Row 19436

Row ID: 19436 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 19436 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

Sure - the biggest challenge I've seen is missing the context around what they're doing. It usually manifests in a few ways -

1. Missing the real question that's being asked. We usually get asked the immediate question, and the real question is much more complicated and buried somewhere underneath it. "What is driving the change to projections this quarter?" might well mean "I don't feel like I have enough visibility into my business."

2. Not tying work to incentives of the stakeholders, aka not explaining how the model is going to make or save the business money. A lot of data scientists think this part is self-explanatory, but to people who aren't neck deep in data, it's often not.

3. Missing the "real" stakeholders. Often the end user of your product isn't the person you're presenting the work to. If we don't understand who is going to be actually using the output of our work, we can't design it in a way that helps them, and even if it gets deployed, it won't be used to it's potential, and someone will come back to us and blame us for developing a poor product.

4. Not articulating a path to production. A lot of good models fail here because they're hard or impractical to operationalize. I once worked with a team who had a model that took 26 hours to run... and ran every day.

The (easy to say, hard to do) solution is to think through the project from concept to delivery, and think about the stuff we don't normally think about - what the real question is, and how the solution is going to be delivered.

There's more to unpack than that, but those are some themes. :)

FieldValue
text Sure - the biggest challenge I've seen is missing the context around what they're doing. It usually manifests in a few ways - 1. Missing the real question that's being asked. We usually get asked the immediate question, and the real question is much more complicated and buried somewhere underneath it. "What is driving the change to projections this quarter?" might well mean "I don't feel like I have enough visibility into my business." 2. Not tying work to incentives of the stakeholders, aka …
label r/datascience
dataType comment
communityName r/datascience
datetime 2024-05-21
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Raw Record

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  "text": "Sure - the biggest challenge I've seen is missing the context around what they're doing. It usually manifests in a few ways - \n\n1. Missing the real question that's being asked. We usually get asked the immediate question, and the real question is much more complicated and buried somewhere underneath it. \"What is driving the change to projections this quarter?\" might well mean \"I don't feel like I have enough visibility into my business.\"\n\n2. Not tying work to incentives of the stakeholders, aka not explaining how the model is going to make or save the business money. A lot of data scientists think this part is self-explanatory, but to people who aren't neck deep in data, it's often not. \n\n3. Missing the \"real\" stakeholders. Often the end user of your product isn't the person you're presenting the work to. If we don't understand who is going to be actually using the output of our work, we can't design it in a way that helps them, and even if it gets deployed, it won't be used to it's potential, and someone will come back to us and blame us for developing a poor product. \n\n4. Not articulating a path to production. A lot of good models fail here because they're hard or impractical to operationalize. I once worked with a team who had a model that took 26 hours to run... and ran every day. \n\nThe (easy to say, hard to do) solution is to think through the project from concept to delivery, and think about the stuff we don't normally think about - what the real question is, and how the solution is going to be delivered. \n\nThere's more to unpack than that, but those are some themes. :)",
  "label": "r/datascience",
  "dataType": "comment",
  "communityName": "r/datascience",
  "datetime": "2024-05-21",
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}
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