Enterprise Data Warehouse

While we understand that poor data quality results in errors that damage brand reputation and customer relationships, ultimately affecting sales and profitability – what’s to be done to reduce poor data quality? 

Enterprise data warehouses (EDW) have become an essential part of any business intelligence operation since its debut 30 years ago. As a result, having an enterprise data warehouse can increase the overall success of your business.

In this article, you will get to know what is an enterprise data warehouse, the difference between an enterprise data warehouse and a data mart. 

You’ll get to know why your business needs an enterprise data warehouse, some enterprise data warehouse architecture, and the advantages and challenges of an enterprise data warehouse. Finally, the types of enterprise data warehouses and how you can choose the best for your company. 

What Is An Enterprise Data Warehouse (EDW)? 

An enterprise data warehouse (EDW) is a relational data warehouse that holds the business data of an organization, including details on its clients. The data often originates from a variety of systems, including flat files, CRMs, ERPs, and physical records. Data analytics, which might provide useful information, are made possible by an EDW. Like other data warehouses, EDWs gather and combine information from many sources, functioning as a data center for the majority of corporate data to enable easy access and analysis.

The warehouse makes the data accessible to all authorized users and provides assistance in the form of in-depth analysis and comprehensive, accessible reports. These warehouses may differ from one organization to another, but they often have a few fundamental features in common.

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What Is The Difference Between An Enterprise Data Warehouse And A Data Mart?

The two databases' approaches and sizes are where they most significantly differ. A data mart maintains a limited quantity of data pertaining to a particular business department or project, as opposed to a data warehouse, which acts as the company's central database and houses data about every area of the organization. Similar to a data mart, a data warehouse gathers data from a number of sources, whereas a data warehouse is often where a data mart gathers data.

As a result, a data warehouse has a more sophisticated and complex architecture compared to  a data mart and also has far bigger storage capacity. A data mart's implementation process may be completed in a short period of time since it gathers considerably less data and has a simpler structure than a data warehouse, which often takes several months or even years to complete.

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Why Your Business Needs An Enterprise Data Warehouse

Reliable, accurate data is crucial for organizations to make informed decisions about their services, personnel, clientele, and other factors. In the absence of high-quality data, company executives must rely on their intuition to make these crucial judgments.

With the help of EDWs, corporate managers can go beyond intuition by integrating data from a variety of unstructured sources into tools for business intelligence and data visualization, such as Tableau, PowerBI, and Qlik. When teams have pressing concerns, these tools give them swift, data-driven responses.

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Enterprise Data Warehouse Architecture

The way data is organized across several databases is specified by a typical data warehouse architecture. The most efficient method for extracting information from raw data is determined by a contemporary data warehouse structure since the data must be sorted and cleansed to be useful. The raw data in the staging area is retrieved and transformed into a simple consumable warehouse structure using a dimensional model to give beneficial business analytics.

Data Source 

These are the various systems that generate or store data that is used in the enterprise data warehouse. They can include flat files, relational SQL databases, IoT systems, straightforward spreadsheets, and more.

Staging Area

Prior to an enterprise data warehouse, data is changed in the staging area. Here, information will be cleaned up, separated, linked, and de-duplicated in order to meet a certain warehouse data model. Tools for managing data quality may also be present in the staging area.

Data Storage

 A database management system (DBMS) is commonly used by the enterprise data warehouse to store data. The database management system (DBMS) may be an older, relational database or a more modern one. The storage area is loaded with the data. Here, some changes may still be necessary using the ELT method. However, all general adjustments will be made at that point, allowing the data to be incorporated into the final model (s).

Analytics And BI 

A key component of an enterprise data warehouse design is analytics and business intelligence (BI), which enables firms to extract insights and valuable business intelligence from the data stored in the warehouse. Data are queried through various reporting, mining, and data visualization technologies. OLAP, data mining, reporting, and visualization technologies may all be used to query data stored in the business data warehouse.

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What Are The Advantages Of Enterprise Data Warehouse?

Having access to a strong enterprise data warehouse offers several important advantages. These benefits range from those that are just basic housekeeping issues to those that are crucial to the overall performance of the business. Here are some of the advantages of an enterprise data warehouse:

Data Storage

This includes a subject-oriented data repository, time-variant (data from the historical point of view) data repository, nonvolatile (read-only) data repository, granular data storage, metadata storage, and storage in multiple environments (cloud, on-premises, hybrid).

Database Performance

This includes scalability, automated DWH maintenance tasks, backups, replication, and patching. Advanced data searching (materialized view support, data indexes, result-caching, etc.)

Data Management And Integration

Access to data integration with ETL/ELT, full and incremental data extraction/load, structured, semi-structured, and unstructured data ingestion. Big data ingestion, streaming data ingestion, and data loading and querying using SQL.

Compliance And Security

This involves data encryption and securing data access with user authentication and authorization. Granular access control (row- and column-level). Compliance with national, regional, and industry-specific regulations (for example, GDPR, HIPAA, PCI DSS).

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Challenges Of Enterprise Data Warehouse

A quick look at some of the challenges of enterprise data warehousing.

Systems Optimization and Data Structuring

Data must be structured in a way that makes sense for your upcoming activities in order to be properly processed. Data structure gets more challenging as you add more data to your warehouse, which can dramatically slow down the process. 

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Additionally, the system manager would find it challenging to qualify the data for analytics. Tools for data analysis must be properly designed and set up in order to optimize systems. Better outcomes will ensue, simplifying development decisions.

Balancing Resources

Most firms decide to provide different departments access to the system in order to maximize the benefits of data warehouse implementation. This might make the warehouse more stressed and reduce productivity. You may balance the performance and usability of warehouse systems by putting access control and security measures in place.

Information Data-Driven Analysis

Taking the necessary time to comprehend and record your company's demands is one of the most crucial components of successful data analysis. You should thoroughly map essential ideas throughout the early phases of implementation since data warehousing is driven by the information you offer. 

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The more effective the original business information model, the quicker and less expensive your implementation process will be, claims Information Quality Solutions.

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What Are The Types Of Enterprise Data Warehouse?

Through important tools and solutions, the data with EDW software is processed, changed, and ingested in an accessible manner. A data warehouse's primary function is to combine information from numerous sources in a comprehensive manner utilizing several deployment scenarios.

Traditional/ On-premises Data Warehouse 

On-premises data warehouses are often utilized within the corporate firewall. Teradata, Netezza, and Exadata are a few examples. Full control is offered by on-premises data warehouses, but that control entails higher responsibility. A traditional data warehouse involves the full tech stack and requires the management of database administrators, system administrators, and network engineers.

Cloud Data Warehouse 

Businesses can satisfy their data warehousing needs by adopting a cloud data warehouse service that a vendor offers. To achieve this, you pay a provider who has their own infrastructure of hardware and software to rent cloud services from them so you can use them to access an online data warehouse. Due to the inherent flexibility of cloud data warehouses, upscaling and downscaling are both feasible with no adverse outcomes on the operation of enterprise data warehouses.

Virtual Data Warehouse 

Data virtualization is a third choice that some businesses use. The data is kept in the original systems in this scenario, and a virtual layer is built for reporting and data analytics. This may seem to be a quicker and easier way to get going. Data virtualization, however, presents significant performance concerns at scale and is dependent on source systems for data querying.

Hybrid Data Warehouse 

For those looking to take advantage of both on-premises and cloud deployment, hybrid EDW implementation is quickly becoming the trend. In this scenario, cloud scalability is combined with the data governance and effectiveness of on-premises EDWs. The ability to increase capabilities in response to changing demands makes a hybrid enterprise data warehouse design more flexible.

Choosing The Right Enterprise Data Warehouse For Your Company 

The organization goes through the crucial discovery phase before beginning to create the enterprise data warehouse. This is a time when you should research the finest strategies and tactics for your company. Here are a few important criteria to consider while picking an enterprise data warehouse.

• Business Needs: Cloud data warehouses are made for various sectors of the economy and corporate divisions. To use your data warehouse service effectively, you must carefully consider the use, purpose, and needs of your business.

• Performance: How soon do you actually need the data? The price rises in direct proportion to performance level. In order to choose a solution that works for you, you need carefully evaluate the speed of the process and the amount of data you want to process. This does not imply that you should accept subpar performance.

• Implementation: When looking at data warehouse installation, there are several factors to take into account. Depending on your objectives, the cost is important, but time may be more crucial. If one data warehouse is somewhat more economical but takes four to five months longer to install, that means four to five months of being less competitive because of a lack of business information. The simplicity of installation should also be considered. Is a consultant necessary for the implementation of your data warehouse?

• Security: Choose a data warehouse that can provide the level of protection that your company requires. The majority of data warehouse companies fix vulnerabilities in their security systems and maintain them up to date.

• Scalability: Consider how much data you presently have access to and how much scale your warehouse will need to accommodate. Large volumes of data can be kept in cloud-based data warehouses without a lot of overhead. You want to be able to plan using your prior data warehouse usage, your business intelligence (BI) strategy, and the expansion of your firm.

• Cost: This is undoubtedly one of the most crucial factors to take into account when selecting your data warehouse. Whether you select a cloud or on-premise solution will affect the price. While vendor price lists are an excellent place to start, you should obtain a quotation with your precise setup because some suppliers provide a pay-as-you-go approach while others offer flat rate pricing, where you may pay per TB or per hour of use.

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Final Thoughts

There you have it! Given how frequently the data environment changes, maintaining and upgrading your business data warehouse also demands ongoing effort. However, having a single, trustworthy source of data has several advantages that will make this effort worthwhile for your company. Overall, an enterprise data warehouse can be a powerful tool for driving your business success. You can’t afford to sit on the sidelines. 

Want to learn more about Enterprise Data Warehouse? Contact us at Capella to discuss the possibilities further!