Uddan Promotions
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Executive Summary
Data is oil, but analytics is the engine. Most companies are stuck at "Reporting".
Data Analytics
13 min read
The Data Analytics Maturity Model
Moving from Descriptive (What happened?) to Predictive (What will happen?) and Prescriptive (How do we make it happen?) analytics.
Contents
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Problem Statement
Data silos prevent a 360-degree customer view.
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Strategic Framework
Descriptive -> Diagnostic -> Predictive -> Prescriptive.
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Recommended Tech Stack
To execute this strategy effectively, we recommend the following tooling:
Snowflake/BigQuery
dbt
Looker/Tableau.
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Step-by-Step Implementation
Step 1
Centralization: ETL all data to a Warehouse.
Step 2
Governance: Define metrics (What is "Active User"?).
Step 3
Visualization: Dashboards for stakeholders.
Step 4
ML Modeling: Churn prediction.
Step 5
Activation: Push segments to marketing tools (Reverse ETL).
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Common Pitfalls
Garbage In, Garbage Out.
Compliance
GDPR/CCPA Data Minimization.
Key Metrics (KPIs)
Data Quality Score, Dashboard Usage.
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Real World Impact
"Retailer predicted stock-outs 2 weeks in advance."
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Future Outlook
Auto-ML and NLP querying.
Conclusion
Climb the maturity ladder step by step.
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