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Introduction to edw aka enterprise data warehouse

An Enterprise Data Warehouse (EDW, among friends) is a data hub that collects and stores data from different sources across an organization and makes it available for analytics and improved decision making. This introduction to EDW should help you understand the concept and how it enhances your ability to unlock the value of your data.




What is an Enterprise Data Warehouse (EDW)?

An enterprise data warehouse (EDW) is a centralized and integrated repository that gathers, organizes, and stores large volumes of structured and unstructured data from diverse sources within an organization. 

Still a bit hard to grasp? Here’s the same in more simple terms:

An enterprise data warehouse (EDW) is like a massive, organized library that collects and stores all sorts of information from different places in a company. 

And why should you care?

Well, the EDW serves as the single source of truth, providing a comprehensive and consistent view of enterprise-wide data, which empowers businesses to: 

  • Extract valuable insights from data
  • Make informed decisions
  • Drive strategic initiatives with heightened efficiency, agility, and competitive advantage
  • Grow faster and better



Cornerstones of the EDW architecture

The technical architecture of and around an enterprise data warehouse involves various components and systems that work together to manage and process data efficiently. These are some of the central parts and their roles:

Data sources

These are the origin points where data is generated or collected. They can include transactional databases, CRM systems, ERP software, IoT devices, social media platforms, and more. Data from these sources is extracted for integration into the EDW.

ETL and ELT processes 

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) tools and processes are responsible for the movement and transformation of data from various sources. The workflows involve extracting data from different sources, transforming it into a consistent format, and loading it into the EDW, and are a central part of data quality management.

Staging area 

Data extracted from source systems often goes through a staging area before being loaded into the EDW. This area acts as an intermediary storage space where data can be cleaned, transformed, and checked for accuracy before entering the EDW.

Data warehouse database 

The core of the EDW, and home to all integrated and transformed data. It's designed for optimized querying and reporting and typically follows a schema optimized for analytics.

Metadata repository

This component stores information about the data stored in the EDW (= data about data). It includes details like data definitions, relationships, data lineage, and other attributes crucial for understanding and managing the data effectively.

Business intelligence (BI) and analytics tools

These tools sit on top of the EDW, allowing users to access, query, analyze, and visualize data stored in the data warehouse. They include reporting tools, dashboards, data mining tools, and more.

Integration with external systems 

EDWs often need to interact with other systems like CRM, ERP, or specialized analytical tools. Integration methods, APIs, and middleware facilitate seamless communication between these systems on the outer rim of the enterprise DWH architecture.




Importance of EDW for data-driven business

So, how do you know if investing in an enterprise data warehouse is worth it?

Who is the enterprise data warehouse for?

If you ask me, there isn’t an organization in existence that wouldn’t benefit from more consistent and efficient data work. But it also makes sense that enterprise data warehouses, as the name suggests, are even more suited for certain circumstances. 

These qualities are best fit for enterprise data warehouse work, and should help you gauge whether EDW is a thing for you:

Large enterprises

Typically, corporations with extensive operations, diverse departments, and a high volume of data generated each day can benefit immensely from an EDW. Industries such as finance, retail, healthcare, manufacturing, telecommunications, and technology often fall into this category.

Smaller but data-intensive businesses

Small and medium-sized firms shouldn’t close their eyes from EDW, if they deal with substantial amounts of data and need insights to drive growth strategies. Even small startups, whose offering is based on data-driven models or emerging technologies like AI, IoT, or analytics, can benefit from adopting an EDW early in their growth to establish a solid foundation for handling data.

Regulated industries

Sectors that face strict regulatory compliance, like banking, healthcare, and government institutions, can benefit from an EDW particularly well, because the warehouse and related data processes are a fantastic support for ensuring data integrity, security, and compliance.

Global organizations

Multinational operations often mean complex and fragmented data landscapes. An enterprise data warehouse helps centralize and standardize data from diverse geographical locations, facilitating a unified view for better decision-making.

Benefits of EDW

The implementation of EDW can give some much-needed peace of mind with common data challenges, especially for large companies with complex data structures. I’ve already hinted at the gains to be had, but here’s a more complete list of the benefits of an enterprise data warehouse:

  • Single source of truth that breaks down data silos
  • More efficient data management and governance
  • Consolidation of data sources and reduced complexity
  • Enhanced data quality, accessibility and reliability
  • Improvements to analytics and data-driven decision making
  • New opportunities in advanced analytics and reporting
  • Improved support for scalability, growth, and innovation

The role of EDW in DataOps

A solid enterprise data warehouse is an important pillar enabling the DataOps methodology. 

DataOps is all about streamlining data development and operations without compromising quality or value. The data architecture must facilitate that goal in order to foster the agility, collaboration, and iterative improvements needed to realize the potential of data coming from different parts of the organization.




Steps and best practices for implementing EDW

If all this got you thinking that EDW needs to happen sooner or later, here’s what you can expect to pay attention to in the next phases:

1Current state and objectives

Evaluate your existing data infrastructure, sources, and quality, and reflect those with business needs. Outline goals and expectations for implementing an EDW.

2Data strategy and roadmap

Document a comprehensive data strategy aligned with business objectives. Define a phased plan detailing the steps to move from current systems to the EDW, considering resources, timelines, and milestones.

3Data preparation and cleansing

Analyze and understand existing data to identify inconsistencies, duplicates, and gaps. Clean, normalize, and prepare data for migration, ensuring accuracy and consistency.

4EDW architecture and technology choices

Evaluate and choose a suitable EDW architecture (like cloud-based, on-premises, or hybrid). Select appropriate tools or platforms based on chosen architecture scalability, performance, security, and compatibility.

5Pilot implementation and testing

Start with a small-scale version of the EDW to test functionality, performance, and integration with existing systems. Validate data integrity, query performance, and user acceptance to identify and resolve any issues.

6Data migration and integration

Transfer cleansed data into the EDW following a staged approach. Ensure data consistency, accuracy, and security during and after the migration process.

7Data governance and security

Define data governance policies, roles, and responsibilities for managing data quality, access, and usage within the enterprise data warehouse. Apply robust security measures to protect sensitive data and comply with regulations.

8Training and change management

Support the adoption of the EDW and related processes with comprehensive training to employees. Manage expectations and address concerns by communicating how the system changes how people work.

9Monitor, evaluate, and optimize

When everything’s up and running, continuously monitor performance, data quality, and user feedback against set objectives. Identify areas for improvement and optimize processes, data models, and infrastructure as needed.

That’s quite a few things to consider. The point is, of course, to give the change the attention it deserves. Remember, that you’re building a foundation for your organization’s future ability to extract value from data. Doing sloppy work at this point will get expensive – maybe not tomorrow, but the day will come.


Enterprise data warehouse FAQ

Can’t get enough of EDW talk? Here’s a few more clarifications to help you get started.

What is the difference between EDW and data warehouse (DWH)?

Data warehouse (DWH) can refer to any centralized data repository, whereas an EDW is optimized for broader data integration and analytics. But: many people consider them as pretty much the same thing.

What is the difference between an EDW and a database?

An EDW is a centralized repository that consolidates data from various sources, optimized for analytics and reporting, while a regular database typically stores data for transactional purposes with specific applications or systems.

Can enterprise data warehouses handle big data?

Yes, they are designed for that.

What are some of the most common EDW solution providers?

In no particular order: Snowflake, Amazon Redshift, Google BigQuery, Microsoft Azure Synapse Analytics, Teradata, Oracle Exadata, and IBM Netezza, among others.

How long does it typically take for a company to transition to an EDW?

Transition time varies based on data complexity, organization size, existing infrastructure, and scope, but often takes several months to a year or more for a complete implementation.

How does an EDW integrate with existing business intelligence (BI) tools?

EDWs integrate with various systems such as Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), Business Intelligence (BI) tools, Extract, Transform, Load (ETL) tools, and specialized analytics platforms.

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Make the most of your enterprise data warehouse

Feels like a bit much, doesn’t it? Don’t lose hope now. The fact that you’re here is a strong sign of positive things to come…

This handy guide helps you understand the power of a modern data warehouse →

And if you’re further down the road, I recommend studying the ways of DataOps for enhanced data work efficiency. Fill your cup and dive into this article covering all things DataOps →