The Potential of EDW: Your Ultimate Enterprise Data Warehouse Guide

What are EDWs and how do they drive better outcomes for customers? An Enterprise Data Warehouse (EDW) is a solution that brings together all of the different stores and sources of data from across the business into one, central location for better decisions and outcomes across the business.

In this guide, we’ll break down what EDWs are and how they centralize and analyze data to help business teams make better decisions and stay competitive in a data-centric landscape.

Key Takeaways

• An EDW centralizes business data, helping teams make informed, data-driven decision-making through comprehensive reporting and intelligent data correlation
• EDWs consist of layers like ETL/ELT processes, and data mart layers that manage and analyze data, providing multi-dimensional analysis and ensuring that data is ready to explore for insights
• Enterprises leverage EDWs for better customer insights and CRM, supporting advanced analytics and machine learning, and to gain a competitive edge with comprehensive analyses and fast decision-making

Decoding the Enterprise Data Warehouse (EDW)

An Enterprise Data Warehouse (EDW) isn’t just any data repository; it’s a relational powerhouse designed to tackle the immense analytical demands of modern businesses. With its robust architecture, an EDW gives businesses tons of benefits. An EDW:

• Centralizes a company’s treasure trove of business data
• Enables comprehensive reporting and intelligent data correlation across multiple domains
• Fosters an environment ripe for effective business intelligence strategies

The EDW is all about better, data-driven decision-making. By consolidating vast and varied data sets, EDWs act as the backbone for businesses to make informed decisions – rooted in accuracy, with a holistic view of the enterprise’s data landscape.

The Anatomy of an EDW: Core Components

Top data warehouses like Snowflake, Databricks, Teradata VantageCloud, Google BigQuery Microsoft Azure and Amazon Redshift are changing the way businesses connect with their audience through data. At the heart of every enterprise data warehouse is a meticulously structured architecture, designed to manage and analyze data with precision. This architecture includes critical layers such as the ETL or ELT processes, which orchestrate the extraction, transformation, and loading of data, ensuring that every bit of information is primed for insightful exploration.

Data Sources and Integration: How an EDW Works

The data journey in an EDW starts at the staging area, where data from all of the different sources in a business are brought together, setting the stage for seamless integration and processing. Whether it’s a cloud data warehouse merging real-time data streams or a traditional system harmonizing batch-uploaded information, the goal remains the same: to ensure data consistency and prepare it for future analytics.

In the era of big data, the ability to integrate data from multiple databases, operational systems, and even semi-structured sources like social media is a game-changer. It’s not just about bringing different data types together; it’s about creating a unified, centralized data management system that paves the way for comprehensive reports and deep customer insights that weren’t possible before.

Managing and Storing Data

Within the walls of an EDW, data is not just stored; it’s strategically managed and standardized to ensure peak efficiency. Take cloud data warehouses, for instance, which have mastered the art of data storage. This meticulous approach to data stored is a cornerstone of any EDW, ensuring that the volumes of structured data are always ready for action.

But it’s not just about storage; it’s about reliability too. EDW providers have made it their mission to store data across data centers, ensuring that no matter what, your data remains accessible. This level of data management is what empowers businesses to extract insights with confidence, knowing that their data is both secure and primed for discovery.

Transforming Raw Data into Actionable Insights

The power of an EDW is manifested through its ETL/ELT processes, which turn raw data into a gold mine of actionable insights. In these processes, not only is data quality managed, but the flexibility to handle unstructured data is also enhanced, opening doors to advanced business intelligence capabilities.
Imagine real-time data processing where every change in the database is captured and replicated, ensuring that decision-makers have access to up-to-the-minute historical records. This dynamic approach to managing data enables companies to harness analytical tools and AI algorithms, turning them into strategic assets that drive forward-thinking decisions.

When transforming data from your data warehouse or data lake into actionable insights, the best tool to use is the Composable Customer Data Platform (CDP). There’s a reason data warehouses and data lakes such as Snowflake, Databricks, Google BigQuery, AWS Redshift and Teradata VantageCloud are making waves: They’re helping organizations embrace the power of composable technology stacks and the Composable CDP.

A Composable CDP brings easy audiencing and activation tools that businesses can use across the customer lifecycle by tapping directly into the data warehouse, with no copies required.

Composable architecture allows IT and data teams to centralize customer data and create their own sources of truth — eliminating the need for bundled or prepackaged CDPs in the process. With the cloud data lakes they’ve built, IT teams can develop their own customer 360 technology and truly tailor their customer data stacks to their unique requirements.

Architectural Styles of Enterprise Data Warehouses

As businesses evolve, so do enterprise data warehouses. From the straightforward one-tier enterprise data warehouse architecture to sophisticated three-tier models, on premises data warehouses, and on to the modern cloud-based solutions, EDWs have continuously adapted to meet the growing demands for scalability and real-time analytics.

Each style brings its own set of pros and cons, whether it’s the capacity for massive parallel processing in cloud data warehouses or the ability to access a globally unified dataset that keeps companies agile and informed.

On-Premises vs. Cloud-Based Solutions

When the cloud beckons with its promises of cost-efficiency and scalability, businesses listen. Cloud-based data warehouses offer the following benefits:

• Eliminate the need for costly hardware
• Offer flexible, pay-as-you-go pricing models
• Adapt effortlessly to your business needs
• Charge only for the resources you actually use

This is a stark contrast to the fixed costs associated with on-premises solutions.

However, it’s not just about cost. Cloud data warehouses also bring:

• Unparalleled flexibility and ease of management
• Support for complex analytic capabilities that can propel a business forward
• Stringent security measures like encryption and multi-factor authentication

It’s easy to see why many organizations are migrating to the cloud.

Strategic Applications of EDWs in Business

The strategic prowess of an enterprise data warehouse is evident in its ability to store and organize large volumes of historical data, making it accessible for analysis over extended time periods. This capability is critical for businesses seeking to enhance operations and customer understanding. By tapping into the rich insights provided by an EDW, companies can refine their approaches to market demands, customer service, and overall business efficiency.

Supporting Advanced Analytics and Machine Learning

An EDW does more than just collect and store data—it sets the stage for advanced analytics and machine learning to thrive. The quality and structure of data housed within an EDW are pivotal for training machine learning algorithms, enabling them to uncover patterns and automate decisions that once required manual intervention. This integration of AI within the data warehouse ecosystem streamlines predictive analytics and pattern recognition, making informed decision-making an intrinsic part of the business process.

The scalability of cloud data warehouses further empowers businesses to expand their analytical horizons. As data volumes grow and analytical demands intensify, these scalable solutions ensure that businesses can keep pace without the burden of physical hardware limitations.

How to Choose an EDW Provider

Choosing the right EDW provider is a critical decision that can shape a business’s data strategy for years to come. It requires a careful assessment of the provider’s:

• Reliability
• Scalability
• Storage formats
• Data security measures

Major players like Amazon Web Services, Google Cloud Platform, Teradata VantageCloud, and Snowflake each offer unique capabilities and should be evaluated based on your specific business needs, such as real-time analytics and cybersecurity.

Evaluating Cloud Providers and Managed Services

When evaluating cloud providers, there’s a plethora of factors to consider. Some popular options include:

• Google BigQuery: known for its serverless execution and multi-cloud capabilities, offering a high degree of performance and scalability.
• Snowflake: known for its serverless approach and architecture that separates storage from compute resources, making it an attractive option for handling large-scale data processing.
• Amazon Redshift: known for its ability to scale up to petabytes and serve thousands of users simultaneously, all starting at an accessible price point.
• Teradata VantageCloud: a connected multi-cloud data platform company with enterprise analytics that solve business challenges from start to scale.
• Databricks: a unified, open analytics platform for building, deploying, sharing, and maintaining enterprise-grade data, analytics, and AI solutions at scale, with flexibility to handle massive and mixed data workloads.

Automation tools for cloud data warehouses are revolutionizing deployment speeds and operational reliability, making them an integral part of data warehousing solutions. The cherry on top is the integration with a variety of data integration tools and APIs, which is indispensable for augmenting an EDW’s functionality and ensuring seamless data management.

Maintaining and Securing Your EDW

Maintaining an enterprise data warehouse goes hand in hand with securing it. It’s essential to:

• Establish robust permissions and access controls from the outset
• Partner with a cloud data warehouse provider that handles data encryption, key management, and access control with the utmost rigor
• Adopt strategies like Zero-Trust Architecture to significantly strengthen EDW security parameters, ensuring that every interaction within the network is authenticated and validated

Centralizing data in an EDW not only simplifies management but also plays a crucial role in maintaining data integrity and compliance with regulations like GDPR. When evaluating cloud data warehouses, it’s important to consider their native support for data types, the required technical expertise, and comprehensive security features that a provider offers.

Leveraging EDWs for Competitive Advantage

Using an enterprise data warehouse as a competitive advantage is like having a chess grandmaster at your side. By providing a vast array of data and integrating it into business intelligence and data visualization tools, the data warehouse enables businesses to perform comprehensive analyses for well-informed decision-making. The agility and timeliness of these decisions can be the critical difference between leading the market and falling behind.

The secret to unlocking this potential lies in the alignment of business objectives with the functionalities of the EDW. A clear understanding of what your business aims to achieve, combined with an EDW’s capabilities, can lead to strategic insights that trickle down throughout the organization. It’s this strategic alignment that allows business users to make the best use of their data in ways that enable innovation, efficiency, and ultimately, success.

Activate Your Data Warehouse with a Composable CDP

Creating a comprehensive view of the customer is key to delivering better customer experiences, but IT teams need a way to extend the hard work of centralizing their data warehouse so business teams can leverage the benefits of data centralization for audience segmentation, journey orchestration and real-time experiences.

Reverse ETL is key, but not just any application that supports reverse ETL will do. IT professionals want to focus on building and maintaining data systems, not fielding ad hoc requests. But too many applications built on top of the data warehouse require IT professionals to write endless SQL queries on behalf of business users.

To build a customer data stack that serves both the needs of technical and non-technical users, IT professionals must seek out solutions that give business teams a user-friendly interface they can use to generate queries in the data warehouse, have those queries automatically translated into SQL, and push them down to the data lake to be executed.

Results can then be processed and served back up to the application, allowing IT teams to decide where data is stored and queried while making sure business users can self-serve their needs — with role-based access to data.

IT professionals can now build their customer data platform right on the data warehouse — no strings attached.

Composable CDPs are the future. But to guarantee success, IT teams must choose tools they can plug and play into their existing martech stack to maximize power, control and performance.

Optimizing EDW for Real-World Scenarios With a Composable CDP

In the real world, EDWs are not just theoretical constructs; they are vital tools that drive business operations forward in the customer experience. When paired with a Composable CDP, brands can bring powerful business applications straight to the data warehouse, to activate the best customer experiences, with the full breadth of their data.

Two Customers Who Activated the Data Warehouse With a Composable CDP

Northwestern Mutual Goes Zero-Copy With a Composable Customer Data Platform

The team at Northwestern Mutual knew something was wrong when they were seeing a proliferation of fragmented data with their on-prem database, with about 200 data labs scattered throughout the organization. They wanted to achieve proper data lineage with all of the classifications of data, and all the security measures — they knew it would be easier to do that and cut down copies by putting it all in one place.

They decided to adopt the data lake and move to Databricks as their central store of data for a centralized data lakehouse with an activation engine — enter ActionIQ and Databricks.

They had three key priorities when they were redesigning their infrastructure, and found that the combination of Databricks and ActionIQ would meet those criteria in the following ways:

• Simplify Data Footprint and Reduce Handoffs
• Ensure Governance and Compliance
• Streamline Operations and Speed-to-Market

Now, marketing teams enjoy the simplicity of activating their campaigns without waiting months for a request, and data engineers enjoy the simplicity of having their data centralized in the data lake — without having to deal with backlogs of requests or data copies. You can catch the full story on how Northwestern Mutual went composable here.

Saks Prepares Their Customer Experience for the Future With a Composable Customer Data Platform

For leading luxury brand Saks, data is incredibly powerful. It empowers team members across the organization to make decisions that are always smarter and better, through both success and failure. But in order to unlock that data-driven strategy, they needed to activate their data warehouse with a CDP to democratize and activate that data-driven decisioning and action — from the analytics team all the way to the marketers and beyond.

Saks’s data is all in Snowflake, where it’s managed by Nivy Swaminathan, Vice President of Advanced Analytics and Personalization and her team. They build data science models, predicting outcomes at a user level, and all of that rich data gets written back into their Snowflake data system. ActionIQ mirrors that work to allow marketers access and activation of all of those insights-driven audiences.

The return from having a CDP for Saks manifested in time savings, agility to rapidly test, learn and optimize on data-driven insights and audiences, and self-service for their marketing teams. You can read the full story of how Saks went composable here.

Summary

It’s clear that enterprise data warehouses are more than just a data storage solution—they are a catalyst for transformation across the business. From offering insights into customer behavior to supporting advanced analytics and machine learning, EDWs provide a foundation for intelligent decision-making and a competitive advantage in the digital age. As technology advances, so too will the capabilities of EDWs, ensuring that businesses are well-equipped to handle the data demands of the future.

To learn more about how a modern data stack with a data warehouse and Composable CDP can be a game changer for your business, reach out to our team.

Frequently Asked Questions

What is EDW SQL?

EDW SQL stands for Enterprise Data Warehouse SQL, which is a cloud-based platform leveraging Massively Parallel Processing to quickly run complex queries across large amounts of data, making it a key component of big data solutions.

What is the difference between an enterprise data warehouse and a standard data warehouse?

The main difference between an enterprise data warehouse and a standard data warehouse is that the former is designed to be a centralized repository for an organization’s entire range of data, while the latter typically serves specific departments or business units with structured data for querying tools and end users.

How do cloud data warehouses compare to on-premises data warehouses in terms of cost and scalability?

Cloud data warehouses are more cost-efficient and scalable compared to on-premises data warehouses. You can save on hardware and operational costs with pay-as-you-go pricing and on-demand scalability.

Can enterprise data warehouses be used for advanced analytics and machine learning?

Yes, enterprise data warehouses can be used for advanced analytics and machine learning by providing a centralized platform for complex data processing, and the structured data within EDWs is crucial for training and refining machine learning algorithms.

What security measures should be considered when maintaining an enterprise data warehouse?

When maintaining an enterprise data warehouse, consider defining permissions, access controls, and implementing rigorous data encryption to enhance security. Adopting a Zero-Trust Architecture can also bolster security measures.