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Welcome to BIPro #111: Expert Insight Edition

We’re excited to introduce Sagar Lad, Lead Data Solution Architect at a leading Dutch bank, as our newest Expert Insight contributor. Each week, Sagar will share battle-tested lessons, practical tips, and implementation strategies for building resilient data products in the Gen AI and Agentic AI era.

He kicks off this week with a deep dive into Data Products: Turning Data into Tangible Value, showing how to move from concept to measurable business impact. This marks the start of a series designed to help you apply hard-won expertise directly into your data practice.

Alongside Sagar’s expert guidance, here are this week’s top stories in data & BI:

🔗 Zero-Latency Data Analytics for PostgreSQL Apps – AWS introduces zero-ETL integration from RDS PostgreSQL to Redshift, enabling real-time analytics without fragile pipelines.
🔗Amazon Q Developer CLI – From theory to practice: AI-powered project advisor that turns AWS certification knowledge into hands-on portfolio projects.
🔗Map Visualization in BigQuery Studio – GA launch brings geospatial queries to life, letting analysts instantly visualize and interact with Earth Engine datasets.
🔗Amazon Timestream for InfluxDB – Expanded global rollout with compute/storage scaling, Multi-AZ replicas, and InfluxDB 3 migration path for time series workloads.
🔗Power BI Semantic Model Refresh Templates (Preview) – Orchestrate refreshes with Fabric Data Pipelines, supporting event-driven triggers, incremental refreshes, sequencing, and alerts.

As always, our mission is to connect you with first-hand expert insights and timely news updates, so you can stay ahead of change while sharpening your craft.

Cheers,

Merlyn Shelley

Growth Lead, Packt

In today’s digital economy, data has become one of the most valuable assets for organizations. Every transaction, interaction, and process generates data that — when properly harnessed — can unlock powerful insights, drive innovation, and create competitive advantages. However, simply collecting and storing vast amounts of data is not enough. To truly realize its value, organizations must transform data into usable, scalable, and outcome-driven solutions. This is where the concept of adata productcomes into play.

A data product is not just raw data, but rather a packaged, consumable, and value-generating asset built on top of data. Just as traditional products solve customer needs, data products solve business challenges by delivering insights, predictions, or automated decisions in a way that is accessible and reliable for end users.

What is a Data Product?

At its core, adata productis a solution designed around data to serve a specific purpose or generate business value. It could take many forms — such as a dashboard, an API serving machine learning predictions, a recommendation engine, or even a dataset curated for a particular domain.

For example:

→ Netflix’s recommendation systemis a data product built to enhance user engagement.

Characteristics of a data product include:

1. Purpose-driven— It is built to achieve a clear outcome (e.g., increase sales, reduce costs, improve customer satisfaction).

2. Reusable— A well-designed data product can serve multiple teams or applications.

3. Consumable— It is packaged in a way that non-technical users or systems can leverage it seamlessly.

4. Scalable— It is designed to evolve with changing business needs and data volumes.

Data Product: Bridge between Producer & Consumer

Data Products vs. Data Assets

It is important to differentiate betweendata assetsanddata products.

Adata assetcould be a data lake, warehouse, or dataset that stores raw or processed data. While valuable, assets by themselves may not generate outcomes unless someone analyzes them.

Adata product, on the other hand, transforms these assets into actionable, consumable outputs that stakeholders can directly use to make decisions or power business processes.

In other words, data assets are ingredients, while data products are the finished dishes that customers can consume.

Why Do Organizations Need Data Products?

Organizations often struggle with extracting value from their data investments. Billions of dollars are spent globally on data platforms, yet many businesses face the“last mile problem”— where insights fail to reach decision-makers in a meaningful way. Data products help bridge this gap by operationalizing data and embedding it into workflows.

Key benefits of data products include:

1. Faster Decision-Making
With well-packaged insights, business users don’t need to spend hours querying databases or waiting for reports. A data product like a sales forecasting model can instantly provide actionable intelligence.

2. Democratization of Data
Data products abstract technical complexity, enabling business users, analysts, and applications to easily consume data-driven insights.

3. Standardization and Reusability
Instead of rebuilding analytics pipelines repeatedly, a single data product can serve multiple business units. For example, a customer segmentation data product could be reused by marketing, sales, and product teams.

4. Scalability and Automation
Data products, once designed, can be scaled to handle growing data volumes and embedded into automated workflows.

5. Value Realization
Ultimately, data products help organizations move beyond storing data tomonetizing and operationalizing it— whether through cost savings, revenue generation, or improved customer experiences.

Key Principles for Designing Data Products

Designing a successful data product requires more than technical skills — it requires product thinking. Some guiding principles include:

1.Start with Business Value
A data product must solve a real business problem. Before building, clearly define the outcome it should drive.

2. User-Centric Design
The product should be intuitive for its target users, whether that’s executives, developers, or customers.

3. Trust & Transparency
Users must trust the data product. This requires data quality checks, explainability in AI models, and governance measures.

4. Scalability & Reusability
Build products that can adapt to future needs, serve multiple stakeholders, and scale across datasets and domains.

5. Operationalization
A data product should integrate seamlessly into business workflows and systems, rather than existing as a standalone artifact.

6. Monitoring & Improvement
Data products must be continuously monitored for performance, accuracy, and relevance, with feedback loops for improvements.

Challenges in Building Data Products

While data products are powerful, organizations face challenges in creating and scaling them:

1. Data Quality Issues: Poor data leads to unreliable products.

2. Cultural Resistance: Teams may hesitate to trust automated insights.

3. Lack of Product Mindset: Many companies treat data as IT projects, not products.

4. Scalability Hurdles: A data product may work for a pilot but struggle in enterprise-wide deployments.

5. Governance & Compliance: Ensuring data products adhere to regulatory and ethical standards is critical.

Overcoming these requires strongdata governance, clear ownership, cross-functional collaboration, and a product-centric approach.

Read the full article on our Packt Medium page, and don’t forget to follow us for more expert insights like this.

🔳 Power BI August 2025 Feature Summary: What if Power BI could think smarter for you? The August 2025 Feature Summary delivers Copilot in SharePoint Online, automated measure descriptions, and filtered report summaries. Plus, Pro workspace org apps, advanced modeling with Databricks Direct Lake, new connectors, and visual upgrades, all boosting speed, scale, and smarter insights.

🔳 Benchmarking AWS S3 Performance With Python Scripts: Ever wondered if your AWS S3 storage is really as fast or cheap, as promised? This guide shows how Python benchmarking uncovers latency and throughput trade-offs across storage classes. Learn setup, key metrics, practical scripts, and best practices to balance cost and performance while avoiding hidden bottlenecks.

🔳 Zero-Latency Data Analytics for Modern PostgreSQL Apps: Tired of building fragile data pipelines? AWS now offers zero-ETL integration from RDS PostgreSQL to Redshift, announced July 23, 2025. Stream transactional data in seconds, apply filters per integration, and even automate setup with CloudFormation, unlocking real-time analytics and ML without the overhead of ETL.

🔳 Using Google’s LangExtract and Gemma for Structured Data Extraction: Struggling to make sense of dense documents? Google’s LangExtract framework with Gemma 3 LLM makes structured data extraction from long unstructured text faster, smarter, and traceable. With chunking, parallel processing, and iterative passes, it surfaces key facts, like insurance policy exclusions, turning legalese into structured, plain-English insights you can actually use.

🔳 From theory to practice using Amazon Q Developer CLI to generate tailored AWS projects: Too much AWS theory, not enough practice? That’s where most learners stall. Amazon’s Q Developer CLI transforms study into action by suggesting skill-matched projects and guiding implementation with CLI commands. From S3 websites to CloudFront and IaC, it makes building practical, portfolio-ready AWS projects achievable for everyone.

🔳 Zero-ETL: How AWS is tackling data integration challenges? In this blog post, we explore how AWS is simplifying data integration with zero-ETL, replacing fragile pipelines with real-time replication. From Aurora, RDS, and DynamoDB to Redshift and SageMaker, zero-ETL reduces costs, handles schema changes automatically, and delivers near-instant analytics, transforming integration from an engineering burden into a strategic advantage.

🔳 Amazon Timestream for InfluxDB: Expanding managed open source time series databases for data-driven insights and real-time decision making? Time series data is exploding, powering everything from IoT to gaming. AWS Timestream for InfluxDB now spans 19 Regions, offering compute/storage scaling, Multi-AZ replicas, and 24xlarge instances. Backed by a strategic partnership with InfluxData, customers gain real-time analytics, high cardinality support, and a migration path to InfluxDB 3.

🔳 Firestore with MongoDB compatibility is now GA: At Google Cloud Next ’25, Firestore previewed MongoDB compatibility, and it’s now generally available. Developers can reuse MongoDB code, drivers, and tools with Firestore’s serverless database, gaining multi-region replication, 99.999% availability, PITR recovery, triggers, and enterprise-grade security. With expanded API support and Firebase access, Firestore delivers scalable, cost-efficient document storage.

🔳 Earth Engine raster analytics and visualization in BigQuery geospatial: Geospatial data holds untapped business value. Now generally available, Earth Engine in BigQuery lets analysts join satellite imagery with structured data for richer insights, from disaster risk to supply chain planning. Paired with new map visualization in BigQuery Studio, it transforms complex geospatial queries into intuitive, interactive decision-making tools.

🔳 Semantic Model Refresh Templates in Power BI (Preview). Keeping data models fresh can be complex. With Semantic Model Refresh Templates in Power BI (preview), you can orchestrate refreshes using Fabric Data Pipelines, supporting event-driven triggers, incremental refresh, sequencing multiple models, and automated alerts. The guided setup makes advanced refresh workflows easier, faster, and more reliable for analysts.

See you next time!