Row 46602

Content Data

This page contains data entry 46602 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

Raw Record

{
  "text": "Hey guys, I work as a Data Scientist in the finance department of a large corporation. Currently, I am developing a rolling forecast and simulation for sales, revenue, and EBIT on a monthly basis.\n\nOne significant challenge we face is the lack of structured data from our sales department, despite having comprehensive financial and production data. For example, we often don't have clear information on whether a contract is ending or being renewed, if a new contract has been signed and the service is being delivered, or if we are expected to lose a major customer in the future. This lack of structured sales data makes it difficult to accurately project our financial performance.\n\nTo address this issue, I am developing a tool that fulfills two main needs. First, it will gather and store information from the sales department in a database (simple CRUD). Second, it will allow my colleagues responsible for sales, revenue, and EBIT to override my forecast, save their adjustments in the database, and include comments. This tool will also preserve all adjustments and comments for future rolling forecasts and provide detailed commentary for the board. Additionally, it will improve compliance by ensuring that all data and modifications are systematically documented and traceable.\n\nMy major achievement was securing the head of sales' support to ensure the necessary information is provided.\n\nI am using the following technologies:\n\n* In my current setup, I use a cron job to automatically trigger the training, evaluation, and prediction processes for the model (combination of [Prophet](https://facebook.github.io/prophet/docs/quick_start.html) an [NBeats](https://pytorch-forecasting.readthedocs.io/en/latest/api/pytorch_forecasting.models.nbeats.NBeats.html)).\n* The model and its information are stored unfortunatly in a fake MongoDB called Azure Cosmo DB.\n* For the web development, I have chosen [Rio](https://github.com/rio-labs/rio), which works exceptionally well for my use case.\n\nSince we're not part of the corporate IT department, we lack the resources for frontend developers. We also don't want to allocate a significant portion of our budget and wait 6-12 months due to prioritization issues. So, we decided to try Rio, a relatively new framework. Honestly, integrating our current setup was relatively easy. I appreciate the Python-native approach and and responsiveness, as it fits our team well. Additionally, it addresses some major drawbacks compared to Streamlit.\n\nI'm currently working on a user management system and plan to publish a non-corporate version on GitHub. However, I'm curious about how you handle the lack of organized data in your forecasting models. I'd love to hear about your strategies and any advice you might have for improving my approach.",
  "label": "r/datascience",
  "dataType": "post",
  "communityName": "r/datascience",
  "datetime": "2024-05-22",
  "username_encoded": "Z0FBQUFBQm5Lak1Rb1ZJaEZkOWxZcFpHNzEtbDBTQ2lYU3NlQmdzRVZIM2pVNmFTN2hIS2N2ekVJNk9TUUtMRlhtVElCN1JJRDkwV0FVRkZac3FqdHkyR0t6OVZXOWM1YXc9PQ==",
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}

Entry Information

• Entry ID: 46602
• Repository: Axioma AXP
• Dataset: arrmlet/reddit_dataset_36
• Total Entries: 100,000