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Differences between an Operational Data Store and an Enterprise Data Warehouse

by Sneha
Data Warehouse

An operational data store (ODS) and an enterprise data warehouse (EDW) both centralize and organize data to support modern analytics. However, there are key differences between them. One of the main differences is the temporality of the data. 

New data coming into an ODS overwrites existing data whereas the data in an EDW is not deleted. This is because an ODS supports tactical decision-making and needs the most current version of the data for this, whereas the EDW supports analytical decision-making that requires access to historical data. 

Purpose of the ODS and EDW

A data warehouse is a common business storage solution that acts as a central repository of data integrated from a variety of sources. It connects with data sources on one end and analytical interfaces on the other. It typically populates on a batch basis once or twice a day or less than that. The pace of updates in a batch-oriented data warehouse is usually too slow for operational decision-making but makes data available for analytical decision-making. 

The difference between a normal data warehouse and an enterprise data warehouse (EDW) is its complex structure and size. EDWs are often divided into smaller databases to enable end-users to query them more comfortably. 

The ODS can be a source of data for the EDW, forming a complementary element in terms of operational decision-making. It integrates data coming from different systems of record. This is low-level data, such as transactions or prices, that businesses can access for real-time or near real-time reporting. Integrating the data involves cleaning it, resolving redundancy and checking it against business rules for integrity. 

next-generation operational data store has evolved from the traditional data store to offer improvements in terms of availability, scalability and throughput. 

Components of an ODS and an EDW

Components of a next-generation ODS include a high-performance operational store and compute engine, smart caching, database integration, event-driven architecture, and microservices APIs. 

The EDW contains much greater volumes of data stored on a less frequent basis. It consists of data coming from operational and transactional systems, such as ERPs and CRMs and also has a staging area for aggregating and cleaning data. It has an access space where data is available for querying and reporting and a range of data tool integrations or APIs. 

Characteristics of an ODS and an EDW

The integrated data in an ODS is at a granular, non-historical level in order to perform operational functions and meet specific business goals. It must contain the specific level of detail businesses require. The EDW stores standardized, structured data and end-users can query the data via BI interfaces of choice for reporting purposes. 

The data in an ODS is subject-oriented and is built based on the functional requirements of the business. The data in an EDW is also subject-oriented. For example, it may give total sales of a certain item. Metadata explains where the information comes from. 

The data in an ODS is time-variant. It is current data that’s continuously updated. The data in an EDW is time-dependent and is usually historical data that describes past events and is divided into time periods. 

In an ODS, data is usually updated by being overwritten. It is highly volatile and doesn’t store data because its purpose is real-time analysis and strategic decision-making. The data in an EDW is non-volatile, which means that it is never deleted once it is in the warehouse. Manipulating, modifying and updating of data may take place but no deletions take place unless this involves a general revision every few years to get rid of irrelevant data. 

Benefits of an ODS and an EDW

Integration: With an ODS and an EDW, information from across the organization is integrated and made accessible, allowing for more comprehensive insights and informed decision-making. 

Improved data quality: In the ODS, all the data from diverse sources is cleared of junk, redundancy and loaded to the ODS as indicated by the business rules for regularity and control of data.

EDWs use an extract-load-transform (ELT) approach. The raw data is extracted and loaded into the data warehouse, making it quick to access and analyze.

Holistic customer view: Both an ODS and an EDW offer a more holistic view of the customer which can help to improve marketing campaigns, minimize churn and increase revenue. 

Digital asset management is another way businesses can access all their assets to gain insights from analytics and save money. 

Predictive modeling: Predictive modeling is possible with an ODS because of the availability of real-time data. Better decisions are able to be made when they are based on meaningful insights from analyzing data. 

An EDW also facilitates predictive analytics whereby teams can use data-driven forecasting and scenario modeling to inform their decisions. 

Data regulation compliance: An ODS and an EDW can assist with data regulation requirements because they make it unnecessary to check multiple data locations. 

Hybrid deployments: Many businesses today have data both on-premise and in the cloud. A next-generation ODS is able to deal with hybrid deployments with no impact on production performance. 

To make scaling up feasible, many data warehouses leverage cloud infrastructure. By using hybrid cloud EDWS, it is possible for businesses to optimize costs by using on-premise assets for predictable workloads and offloading unpredictable workloads to the public cloud. 

The bottom line

Managing data as a strategic asset can transform business processes to help organizations achieve their objectives. Implementing an ODS can help businesses to leverage use cases that aren’t always possible with other solutions. It allows them to act on data as soon as it enters the system, so they can make sound decisions based on fresh data. 

An EDW also collects and aggregates data from multiple sources and acts as a repository to facilitate availability and analysis. The main difference is that the ODS provides a snapshot of current data, whereas the data in an EDW is non-volatile and isn’t deleted. The ODS supports tactical decision-making while the EDW supports analytical decision-making. 

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