When building a Business Intelligence (BI) platform, I quickly realized that AI/ML projects aren’t just about algorithms—their success hinges on data infrastructure, accessibility, and integrity. From navigating JSON complexities in columns to choosing between Dedicated vs. Built-In SQL pools and overcoming infrastructure restrictions, data handling proved to be the real challenge.

However, these issues aren’t unique to any single organization. Different industries face similar hurdles, each solving them in unique ways.

Managing Complex JSON Data Across Industries

Many modern systems store semi-structured data in JSON format, posing challenges in retrieval and processing:

• E-commerce & Retail: Platforms like Amazon and Shopify frequently store user transactions, product details, and inventory data in JSON within databases. Optimizing query performance requires flattening JSON structures before ingestion into their data lakes.
• Healthcare: Electronic Health Records (EHRs) often have nested JSON formats containing patient data, diagnoses, and treatments. Data preprocessing is essential to make it usable for AI-driven predictive analytics.
• Finance & Banking: Transaction logs, fraud detection reports, and customer profiles often have JSON-stored metadata. Leading banks preprocess this data before feeding it into risk assessment models.

For our BI platform, we leveraged Azure Data Factory (ADF) and Pipelines to flatten JSON structures before ingestion into Synapse Analytics—a method similar to how FinTech and retail platforms optimize their data workflows.

Dedicated vs. Built-In SQL Pool Choices Across Sectors

The Dedicated vs. Built-In SQL pool dilemma is common in data-heavy industries:

• Retail & Consumer Analytics: Large retailers like Walmart and Target use Dedicated SQL pools for handling high-velocity transaction data while opting for Built-In SQL pools for smaller queries in operational dashboards.
• Healthcare & Life Sciences: Drug discovery companies often run Dedicated SQL pools for complex biomedical data processing, whereas hospital management systems prefer Built-In SQL pools for daily reports.
• Manufacturing & IoT: Factories rely on Built-In SQL pools for real-time monitoring of sensor data, while simulations involving large datasets use Dedicated SQL pools for batch processing.

For our BI platform, we used a hybrid approach—leveraging Built-In SQL pools for lightweight queries while Dedicated SQL pools handled bulk transformations and analytics tasks, mirroring techniques from retail and industrial AI solutions.

Handling Infrastructure Restrictions with Microservices—A Common Industry Challenge

Data access restrictions often stem from compliance and security policies, affecting how companies extract production data:

• Financial Institutions: Banks frequently prohibit direct queries into production databases, instead using Python-based microservices to securely fetch transaction records for analysis.
• Healthcare & Pharma: Due to HIPAA compliance, hospitals don’t allow direct cloud access to sensitive patient data, leading to workarounds like API-driven microservices for controlled data extraction.
• Enterprise IT & SaaS Platforms: Large enterprises block direct external access from production databases, requiring teams to build custom data connectors between local servers and cloud storage.

For our platform, we developed a Python-based microservice as an intermediary—securely extracting, formatting, and pushing data to Azure Storage—an approach commonly seen in FinTech and healthcare analytics.

Power BI—Bringing Data to Life Across Industries

Turning raw data into insights is the final step, and Power BI adoption spans multiple sectors:

• Retail: Companies like Nike and Sephora use Power BI to track supply chain analytics, customer sentiment, and product trends.
• Healthcare: Hospitals visualize patient trends, treatment effectiveness, and resource allocation using Power BI dashboards.
• Manufacturing: Industrial firms map equipment efficiency and predictive maintenance insights using Power BI integrations.

For our BI solution, we ensured clean data structuring so that Power BI dashboards delivered accurate, actionable insights without misleading trends—a method widely adopted by e-commerce and enterprise analytics platforms.

Final Thoughts

Building a BI platform wasn’t just about choosing the right tools—it was about ensuring the data was clean, structured, and accessible. Every industry faces these data challenges, from retail optimizing JSON-based transactions to healthcare structuring patient records and banks ensuring secure data pipelines.

This experience reinforced a fundamental truth: The hardest part of AI isn’t the model—it’s making sure the data is ready.

By navigating data integrity, pipeline architecture, SQL optimizations, and security hurdles, I gained firsthand insight into how real-world AI applications require engineering, compliance, and strategic decision-making—not just advanced algorithms.

Did you work on any Data Engineering or AI projects recently? I would love to know about your experience.