DISCUSSION~~ | ||
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