DataOps in Data Analysis world

Abstract

Introduction

Before launching a new lactose-free product, a company needs to understand market demand and consumer needs. Data analysis is essential for identifying what enhancements are needed and ensuring a product isn't released without proper market understanding.

Case Study Overview

A new company plans to launch a lactose-free product in a city. To assess market feasibility, they collect and analyze data from grocery stores and healthcare centers.

Key Areas of Study:

1. Target Audience Identification:

   • Frequency of lactose-free product purchases

   • Age demographics of consumers

   • Number and age group of lactose-intolerant patients

2. Feasibility Study:

   • Comparison with current leading products

   • Survey of families interested in alternatives

   • Complaints against existing products

3. Cost Analysis:

   • Pricing of current leading lactose-free products

Using DataOps in the Survey

What is DataOps?

DataOps is a methodology inspired by DevOps and Agile, aiming to streamline the entire data lifecycle—from collection and cleaning to validation, transformation, and orchestration—ensuring collaboration and data quality for decision-making.

End-to-End DataOps Workflow for Survey

1. Raw Data Collection:

   • Survey responses collected in CSV format (e.g., participant demographics, lactose intolerance status, dairy/lactose-free usage frequency)

2. Data Processing (Python):

   • Cleans the data (standardizes fields, formats timestamps, renames columns)

   • Uses Airflow for automation

3. Data Quality Check (Great Expectations):

   • Ensures fields like gender, usage frequency, and diagnosis values meet expectations

   • Flags missing or invalid entries before further processing

4. Data Modeling (dbt):

   • Aggregates cleaned data (e.g., by age group, gender, diagnosis) into summary tables for analysis

5. Workflow Orchestration (Apache Airflow DAG):

   • Automates the pipeline:

     • Step 1: Clean raw data

     • Step 2: Validate cleaned data

   • Ensures all tasks run in correct order with dependency management

Copyright

Copyright © 2025 Justin Pathrose James. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.