Row 64796

Row ID: 64796 | Dataset Entry | Axioma AXP Content Repository

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I think that "using a lot of data" is often mistaken for being "data-driven".

If you run a lot of reports, build a lot of models, but decision-makers ultimately pick and choose what numbers they choose and believe and make whatever decision they want without it being retroactively scrutinized relative to a data-drive approach, then you're not data driven. The data doesn't do any driving, the data is in the trunk and it's brought out when you need it to justify your point.

Here's what I have seen: smart companies don't fool themselves - they look at the things that can and should be data driven and make them data driven, and then they take the things they don't think can be data driven and just focused on having a well-defined, disciplined process that can be evaluated and improved on.

So, for example: I worked at a company that had incredibly stable, predictable sales and very infrequent and clear-cut price changes. That company was *insanely* data driven as it related to price changes, because they *knew* the data told them 99% of the story. There was no room for people to come with opinions to a pricing meeting.

On the other hand, this company often introduced new products in new areas - and they *knew* that data would never answer 100% of the questions they had about products with no history. What they did do is set up a very structured process for how to approach those situations in a way that was repeatable and decomposable - so that once you got to see what actually happened, you could go back and ask "well, what went well and what went wrong?".

So, for example, in defining the plan for a new product you would make some choices:

1. What past new product is this most likely to behave like - and why do we believe that?

2. What is the rate at which we will introduce the product to the market from a supply chain perspective?

3. What are our anticipated marketing funds/promotions/etc going to look like over the onboarding period?

4. How have focus group results compared to past, similar products?

So the strength of that process is that once things actually happened, if you missed big it wasn't just a "oopsie poopsie, we messed up", it was "well, it looks like we overestimated how quickly we could roll this out, and we overeestimated the effectiveness of ad campaign A", which then allows you to go back and evaluate *why* you over/underestimated specific factors.

FieldValue
text I think that "using a lot of data" is often mistaken for being "data-driven". If you run a lot of reports, build a lot of models, but decision-makers ultimately pick and choose what numbers they choose and believe and make whatever decision they want without it being retroactively scrutinized relative to a data-drive approach, then you're not data driven. The data doesn't do any driving, the data is in the trunk and it's brought out when you need it to justify your point. Here's what I have …
label r/datascience
dataType comment
communityName r/datascience
datetime 2024-05-23
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url_encoded Z0FBQUFBQm5Lak9yczNtV0NLWW9zZmw5eU1MX1hwaWpZbjFvTDM2akhXRWExN2c5cS1TX0VhdVJJa3VZVU0waWRTOXVJWm5jN0YzLTRmemo4eFlhTEJWSHJyQW03RXlmNUNkcVlGVTJPamFaZEw5Z3UzdDFwSU5JWmExNGdYc2FKYjRuQWw1WndfZ0ZHRE5HdWxFUjAxVlNSakt0TzQ0ZnhkT0ZoVUEtRzZVak0tXzlHc2tmaEZ1SnU2YUN3SGRsaFd5cFRPNU9oWC1xU1JoeDBUYzZaUm9Nc2hBZmMzWFc0dz09

Raw Record

{
  "text": "I think that \"using a lot of data\" is often mistaken for being \"data-driven\". \n\nIf you run a lot of reports, build a lot of models, but decision-makers ultimately pick and choose what numbers they choose and believe and make whatever decision they want without it being retroactively scrutinized relative to a data-drive approach, then you're not data driven. The data doesn't do any driving, the data is in the trunk and it's brought out when you need it to justify your point. \n\nHere's what I have seen: smart companies don't fool themselves - they look at the things that can and should be data driven and make them data driven, and then they take the things they don't think can be data driven and just focused on having a well-defined, disciplined process that can be evaluated and improved on.\n\nSo, for example: I worked at a company that had incredibly stable, predictable sales and very infrequent and clear-cut price changes. That company was *insanely* data driven as it related to price changes, because they *knew* the data told them 99% of the story. There was no room for people to come with opinions to a pricing meeting. \n\nOn the other hand, this company often introduced new products in new areas - and they *knew* that data would never answer 100% of the questions they had about products with no history. What they did do is set up a very structured process for how to approach those situations in a way that was repeatable and decomposable - so that once you got to see what actually happened, you could go back and ask \"well, what went well and what went wrong?\".\n\nSo, for example, in defining the plan for a new product you would make some choices:\n\n1. What past new product is this most likely to behave like - and why do we believe that?\n\n2. What is the rate at which we will introduce the product to the market from a supply chain perspective?\n\n3. What are our anticipated marketing funds/promotions/etc going to look like over the onboarding period?\n\n4. How have focus group results compared to past, similar products?\n\nSo the strength of that process is that once things actually happened, if you missed big it wasn't just a \"oopsie poopsie, we messed up\", it was \"well, it looks like we overestimated how quickly we could roll this out, and we overeestimated the effectiveness of ad campaign A\", which then allows you to go back and evaluate *why* you over/underestimated specific factors.",
  "label": "r/datascience",
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  "datetime": "2024-05-23",
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}
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