Explain Your Last Project. MDM Project With Tyson Foods (2023)

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Need a write up explaining my last project at Infosys on MDM for interview at my current client.

Here is the project decription from my resume:

Master Data Management for Tyson Foods (US)
Developed lazy learning algorithms based around simple statistics to harmonize UPCs and abbreviated item descriptions
with product attributes like brand, manufacturer, category, protien, packaging, etc.

I'd be glad to help you craft an explanation of your MDM project at Infosys for your current client. Here's a structured approach you can tailor to your specific project:
Context (1-2 sentences):
Briefly introduce Master Data Management (MDM) and its role in ensuring data accuracy and consistency across an organization.
Project Goal (1-2 sentences):
State the specific challenge or business need your MDM project at Infosys addressed.
Focus on Client Value (2-3 sentences):
Explain how your project improved data quality and delivered value to Infosys's client. Quantify the impact if possible (e.g., reduced costs, improved efficiency, enhanced customer satisfaction).
Technical Details (2-3 sentences, optional):
If relevant to your client's needs, you can mention the type of MDM solution implemented (e.g., customer MDM, product MDM) and any specific tools or technologies used.
Key Achievements (2-3 sentences):
Highlight the key accomplishments of your project. This could include:
Establishing a single source of truth for critical data (e.g., customer master data)
Eliminating data duplication and inconsistencies
Improving data governance processes
Enabling better reporting and analytics
Client-Specific Applications (1-2 sentences):
Briefly connect your MDM project experience to your current client's situation.
How can they benefit from similar MDM practices?
Are there any challenges they face that your MDM expertise can address?
Example:
At Infosys, I led a Master Data Management (MDM) project that focused on improving the accuracy and consistency of customer data across multiple systems. This initiative helped us create a single source of truth for customer information, which significantly reduced errors in marketing campaigns and improved customer service efficiency. By implementing a customer MDM solution, we were able to streamline data management processes and generate more accurate reports for business insights.  In your case, I believe a similar MDM approach could help you [mention a specific challenge your client faces related to data management].
Remember to tailor this approach to your specific project and client situation. Focus on the value delivered and how your expertise can benefit your current client.

My last project at Infosys was in MDM (which stands for Master Data Management) for Tyson Foods.

Project Goal:
Tyson Foods wanted to capture accurate details about items sold via POS devices in it's reporting tools, visualizations and reports for sales and marketing teams.

Focus on client value:
The existing system of managing data about sold items was done by Data Stewards. With MDM in place, Tyson Foods was trying to reduce work load on Data Stewards and make the process more automated. This initiative led to reduced costs and expenses on human effort and led to overall improved efficiency by reducing the input required by human intervention.

Technical details:

Basically, the type of MDM solution implemented for Tyson Foods was a 'Product MDM' and it was for retailers like Kroger, IRI, Walmart, Amazon, Instacart, Meijer, among others.

The data was uploaded to a file server by the client in the form of flat files.
The flat files were then read by Python scripts in the GCP to load the data into Postgre SQL databases.
From PostgreSQL the data was loaded into Pandas dataframes and PySpark dataframes, from where it was then processed by lazy learning algorithms.

These were simple algorithms based on a couple of techniques such as:

1. First word model : Used to create a "hierarchical classifier" that started from brand, manufacturer, then megacategory, then category, then subcategory, then protien, packaging, weight, bone, etc.
Starting point is the first word that is an abbreviation for brand for most retailers.

2. Bag of words model 

3. n-gram model 

Along with the AI/ML was in order the "Reject Option" that was used if model's confidence was lower than a threshold and Tyson Foods needed a human reviewer to fill in the details for a record.

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