Let It Snow ?? In Educational Institutes Now !!

?? Introduction

Educational institutes face several challenges in maintaining hard copies of documents, including:

1. Storage Space: Physical files require a significant amount of space for proper storage, leading to crowded offices and expensive real estate for file cabinets or storage rooms.
2. Organization and Accessibility: Sorting and organizing hard copies can be time-consuming and inefficient. Finding specific documents can take longer, especially if records are not properly indexed or categorized.
3. Document Loss or Damage: Physical documents are susceptible to damage from fire, water, pests, or natural disasters. Over time, papers can also degrade, leading to the potential loss of important information.
4. Security Risks: Hard copies are vulnerable to unauthorized access, theft, or tampering. Ensuring the security of sensitive data, like student records, becomes difficult without proper systems in place.
5. High Operational Costs: The cost of paper, printing, and physical storage materials, as well as the labor required for filing and managing documents, can add up over time.
6. Limited Collaboration: Sharing hard copies among staff and departments is inefficient, as physical documents need to be transported and cannot be accessed remotely, hindering timely collaboration.
7. Environmental Impact: The constant need for paper leads to high paper consumption, which contributes to deforestation and environmental degradation.

These challenges highlight the inefficiency of relying solely on hard copies for managing educational records and underscore the growing need for digital solutions.

Despite of all the above-mentioned challenges still these institutes maintain various hard copies to ensure smooth functioning and proper record-keeping. Some common categories of hard copies that are typically maintained in such institutions include:

1. Student Records
2. Admission forms
3. Enrollment forms
4. Transcripts and mark sheets
5. Attendance registers
6. Student personal details and contact information
7. Disciplinary records
8. Application forms for scholarships or financial aid
9. Medical records (if applicable)

??General Proposed Solution??

Hire Data Entry Operators to enter all these details in an On-Prem or Cloud Systems where teams come across multiple challenges daily, some of them are listed below:

1. Repetitive and Monotonous Tasks
2. Errors and Accuracy
3. Physical Strain
4. Time Pressure
5. Inconsistent or Unclear Instructions
6. Data Quality Issues
7. Lack of Motivation and Engagement
8. Technical Challenges
9. Security and Confidentiality
10. Lack of Advancement

?? Snowflake Based Solution??

Architecture Diagram:

Data Ingestion Phase:

Here I see one of the best use case of Snowflake Cortex AI. Lets deep dive in below points:

1. At first we will upload all scanned copies of those documents as pdfs in Snowflake Stage.
2. We will now initiate a Snowflake Document AI model which will be trained on sample documents with as optimize as possible to eradicate out the possibilities of over training and under training.
3. Based on different documents we will perform feature engineering to find the best possible columns for the tables for each document.
4. Create tables in Snowflake for each document types.
5. Run the Document AI Model in Snowflake to ingest the data in those tables.

Data Visualization Phase:

In this phase we will understand the need to make interactive dashboards in Snowflake itself:

1. Data Centralization and Integration: Snowflake natively integrates with a variety of data sources and supports modern data integration patterns. This can simplify data management without the need for additional data connectors or extraction steps that tools like Power BI might require.

2. Performance and Scalability: Snowflake can run complex queries with high performance thanks to its unique architecture (separation of compute and storage). This means you can work with large datasets efficiently and create high-performance dashboards.

3. Cost Efficiency: By performing data transformations and visualizations within Snowflake, you avoid unnecessary data transfer costs between the warehouse and an external BI tool.

4. Data Transformation and Calculations: Snowflake supports advanced analytics (e.g., machine learning models, Python integration) within the warehouse itself, which could be integrated directly into dashboards, providing deeper insights from raw data.

5. Real-Time Data Access: If your data resides directly in Snowflake, you don’t have to worry about syncing data between Snowflake and Power BI (or another BI tool), ensuring that dashboards reflect the most accurate and current data at all times.

6. Customizable and Flexible Dashboards: While Power BI is a popular visualization tool, Snowflake can work with a variety of BI tools, including Power BI. But Snowflake itself supports creating simple dashboards using SQL queries or integrating with third-party dashboarding tools via connectors. You have the flexibility to design dashboards that meet specific performance needs, using a combination of Snowflake's computational power and integration capabilities.

7. Security and Governance: Managing user access and permissions can be streamlined by keeping everything within Snowflake, avoiding the need for disparate systems or complex role management across tools.

8. Simplified Management and Maintenance: Snowflake handles infrastructure scaling automatically, so you don't need to worry about performance bottlenecks or manual scaling, which might be a concern in traditional BI tools like Power BI.

9. Better for Data Science and Advanced Analytics: If your organization is using machine learning, artificial intelligence, or other advanced analytics techniques, Snowflake supports integration with these processes, which can then feed directly into your dashboards, providing enhanced insights that might not be as easily achievable with Power BI alone.

Chatbot Phase:

As now the data will reside my Snowflake table we can easily build a Streamlit Chatbot application using Snowflake Cortex Analyst / Cortex Search.

Read Matertial:

https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst