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Generative Marketing

University

CDP


ENTERPRISE DATA WAREHOUSE (EDW)

Researched by
GrowthLoop Editorial Team
verified by
David Joosten


KEY TAKEAWAYS:

 * Enterprise data warehouses (EDWs) give your organization a single,
   centralized repository for gathering and storing customer data.
 * Common EDWs include Google Cloud BigQuery, Snowflake, and Amazon Redshift.
 * An EDW enables higher-quality data management and security, and it helps you
   make better, data-backed decisions more quickly.


TABLE OF CONTENTS

What is an enterprise data warehouse (EDW)?
Types of enterprise data warehouses
Why does my organization need an enterprise data warehouse?
Components of an enterprise data warehouse
How can my organization implement an enterprise data warehouse?
How do I choose the right EDW for my organization?

Enterprise data warehouses are essential tools to help businesses store,
analyze, and act on data organization-wide.


WHAT IS AN ENTERPRISE DATA WAREHOUSE (EDW)?

An enterprise data warehouse (EDW) is a central repository that brings together
company-wide data about customers from various sources. It serves as the core
location for storing data so that those who need it — including sales,
marketing, and customer service teams — can access, analyze, and activate data.

Data warehouses are optimized to store, query, and scale with large amounts of
data. This makes them the best choice for storing data across disciplines,
including marketing, product, finance, and customer success.

Some of the most common enterprise data warehouse vendors include Google
BigQuery, Snowflake, and Amazon Redshift.


WHAT IS A DATA WAREHOUSE VS. ENTERPRISE DATA WAREHOUSE?

While often used interchangeably, the term “enterprise data warehouse” refers to
an organization-wide repository for data, whereas the term “data warehouse” is
more general and can be used to reference smaller data repositories that support
a subset of the company such as a team or department.


DATA WAREHOUSE VS. DATA LAKE

Data warehouses and data lakes have similar but complementary functions. Data
lake architecture accepts both structured and unstructured data types, while
data warehouses can only accept structured data.

Organizations often prefer data warehouses for their repeatable reporting
capabilities, which data lakes don’t have.

Data warehouses and data lakes both require the technical expertise of data
scientists or data analysis developers to transform and process the data. That’s
why many organizations use platforms designed to help marketers access and
activate data from the warehouse, like a composable CDP.


DATA WAREHOUSE VS. DATA MART

A data mart is a subsection of a larger enterprise data warehouse, designed for
a specific team, department, or business function. An EDW is much more extensive
than a data mart, while a data mart contains a much smaller set of data to meet
the needs of a particular group.

A data mart also gives that group more complete control of their data
management. Through data sharing, a data mart can feed into a larger data
warehouse, and the data mart can also process data from the warehouse.


EDW AND THE DATA CLOUD

A data cloud is a serverless data management system that centralizes and stores
a company’s data — in other words, the data isn’t stored on physical equipment
in your company’s offices.  

Historically, organizations relied on on-premise methods of storing data, but in
recent years, they have moved to cloud-based solutions. Most people are familiar
with cloud-based software like Google Docs that store data online instead of on
a physical device.

An EDW can be either on-premise, cloud-based, or hybrid with on-premise and
cloud-based elements. A cloud-based EDW uses a data cloud to store and manage
data.


EDW VS. DMP

An EDW stores large amounts of structured data and makes it available to the
organization. A data management platform (DMP) focuses on the collection and
storage of third-party data, relying heavily on anonymous sources like cookies,
devices, and IP addresses.

DMPs, such as Google Marketing Platform, help marketers take action on
third-party data for advertising efforts, while EDWs store data but don’t assist
teams in activating the data.


TYPES OF ENTERPRISE DATA WAREHOUSES

There are three primary types of data warehouses: on-premises, virtual, and
cloud-based systems.


ON-PREMISES DATA WAREHOUSE (ON-PREM)

An on-premises (often called “on-prem”) data warehouse is a data center using
onsite systems and servers at a physical location. On-premise data warehouses
run on a local network using purpose-built hardware or the company’s existing
data infrastructure, but they can also run on virtual machines. Some of the most
prominent cloud data warehouse providers still offer on-premise or hybrid data
solutions, like Amazon’s AWS Outposts and Teradata Vantage.


CLOUD DATA WAREHOUSE

Rather than running on a local network using hardware, a cloud data warehouse
stores data in the cloud, using a distributed network that the user doesn’t have
to manage. The cloud service provider manages the data infrastructure.

Cloud-based data warehouses offer greater flexibility and scalability for
companies because they don’t have to rely on prohibitive storage limitations.
They can adapt to needs for real-time data access, processing, and analysis.
Some of the most popular cloud data warehouses include Google BigQuery,
Snowflake, and Amazon Redshift.


WHY DOES MY ORGANIZATION NEED AN ENTERPRISE DATA WAREHOUSE?


ENTERPRISE DATA WAREHOUSE BENEFITS

An EDW gives your organization a single, centralized repository for gathering
and storing data and generating real-time customer insights. This central data
location enables higher-quality data management and security, and it helps you
make better, data-backed decisions more quickly — driving revenue in the
process.




MARKETING USE CASES FOR EDWS

Enterprise data warehouses offer marketers a wealth of actionable insights to
help them understand customers, observe their behavior, and improve marketing
across every channel. For instance, using the customer data and analytics from
an EDW, a marketing team can review an individual customer’s journey across
channels in a unified customer profile. From there, they can gauge how effective
their messaging is by reviewing how often a customer converts on a certain page.

The data and analytics from an EDW let marketers create detailed audience
segments. Then, they can place individuals into those segments for targeted and
individualized marketing campaigns. Better campaigns reduce the time to
conversion and customer acquisition costs. Marketers can also improve retention
and reduce churn by presenting the right information at the right time — all
guided by in-depth customer data in the EDW.

An EDW improves collaboration for large marketing teams by breaking down data
silos. Because customer data is all in one location, all marketing teams —
including social, content, and demand generation — can work together instead of
relying on separate channels.

Learn more: Why your team needs a data warehouse


SALES USE CASES FOR EDWS

Customer data from an EDW sets up sales teams for success by showing them
accounts that are primed for outreach and ready to buy. When reps are doing
outbound sales, data offers a full picture of who an individual is and their
role in the decision-making process. Sales teams achieve better lead scoring,
and the data signals who is ready for outreach versus who is a poor fit for
their product.

Before a discovery call or demo — when an account indicates they’re interested
in learning more — the sales rep can review in-depth data on a potential
customer to learn more about them and their needs based on past behavior. Reps
can go into the sales call prepared to position their product or service to
match the prospect’s needs.


CUSTOMER SERVICE USE CASES FOR EDWS

An EDW allows customer service teams to offer customers a better experience from
start to finish.

For instance, when a customer reaches out via chat, email, or phone with a
concern or challenge, an EDW offers a unified data view for the support rep.
Support reps can quickly review a customer’s health score to prioritize tickets
from at-risk customers to reduce churn. From there, support teams can use
customer data to understand who the customer is, their needs, and whether
they’ve had this issue before. Then, they can efficiently solve their problem
and send the customer on their way.

Outside of individual customers, customer service teams and leaders can review
EDW data to track big-picture trends and challenges. If a large number of
customers encounter the same problem or have a poor support experience under
certain conditions, the team may be able to partner with the product team or
conduct training to improve customer experiences and satisfaction overall. Data
analysis can quickly reveal these issues for service teams.


COMPONENTS OF AN ENTERPRISE DATA WAREHOUSE

An enterprise data warehouse has several key components:

 * Data sources. These include all of the sources that send data to the EDW,
   including enterprise resource planning (ERP) systems (such as Oracle),
   customer relationship management platforms (such as Salesforce), marketing
   channels like social media and websites, SQL databases (such as MySQL),
   product data, and others.

 * Staging area. In the staging area, data is transformed before being loaded
   into the EDW. The data is aggregated and cleaned, ensuring it’s in the
   correct format — for most warehouses, this is a tabular format (or in rows
   and columns) so that SQL can query the data. Then, it’s ready for storage and
   analysis.

 * Storage layer. From the staging area, data is loaded into the storage layer.
   The storage layer includes a metadata module that tells administrators
   details about the data, such as its source. The EDW itself and any smaller
   data marts each receive metadata within the storage layer.

 * Integrations and APIs. The EDW exchanges data with other tools and software,
   acting as both sender and receiver. In some cases, a team may have other
   software elements (such as a composable CDP) that overlaps this function and
   also acts as the data sender and receiver. Integrations and APIs are
   important in facilitating data processing within the EDW.

 * Presentation space. For teams to be able to access, analyze, and activate the
   data, they need a unified interface or dashboard. Also known as the access
   space, the presentation space lets business users run reports and analytics,
   enables data sharing, provides a dashboard for data visualization, and offers
   in-depth insights that teams need to make decisions.


ENTERPRISE DATA WAREHOUSE ARCHITECTURE

There are three types of data warehouse architecture, each referred to by the
number of tiers involved. You should choose the structure that best meets your
organization’s needs so that you can maximize the value gained from your data.

 * One-tier architecture. This structure has a data source layer, warehouse
   layer, and presentation layer. Although it can improve data quality, it
   limits the amount of data an organization can store.

 * Two-tier architecture. This structure includes the layers of one-tier
   architecture, but it adds a staging area between the source layer and data
   warehouse to clean data and ensure it’s in the correct format. Two-tier
   structure is helpful for some organizations that use data marts but is not
   preferable for most organizations because it cannot scale to support a large
   number of users.

 * Three-tier architecture. Most organizations use this structure, which
   includes both a staging area and data marts. Three-tier architecture offers
   scalability and faster development than other structures. Its three tiers are
   as follows: one to receive the data that’s been cleansed in the staging area,
   one that readies the data for analysis, and one where users interact with the
   data and gather insights.


HOW CAN MY ORGANIZATION IMPLEMENT AN ENTERPRISE DATA WAREHOUSE?


BEST PRACTICES

Organizations should consider these best practices as they implement a data
warehousing solution:

 * Determine your business requirements before implementation. The solution you
   choose should be based on the use cases and priorities of the teams that will
   use the EDW. Consider the volume of organization-wide data, existing
   ecosystems used (such as Amazon Web Services), and use of artificial
   intelligence.

 * Identify all current and potential marketing data sources. These include
   advertising platforms like Google and Facebook Ads, social media channels,
   Google Analytics 4, Amazon’s Delivery Service Partner (DSP) Program, and
   others. The sources feed your data warehouse and create the single source of
   truth used for marketing insights.

 * Involve all stakeholders in the implementation process for the EDW.
   Stakeholders include everyone who will activate the data (such as marketing,
   sales, and customer service teams), the data teams who will implement the
   warehouse (including data engineers, data scientists, and security teams),
   and leaders and decision-makers who must sign off on an EDW.

 * Use a data activation platform to take action on data directly from the EDW.
   Marketers often struggle to create cross-channel campaigns because of siloed
   data or access challenges. But tools designed for data transfer help
   marketers build audiences directly from their EDW and drive direct value
   through self-service access.

 * Create a data feedback loop. When marketers effectively use an enterprise
   data management solution, they use data to create and A/B test campaigns,
   then send the resulting data back to the EDW. This back and forth continues
   to fuel insights about campaign performance for better results.


PITFALLS TO AVOID

When implementing an enterprise data warehouse, marketers should avoid these
mistakes:

 * Failing to validate and profile data before loading it into the EDW.
 * Focusing on single channels after implementation instead of using the EDW to
   integrate all channels to create more effective cross-channel campaigns.
 * Relying on analytics teams to query data and export data, instead of using
   tools designed for self-serve access.
 * Assuming what customers want, rather than relying on data from the EDW and
   experimentation results.
 * Not using a data activation platform with the EDW.
 * Sticking with outdated manual workflows, even after EDW implementation,
   rather than building automations to make data-driven decisions more quickly.




HOW DO I CHOOSE THE RIGHT EDW FOR MY ORGANIZATION?

Use these three steps to guide your process for choosing an enterprise data
warehouse.


STEP 1: RESEARCH THE TOP ENTERPRISE DATA WAREHOUSE SOLUTIONS

Most organizations are moving toward cloud-based enterprise data warehouses.
Three of the most prominent options are:

 * Snowflake
 * Google Cloud BigQuery
 * Amazon Redshift


STEP 2: READ EDW REVIEWS

Visit review sites like G2 and Gartner Peer Insights to read the experiences and
opinions of other organizations who have used the solutions you are considering.
Most reviews provide examples of pros and cons for each EDW, so you can learn
about what challenges you might face and how a particular solution might meet
your organization’s needs.

Look for reviews from organizations in a similar industry, of a similar size, or
with a similar audience to yours to best understand an enterprise data
warehouse’s ability to meet your needs. You might seek out the reviews of your
competitors or partners, if available, to ensure their experiences are relevant
to your organization.

When reading reviews, remember your organization’s and team’s top priorities for
a data warehouse, and look for feedback related to those topics that can inform
your opinion of each solution.

In addition to reading reviews, consider asking your network or those you know
in similar organizations for their recommendations and experiences with their
EDW. They may offer more in-depth information than a written review could.


STEP 3: WORK CLOSELY WITH THE DATA LEADER AT YOUR ORGANIZATION

As marketers, you will be activating data from the EDW and using it to drive
value and target campaigns to your customers, but your data team will still need
to assist with the management of the EDW, during implementation and on an
ongoing basis.

When choosing an EDW, collaborate closely with data leaders who can provide
insights on existing organizational data, the right model for your company, and
the capabilities your solution needs to have. If you work with the data team
early on, you will establish a shared understanding and partnership between
marketing and data management that leads to better results.



Published On:
November 10, 2023
Updated On:
April 29, 2024
Read Time:
9 min
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