Enterprise Data Warehouse (EDW)
An Enterprise Data Warehouse (EDW) consolidates data from multiple sources, giving the right people access to the right information so that they can take necessary action. An Enterprise Data Warehouse (EDW) can act as a central repository of integrated data from one or more disparate source systems.
The primary attraction of an enterprise data warehouse is that all the data is constantly available for analyzing and planning purposes. The alternative is for a business to have different databases for each major branch or organizational division, leading to a complex schedule of data reporting to allow for higher level analytics and planning. An enterprise data warehouse imposes a standard treatment of data and can grow with a business’s needs by adding classifications as they emerge in the business model. Ideally, an enterprise data warehouse provides full access to all the data in an organization without compromising the security or integrity of that data.
Over the course of the past few years, Enterprise Data Warehouse systems (EDW) have become one of the most important components of modern decision support systems. Their main benefit consists in bringing together data from different sources – not available in appropriate form in the operational systems, for instance because of missing historical data – in one central location and preparing them for analysis, or bringing together data from diverse sources that may have totally different formats. Added to this is a landscape that is increasing significantly in complexity and acting as the source of structured data (e.g. ERP systems), unstructured data (social networks) and even Big Data (e.g. sensor data from numerous events). Thanks to the use of an EDW system, the typical risks inherent in heterogeneous data warehousing that most companies are faced with, i.e. losing track, increasing data redundancy and long decision-making paths, can be avoided effectively. All relevant partial data from the most important data sources along your company’s entire value chain are brought together simultaneously in a way that facilitates rapid and purposeful decision-making at all company levels. Various types of information, for instance on suppliers, products, production, stock levels, partners, customers, staff and sales, are all combined in the data warehouse system to provide a holistic view.
Attributes of Enterprise Data Warehouse (EDW)
In order to give a clear picture of an Enterprise Data Warehouse and how it differs from an ordinary data warehouses, the following five attributes may being considered. This is not really exclusive they bring people closer to a focused meaning of the Enterprise Data Warehouse from among the many interpretations of the term. These attributes mainly pertain to the overall philosophy as well as the underlying infrastructure of an Enterprise Data Warehouse.
- The first attribute of an Enterprise Data Warehouse is that it should have a single version of truth and that entire goal of the warehouse’s design is to come up with a definitive representation of the organization’s business data as well as the corresponding rules. Given the number and variety of systems and silos of company data that exist within any business organization, many business warehouses may not qualify as an Enterprise Data Warehouse.
- The second attribute is that an Enterprise Data Warehouse should have multiple subject areas. In order to have a unified version of the truth for an organization, an Enterprise Data Warehouse should contain all subject areas related to the enterprise such as marketing, sale, finance, human resource and others.
- The third attribute is that an Enterprise Data Warehouse should have a normalized design. This may be an arguable attribute as both normalized and de-normalized databases have their own advantages for a data warehouse. In fact, may data warehouse designers have used denormalized models such as star or snowflake schemas for implementing data marts. But many also go for normalized databases for an Enterprise Data Warehouse in the consideration of flexibility first and performance second.
- The fourth attribute is that an Enterprise Data Warehouse should be implemented as a Mission-Critical Environment. The entire underlying infrastructure should be able to handle any unforeseen critical conditions because failure in the data warehouse means stoppage of the business operation and loss of income and revenue. An Enterprise Data Warehouse should have high availability features such as online parameter or database structural changes, business continuance such as failover and disaster recovery features and security features.
- Finally an Enterprise Data Warehouse should be scalable across several dimensions. It should expect that a company’s main objective is to grow and that the warehouse should be able to handle the growth of data as well as the growing complexities of processes which will come together with the evolution of the business enterprise.
Benefits of Enterprise Data Warehouse (EDW)
Having access to an effective enterprise data warehouse provides a number of significant benefits. These advantages range from simple, house-keeping issues, to ones that have a significant impact on the success of the company as a whole. Here are some of the most-impactful advantages offered by data warehouses:
- Data warehouses offer added support for data, in that they are designed to track, manage, and analyze information, in order to provide a more-actionable resource.
- EDW’s work hand-in-hand with other analytics programs to promote company growth. In fact, about 37% of businesses surveyed state that data analytics processes facilitate growth in the business.
- They provide context, and demonstrate the relationships between individual data points. This allows for better understanding of what the information means, and how it can be put to use.
- Data Warehouses are capable of tracking and modifying marketing campaigns, for faster, more accurate evaluation of campaign effectiveness.
- It allows users to store as much data as needed with regards to a large variety of parameters. That data can be drawn from multiple, often-unrelated sources.
- EDW’s refine data, eliminating useless excess or redundant information, and improving overall data quality.
- Gives the user the ability to examine information within the platform itself. This keeps data manipulation to a minimum and integrity at its highest level. Allowing decisions to be made with the most accurate data possible.
- Taken all together, these advantages have been known to reduce cost associated with data analytics, and to increase company ROIs. And given that data-related problems cost the majority of companies more than $5 million annually, this is one advantage that is particularly difficult to overlook.
- Definition of Enterprise Data Warehouse (EDW) Atlas Systems
- Explaining Enterprise Data Warehouse (EDW) Techopedia
- Understanding Enterprise Data Warehouse (EDW) CubeServe
- Attributes of Enterprise Data Warehouse (EDW) GeekInterview
- What are the Benefits of an Effective Enterprise Data Warehouse (EDW)? Salesforce
- The Shortcut Guide To Large Scale Data Warehousing and Advanced Analytics Mark Scott
- The analytical advantages of an enterprise data warehouse system Techtarget
- Enterprise Data Warehouse (EDW) V/s Modern BI Siddhant Gupta
- Modern Enterprise Data Warehouses: What’s Under the Hood? cio.com
- Clinical Use of an Enterprise Data Warehouse NCBI
- How to Successfully Adopt an Enterprise Data Warehouse in the Cloud Cloud Technology Partners