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Actian Blog / Enterprise Data Warehouse

Enterprise Data Warehouse

Enterprise Data Warehouse

Do you need a Data Warehouse or Enterprise Data Warehouse (EDW) in your organization today? Its functional units, people, services, and other organizational assets support the vision and mission of an organization. An organization can have strategies, tactics, and operational activities that are performed to meet the overall vision and mission of the organization for service to their customers. To be high performing today requires utilizing the power of data supported by innovations in information technology. People and advanced technologies are making decisions every day to support the organization’s success. Data drives decisions.

Today, one of the essential tools is an Enterprise Data Warehouse to support effective decisions using a single source of truth across the organization. The organization has to work as a high-performing team, exchanging data, information, and knowledge for decisions, and the EDW plays a central role. As organizations move their IT to the Cloud, the EDW is also transforming and moving there as well, further improving the organization’s business decision-making.

What is an Enterprise Data Warehouse?

An Enterprise Data Warehouse is a central repository of data to support a value chain of business practice interactions between all functional units within the organization. Also knows as a EDW Data Warehouse, where data is collected from multiple sources and normalized for data-driven insightful analytical decisions across the organization for the services and products delivered and supported for its customers. Data is transformed into information, information into knowledge, and knowledge into decisions for analytics and overall business intelligence (BI). Enterprise Data Warehouse Reporting capabilities take advantage of the EDW to provide the organization with needed business and customer insights.

Enterprise Data Warehouse architecture makes use of an Extract, Transform, and Load (ETL) process to ingest, consolidate and normalize data for organizational use. The data is modeled based on decisions that need to be made by the stakeholders and put in a consistent format for usage and consumption with integrated technologies and applications. The organization’s sales, marketing, and other teams can use the EDW for an end-to-end perspective of the organization and the customers that are being serviced. Enablement of the EDW utilizing the cloud is a plus because of the power of cloud technologies today in making data accessible anywhere and anytime.

The basic Enterprise data warehouse requirements that make up the EDW system include but are not limited to the following components:

  • Data Sources – databases, including a transactional database, and other files with various formats
  • Data transformation engine – ETL tools (typically an external third-party tool)
  • The EDW database repository itself
  • The database administration for creation, management, and deletion of data tables, views, and procedures
  • End-user tools to access data or perform analytics and business intelligence

Leveraging data from multiple data silos into one unified data repository that contains all business data is powerful. An EDW is a database platform of multidimensional business data that different parts of the organization can use. The EDW has current and historical information that can be easily modified, including the model to support changes in business needs. EDWs can support additional sources of data quickly without redesigning the system. As the organization learns how to use the data and gives feedback, the solution can transform rapidly to support the organization and the data stakeholders. As the organization matures, so can the data in the EDW rapidly mature.

Enterprise Data Warehouse vs Data Mart

An Enterprise Data Warehouse becomes a single source of truth for organizational decisions that need collaboration between multiple functional areas in the organization. The EDW can be implemented as a one-tier architecture with all functional units accessing the data in the warehouse. EDW can also be implemented with the addition of Data Marts (DM). The difference between DM and EDW is the DW is much smaller and focused than an enterprise data warehouse. Enterprise Data Warehouse services are also for the entire organization, whereas a Data Mart is usually for a single line of business within the organization.

Data Marts contain domain or unique functional data, such as only sales data or marketing data. The data mart can extend the usage of the EDW using a two-tier architecture leveraging on-premise and/or the cloud capabilities that use the EDW as a source of data for specific use cases. Data marts typically involve integration from a limited number of data sources and focus on a single line of business or functional unit. The size of a data mart is in gigabytes versus terabytes for an EDW. Data Marts do not have to use an EDW as a data source but can use other sources specific to needs.

Organizations may want to use a data mart or multiple data marts to help increase the security of the EDW by limiting access to only domain-specific data through the data mart if using a two-tier architecture. An organization may also use the data mart to reduce the complexity of managing access to EDW data for a single line of business.

Choosing between EDW and a data mart does not have to be one or the other. Both are valuable. Remember, the outcome is to provide data for high performing decision support within the organization. EDW helps bring the bigger organization perspective for delivering and supporting business services. Data marts can complement the EDW to optimize performance and data delivery. Overall, enterprise-wide performance for decisions, reporting, analytics, and business intelligence is best done with a solution that spans the organization. A complete end-to-end value view of customers, products, tactics, and operations that support the organizational vision and mission will benefit everyone in the organization, including the customers.

Data Marts are easier and quicker to deploy than an EDW and cost less. A line of business can derive value quickly with a solution that can be deployed faster with a limited scope, fewer stakeholders, less modeling, and integration complexity than an EDW. The data mart will be designed specifically for that line of business to support their ability to work in a coordinated, collaborative way within their function. This can help create a competitive advantage against competitors by enabling better data analytics for decision support within a specific line of business or functional unit.

Enterprise Data Warehouse and the Cloud

Cloud Enterprise Data Warehouse (EDW) takes advantage of the value of the cloud in the same manner as many other cloud services that are becoming the norm for many organizations. The EDW itself may be better suited to reside in the cloud instead of on-premise. The cloud provides:

  • The flexibility to build out and modify services in an agile manner.
  • The potential to scale almost infinitely.
  • The assurance of enhanced business continuity.
  • The ability to avoid capital expenditures (CapEx).

Organizations can still choose to architect hybrid-cloud solutions for EDW that take advantage of on-premise organizational capabilities and vendor cloud capabilities. EDW should be planned using expertise focused on organizational constraints and business objectives for best long-term solutions that can take advantage of the ease of use with continuous improvement of the EDW solution. This use of expertise includes using Data Marts in the solution for maximum benefit to the organization.

Conclusion

EDWs architecture can be challenging for bringing the organization’s data into one database, especially all simultaneously. Organizations should design for the big picture and deploy incrementally, starting with specific business challenges or specific lines of business. This will create patterns of success for improving the next increment. This will also help with the faster delivery of a solution that can benefit the organization without the complete solution being finished.

In many instances, an organization can’t simply rely on silos of line-of-business data marts. They need enterprise data warehouse reporting to get a complete view of customers, products, operations, and more to make decisions that best benefit the whole company. Yes, enterprise data warehouse architectures can be painful. In most instances, you can deploy incrementally, starting with specific domains or business challenges. This will help you deliver value faster and evolve into the holistic purpose your EDW is intended to serve.

The power of having a cross-organizational repository of meaningful data to enable better decision making and overall better service delivery and support for the customer outweigh the challenges with the architecture. An organization that does this successfully will gain improved marketability, sales, and overall better relationships with its customers. The business data insights will also enable the organization to position its internal assets more appropriately based on the improvements in data insights, analytics, and business intelligence.

Managing and utilizing data for an organization has to be done effectively, efficiently, and economically for value. Data is the organization’s lifeblood that supports the long-term viability of the organization itself. An organization that is not informed and does not view data as a point of contention for business service performance and decisions may find themselves optional in the marketplace. An EDW can help with the organization’s current and future business decision needs.

About Teresa Wingfield

As the Director of Product Marketing at Actian, Teresa Wingfield focuses on hybrid cloud data solutions. Prior to joining Actian, Teresa managed cloud and security product marketing at industry leaders such as Cisco, VMware, and McAfee. She was also Datameer’s first Vice President of Marketing where she led all marketing functions for the company’s big data analytics solution built on Hadoop. Before this, Teresa was VP of Research at Giga Information Group, acquired by Forrester, providing strategic advisory services for data warehousing and analytics. Teresa holds graduate degrees in management from MIT’s Sloan School and software engineering from Harvard University.