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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.
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.
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?
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:
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.