Every organization has data. From financial data to customer information to operational data, every company has a wealth of information at its fingertips to store, organize, reference, and analyze for decision-making purposes. Unfortunately for many organizations, this data exists in department or business unit silos where only a few people can access it. This can lead to several business challenges:

  • Reporting is inconsistent, or the data doesn’t match up across departments
  • A high-level view of data across the organization is unavailable
  • Obstacles that one could address through interdepartmental analysis go unrecognized
  • Data quality and data integrity are at risk
  • Data analysis across the company becomes laborious and unreliable

To overcome these challenges, many organizations build an enterprise data warehouse. This article will review the definition of an enterprise data warehouse (EDW), its benefits, and the decisions organizations need to make before they start an EDW implementation.

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Table of Contents

  1. What is an Enterprise Data Warehouse?
  2. Why Do Companies Invest in an Enterprise Data Warehouse?
  3. EDW in the Cloud or On-premises?
  4. Choosing an EDW Vendor
  5. Final Thoughts from Xplenty

What is an Enterprise Data Warehouse?

An enterprise data warehouse is a virtual corporate repository that unifies and stores business data. This data exists across an organization, with a high level of availability. An EDW ensures the right people can access the information they need to make decisions that move the business forward.

The primary goal of building an EDW is to provide standardized and consistent access to data sets. Those sets exist in a variety of data stores and are necessary for planning and analysis purposes. An EDW can be on-premises or cloud-based. It's governed by security and data privacy protocols that protect the validity and integrity of the information it houses.

An enterprise data warehouse can store both structured and unstructured data sets. These sets originate from many potential data sources, including:

  • Enterprise Resource Planning (ERP) systems
  • Customer Relationship Management (CRM) applications
  • Social networks
  • Internet of Things (IoT) devices
  • Supply chain management systems
  • Human resources and payroll
  • Metadata

An enterprise data warehouse can be any size with any level of technical complexity, depending on the needs of the business. A successfully executed EDW centralizes and stores many types of data, transforms it, and makes it available for viewing by the end-users. That visibility can be as reports or outputs from the business intelligence (BI) tool of choice.

Why Do Companies Invest in an Enterprise Data Warehouse?

The benefits of an enterprise data warehouse are easy to outline. According to Datamation, the data warehouse market will grow to about $34 billion over the next four years. The following are just a few of the reasons why companies are making the investment.

Consistency in reporting: Data consistency is essential to any business, especially for business users and executives making critical decisions that will determine the future of the company. An EDW addresses this requirement by consolidating and standardizing historical data from source systems across the enterprise.

Greater efficiency: With an enterprise data warehouse, teams can pull raw, consolidated data from one source. This data is ready for real-time analysis. There's no need to manually gather it from multiple data sources. A process that once took days or even weeks to complete is now executed in minutes.

Scalability with business growth: Any technological undertaking needs to flex and scale with the growth and change of the business. While many companies might start with siloed data marts and poor data management practices, they will eventually find that an EDW is a necessary step to enabling their own maturation. 

Innovation and technological enablement: The functionality of today’s business intelligence tools is becoming more sophisticated by the minute. Machine learning, artificial intelligence, and data modeling capabilities are available to sophisticated organizations focused on data transformation. As the technologies get more sophisticated, the importance of having standardized, accurate underlying high-performance data to feed them will increase.

Recommended reading: Top 17 Business Intelligence Tools Of 2021.

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EDW in the Cloud or On-premises?

One question companies need to answer before starting down this path is whether they want to build their EDW on-premises or in the cloud. These days, the answer is typically pretty clear: most modern organizations are starting cloud-native so they can take advantage of the flexibility, scalability, and cost savings of the cloud from day one. However, that doesn’t mean the cloud is the answer for everyone. 

Here are some questions that will help organizations determine which option to go with:

  • What is the organization’s budget? While there is some debate about how the costs balance out over the long term, an on-premises EDW will have higher upfront costs while the cloud will spread those costs out over time.
  • What data restrictions or regulations is the company under? An on-premises data warehouse yields control over data flow and every data integration point, making it easier to stay in compliance with any relevant outside entities and authorities. 
  • Where will the company be in ten years? In 2020, Gartner estimated that 30 percent of data warehousing workloads were cloud-run, and that this would grow to two-thirds by 2024. If the long-term plan is to take advantage of the cloud, it likely won’t be worth the expense to invest in an on-premises EDW.
  • How much existing infrastructure is on-premises? If much of a company’s current infrastructure and relational database is already on-premises, it makes sense to implement an on-premises enterprise data warehouse. Keep in mind, however, that hybrid environments are becoming more valuable. So deploying an EDW in the cloud could help with a long-term overall cloud transition.

The decision about whether to invest in a cloud or on-premises enterprise data warehouse really boils down to an organization’s long-term goals and business requirements. By answering these questions and thinking about the needs of business users, most companies can get a clear view of which pathway is the best for them.

Choosing an EDW Vendor

Selecting the right platform for an enterprise data warehouse is an important decision. The overall goal is to find a vendor that provides a reliable platform that can conform to the needs and requirements of the business—with security, integrations, and data flow topping the list of priorities for most companies.

Here’s a shortlist of the top six EDW vendors with links for more information:

  • Snowflake: An architecture and technology that enables today’s data-driven organizations.
  • Microsoft Azure Synapse: Brings together data integration, enterprise data warehousing, and big data analytics.
  • Google BigQuery: Serverless, highly scalable, and cost-effective multi-cloud data warehouse designed for business agility.
  • Amazon Redshift: Query and combine exabytes of structured and semi-structured data across a data warehouse, operational database, and data lake. 
  • Oracle Autonomous Data Warehouse: Simplified data warehouse management—with autonomous administration.
  • Teradata Vantage: The connected multi-cloud data platform for enterprise analytics.

Recommended reading: Snowflake vs BigQuery.

Final Thoughts from Xplenty

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An investment in an enterprise data warehouse is an investment in a company’s future. Without an EDW solution to organize, standardize, and dispense an organization's data, companies leave themselves vulnerable to inaccurate reporting and poor decision-making. With the promises of greater efficiency, data consistency, and scalability, an EDW is a smart decision for growing enterprises across the globe.

As organizations mature, they also need to supplement investments in an enterprise data warehouse with data pipelines and advanced ETL tools like Xplenty. Xplenty is a cloud-based ETL (Extract, Transform, Load) solution providing simple visualized data pipelines for automated data flows across a wide range of sources and destinations. The company's powerful on-platform transformation tools allow its customers to transform, normalize, and clean their data while also adhering to compliance best practices. For more information, please contact us for a 14-day pilot that could positively transform your EDW data flows.