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Driver

Lavanya Davluri Igor Rahal Linkewitsch

Approver

Igor Rahal Linkewitsch Zahra Lakhani

Contributors

Lavanya Davluri Igor Rahal Linkewitsch Grace Jin (Deactivated) JinZhao Wang Anirudha Porwal Shawn Zou Zhiyi Wang

Informed - Phase 1

Scott Williams Amanda Khan (Deactivated) Adrian Monk

Objective

Automate reporting

Due date

2023-04-06

Key outcomes

Phase 1 - Automate Weekly execution

Status

Status
colourYellow
titlein progress

\uD83E\uDD14 Problem Statement

Our current manual reporting process for Report Weekly Execution Report is time-consuming and prone to errors. We need to replicate the manual report and create a fully automated process for collecting, analyzing, and presenting the data.

🎯 Scope

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Must have:

...

  • Weekly execution - transactions and guest-level data

  • Replicate charts into tableau from the weekly execution deck

...

Nice to have:

...

  • Points reporting

  • Offer dashboard views

...

Not in scope:

...

  • App MAU and App Features

  • Special views of Weekly Execution ex. RUTW

...

Jira Legacy
serverSystem JIRA
serverId255417eb-03fa-3e2f-a6ba-05d325fec50d
keyAA-2661

🎯 Objective

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🗓️ Timeline

Roadmap Planner
maplinks
timelinetrue
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pagelinks
titleRoadmap%20Planner
hash61a72a835ee54a693a38ca7014c4abbd
Info

Jira Legacy
serverSystem JIRA
serverId255417eb-03fa-3e2f-a6ba-05d325fec50d
keyAA-2661

🥅 Objective

...

768cfb5bf6f790f009dba45817f47c57

💡 Plan

Stage

Objective

Key results

KPI Definitions

Lavanya Davluri

  •  Define KPI’s
  •  Design view
  •  Drill Down Details
  •  Setting a cadence
  • Reduction in Ad hoc requests

  • Teams have access to most tracked KPIs via dashboard

Introductions

Lavanya Davluri

  •  Introduce BI project
  •  Gather feedback on pain points  in current state reporting
  •  Get details on views needed
  • Excitement for dashboards

  • Buy in

Data Engineering

JinZhao Wang

Zhiyi Wang

  •  Translate requirements into datasets
  •  Create Pipelines for new sources
  • Fast, reliable and centralized report with well defined data

QA

Shawn Hou (Deactivated) Igor Rahal Linkewitsch Lavanya Davluri

  •  QA code/Logic
  •  QA Data

Dashboarding

Zhiyi Wang JinZhao Wang

  •  Create visualizations that are usable and fast
  •  Allow capacity to drill down and download
  • Consistent usage from intended audience

Documentation

Lavanya Davluri

  •  Document available dashboards
  •  Definitions, Owners and collaborators
  • Most questions are answered by the confluence page

Training/Handoff

Lavanya DavluriIgor Rahal Linkewitsch

  •  Onboard team members of the vertical to start using reporting from BI stream
  • Active usage on dashboards

  • Decrease in Ad-hoc data requests 

  • Ease of access to data

Ongoing Maintenance <TBD>

(blue star)  Process

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🏗️ Phases

Phase 1

Phase 2

Phase 3

Weekly Execution - Automation

Executive Summary and Loyalty

TBD

Weekly Execution - Report

TBD

TBD

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Phase 1 - Weekly Execution - Automation

🤔 Problem Statement

Our current manual reporting process for Report Weekly Execution

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TBD

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Report is time-consuming and prone to errors. We need to replicate the manual report and create a fully automated process for collecting, analyzing, and presenting the data.

 👾 Scope

Must have:

  • Weekly execution - transactions and guest-level data

  • Replicate charts into tableau from the weekly execution deck

Nice to have:

  • Points reporting

  • Offer dashboard views

Not in scope:

  • App MAU and App Features

  • Special views of Weekly Execution ex. RUTW

...

Panel
panelIconId1f517
panelIcon:link:
panelIconText🔗
bgColor#EAE6FF

Reference materials

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Databricks Notebook: Weekly Execution - All Codes Combined - Reference Notebook

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