What is ETL and its advantage in Business Intelligence

Published on Oct 1, 2019

Updated on May 22, 2024

ETL (Extract, Transform, Load) is a process of extracting data from different data sources; manipulating them according to business calculations; loading the modified data into a different data warehouse. Because of the in-depth analytics data it provides, ETL function lies at the core of Business Intelligence systems. With ETL, enterprises can obtain historical, current, and predictive views of real business data. Let’s look at some ETL features that are necessary for business intelligence.

The Importance of ETL in Business Intelligence

Businesses rely on the ETL process for a consolidated data view that can drive better business decisions. The following ETL features justify the point.

High-level Data Mapping

Leveraging data and transforming them into actionable insights is a challenge with dispersed and voluminous data. Data mapping simplifies database functionalities like integration, migration, warehousing, and transformation.

ETL allows mapping data for specific applications. Data mapping helps in establishing a correlation between different data models.

Data Quality & Big Data Analytics

Huge volumes of data aren’t of much use in their raw form. Applying algorithms on raw data often leads to ambiguous results. It needs structuring, analyzing, and interpreting well to gain powerful insights. ETL also ensures the quality of data in the warehouse through standardization and removing duplicates.

ETL tools combine data integration and processing, making it easier to deal with voluminous data. In its data integration module, ETL assembles data from disparate sources. Post integration, it applies business rules to provide the analytics view of the data.

[Also read: Popular ETL Tools for 2020]

Automatic & Faster Batch Data Processing

The modern-day ETL tools run on scripts, which are faster than traditional programming. Scripts are a lightweight set of instructions that execute specific tasks in the background. ETL also ‘batch’ processes data like moving huge volumes of data between two systems in a set schedule.

Sometimes the volume of incoming data increases to millions of events per second. To handle such situations, stream processing (monitoring and batch processing data) can help in timely decision making. For example, Banks batch process the data generally during night hours to resolves the entire day’s transactions.

Master Data Management

Using ETL and data integration, enterprises can obtain the “best data view” across multiple sources.

How ETL Works?

ETL systems are designed to accomplish three complex database functions: extract, transform and load.

#1 Extraction

Here, a module extracts data from different data sources independent of file formats. For instance, banking and insurance technology platforms operate on different databases, hardware, operating system, and communication protocols. Also, their system derives data from a variety of touchpoints like ATMs, text files, pdfs, spreadsheets, scanned forms, etc. The extraction phase maps the data from different sources into a unified format before processing.

ETL systems ensure the following while extracting data.

Removing redundant (duplicate) or fragmented data
Removing spam or unwanted data
Reconciling records with source data
Checking data types and key attributes.

#2 Transformation

This stage involves applying algorithms and modifying data according to business-specific rules. The common operations performed in ETL’s transformation stage is computation, concatenation, filters, and string operations like currency, time, data format, etc. It also validates the following-

Data cleaning like adding ‘0’ to null values
Threshold validation like age cannot be more than two digits
Data standardization according to the rules and lookup table.

#3 Loading

Loading is a process of migrating structured data into the warehouse. Usually, large volumes of data need to be loaded in a short time. ETL applications play a crucial role in optimizing the load process with efficient recovery mechanisms for the instances of loading failures.

A typical ETL process involves three types of loading functions-

Initial load: it populates the records in the data warehouse.
Incremental load: it applies changes (updates) periodically as per the requirements.
Full refresh: It reloads the warehouse with fresh records by erasing the old contents.

The ETL systems validate the following data loading parameters-

The Business Intelligence report on view layer matches with the loaded facts
Data consistency between the data warehouse and the history table.
Models are based on transformed data and not the raw data from the original databases.

The modern-day ETL applications utilize NoSQL database systems for warehousing. NoSQL systems are suitable for big-data and real-time web-applications. NoSQL executes queries faster than traditional databases and is more memory efficient.

ETL Business Applications

Transactional databases are not enough to resolve complex business queries. Also, dealing with unorganized data formats is more time-taking. ETL can help in obtaining-

Memory efficiency
Real-time query processing
Mapping data historical, current, and predictive data to derive actionable insights
Smart data storage and retrieval.

Almost all industries can deploy the benefits of ETL systems. However, businesses like banking, insurance, customer relations, finance, and healthcare are the early adopters of this technology.

If your business needs intelligent data processing, we’re here to listen to your requirements. Drop us a word at hello@mantralabsglobal.com to know about our previous works on developing ETL applications.

Generic AI is no longer enough. Domain-specific AI is the new enterprise advantage.

From hospitals to factories to insurance carriers, organizations are learning the hard way: horizontal AI platforms might be impressive, but they’re often blind to the realities of your industry.

Here’s the new playbook: intelligence that’s narrow, not general. Context-rich, not context-blind.
Welcome to the age of domain-specific AI agents— from underwriting co-pilots in insurance to care journey managers in hospitals.

Why Generalist LLMs Miss the Mark in Enterprise Use

Large language models (LLMs) like GPT or Claude are trained on the internet. That means they’re fluent in Wikipedia, Reddit, and research papers; basically, they are a jack-of-all-trades. But in high-stakes industries, that’s not good enough because they don’t speak insurance policy logic, ICD-10 coding, or assembly line telemetry.

This can lead to:

Hallucinations in compliance-heavy contexts
Poor integration with existing workflows
Generic insights instead of actionable outcomes

Generalist LLMs may misunderstand specific needs and lead to inefficiencies or even compliance risks. A generic co-pilot might just summarize emails or generate content. Whereas, a domain-trained AI agent can triage claims, recommend treatments, or optimize machine uptime. That’s a different league altogether.

What Makes an AI Agent “Domain-Specific”?

A domain-specific AI agent doesn’t just speak your language, it thinks in your logic—whether it’s insurance, healthcare, or manufacturing.

Here’s how:

Context-awareness: It understands what “premium waiver rider”, “policy terms,” or “legal regulations” mean in your world—not just the internet’s.
Structured vocabularies: It’s trained on your industry’s specific terms—using taxonomies, ontologies, and glossaries that a generic model wouldn’t know.
Domain data models: Instead of just web data, it learns from your labeled, often proprietary datasets. It can reason over industry-specific schemas, codes (like ICD in healthcare), or even sensor data in manufacturing.
Reinforcement feedback: It improves over time using real feedback—fine-tuned with user corrections, and audit logs.

Think of it as moving from a generalist intern to a veteran team member—one who’s trained just for your business.

Industry Examples: Domain Intelligence in Action

Insurance

AI agents are now co-pilots in underwriting, claims triage, and customer servicing. They:

Analyze complex policy documents
Apply rider logic across state-specific compliance rules
Highlight any inconsistencies or missing declarations

Healthcare

Clinical agents can:

Interpret clinical notes, ICD/CPT codes, and patient-specific test results.
Generate draft discharge summaries
Assist in care journey mapping or prior authorization

Manufacturing

Domain-trained models:

Translate sensor data into predictive maintenance alerts
Spot defects in supply chain inputs
Optimize plant floor workflows using real-time operational data

How to Build Domain Intelligence (And Not Just Buy It)

Domain-specific agents aren’t just “plug and play.” Here’s what it takes to build them right:

Domain-focused training datasets: Clean, labeled, proprietary documents, case logs.

Taxonomies & ontologies: Codify your internal knowledge systems and define relationships between domain concepts (e.g., policy → coverage → rider).
Reinforcement loops: Capture feedback from users (engineers, doctors, underwriters) and reinforce learning to refine output.
Control & Clarity: Ensure outputs are auditable and safe for decision-making

Choosing the Right Architecture: Wrapper or Ground-Up?

Not every use case needs to reinvent the wheel. Here’s how to evaluate your stack:

LLM Wrappers (e.g., LangChain, semantic RAG): Fast to prototype, good for lightweight tasks
Fine-tuned LLMs: Needed when the generic model misses nuance or accuracy
Custom-built frameworks: When performance, safety, and integration are mission-critical

Use Case	Reasoning
Customer-facing chatbot	Often low-stakes, fast-to-deploy use cases. Pre-trained LLMs with a wrapper (e.g., RAG, LangChain) usually suffice. No need for deep fine-tuning or custom infra.
Claims co-pilot (Insurance)	Requires understanding domain-specific logic and terminology, so fine-tuning improves reliability. Wrappers can help with speed.
Treatment recommendation (Healthcare)	High risk, domain-heavy use case. Needs fine-tuned clinical models and explainable custom frameworks (e.g., for FDA compliance).
Predictive maintenance (Manufacturing)	Relies on structured telemetry data. Requires specialized data pipelines, model monitoring, and custom ML frameworks. Not text-heavy, so general LLMs don’t help much.

Strategic Roadmap: From Pilot to Platform

Enterprises typically start with a pilot project—usually an internal tool. But scaling requires more than a PoC.

Here’s a simplified maturity model that most enterprises follow:

Start Small (Pilot Agent): Use AI for a standalone, low-stakes use case—like summarizing documents or answering FAQs.

Make It Useful (Departmental Agent): Integrate the agent into real team workflows. Example: triaging insurance claims or reviewing clinical notes.
Scale It Up (Enterprise Platform): Connect AI to your key systems—like CRMs, EHRs, or ERPs—so it can automate across more processes.

Think Big (Federated Intelligence): Link agents across departments to share insights, reduce duplication, and make smarter decisions faster.

What to measure: Track how many tasks are completed with AI assistance versus manually. This shows real-world impact beyond just accuracy.

Closing Thoughts: Domain is the Differentiator

The next phase of AI isn’t about building smarter agents. It’s about building agents that know your world.

Whether you’re designing for underwriting or diagnostics, compliance or production—your agents need to understand your data, your language, and your context.

Ready to Build Your Domain-Native AI Agent?

Talk to our platform engineering team about building custom-trained, domain-specific AI agents.

Further Reading: AI Code Assistants: Revolution Unveiled

Role of ETL in Business Intelligence