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Operational Data Store vs. Data Warehouse: What’s the Difference?

By: Edward Huskin


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Data has become so ingrained in how business is done these days that data storage is now considered a core component of business intelligence. Data quality and processing speed are now vital business considerations, helping improve operational efficiency and providing useful insights for better business decision-making.

As volumes of data continue to increase, data analytics is becoming more of a game changer as new technologies arise to take advantage of big data and its multitude of use cases. One major consideration for both big and small businesses aside from finding ways to process data as quickly as possible is choosing an effective way to store this data.

A data warehouse is a common business solution because it acts as the central repository of data integrated from a variety of sources. It helps in the generation of analytical reports by being the source of both current and historical data. By storing large amounts of both, a data warehouse is a great source of insight for long-term strategic planning. Companies like Acuto offer data warehouse solution for businesses to consolidate their data, making it easier to streamline their reporting and analytics.

The main disadvantage is that data updates are done in scheduled batches, which means that there’s the possibility of stale data reporting. A cost-effective solution to the challenge of stale data is an operational data store (ODS). This acts as a repository that stores a snapshot of the business’s most current data. Highly beneficial in real-time analyses and strategy planning, an ODS is highly volatile and doesn’t store the history of data changes to ensure that everything within the repository is current and updated. It is typically used as an intermediary between transactional databases and the data warehouse.

How is an Operational Data Store Different?

Although an ODS and data warehouse have a number of similarities, there are some crucial differences that distinguish one from the other, including the following:

Volatility of data

Data in an ODS is extremely volatile because it’s regularly updated as new data comes along. This ensures that data values in an ODS are updated in near real-time. On the other hand, a data warehouse focuses more on stability. In contrast to an ODS, it retains historical values and integrates these with incoming ones. Data may not always be the most recent since the contents of a data warehouse are updated in batches only a few times per day.

Scope of data

Data in an ODS has a short life cycle because it can be overwritten with new incoming data at any given time. Bl queries will return only the most current data, which helps in operational decision making as it provides insight on the current state of the business. With a data warehouse, the repository of data is much larger, with support for historical queries. It’s useful for activities that require significant volumes of data like analytics.

Growth rate of data

In an ODS, growth occurs in relation to the growth rate of data in transaction databases. This linear growth allows for easy vertical scaling of available storage space. In contrast, the growth of a data warehouse is typically exponential because new and historical data reside together. To make scaling up feasible, most data warehouses leverage a cloud infrastructure.

Why Businesses Should Use an Operational Data Store

Small businesses would benefit from the implementation of an ODS because it will help them leverage use cases that aren’t always feasible or possible with other solutions. With data at the forefront, data analytics has become an integral part of business systems and is set to be one of the top trends for small businesses now and in the foreseeable future. Companies would be remiss not to maximize the potential benefits of big data today, regardless of the nature of their business. The following are a few of the advantages of an ODS that will help your business gain a competitive edge.

  • Real-time reporting and analytics
    Because an ODS stores the most recent version of data, it helps perform real-time BI tasks, including logistics management, order tracking, and customer monitoring. It allows businesses to act on data as soon as it enters the system, helping them make sound decisions based on fresh data.
  • System Integration
    The ODS architecture allows for the continuous flow of data between systems, which is required if systems are to be integrated. This, in turn, makes it possible to create business rules within the ODS so that a corresponding action is triggered on a system whenever data changes in another. This is especially useful in eCommerce and customer monitoring where customer data constantly changes.
  • Consolidation of data
    For businesses that rely on multiple systems or applications, an ODS can help consolidate and bring their data together, even if they come from disparate sources. It acts as a single repository for current data while also remaining light and fast. An ODS can consolidate data even if they come from entirely different systems or geographical locations.

The Future of Business Data

The continuous growth of data poses challenges along the way, and technology may not be quick enough to catch up. Difficulties in data processing and storage are merely the tip of the data challenge iceberg. There are still the issues of data privacy and security that can lead to bigger issues down the line. However, the potential value of big data and data composability can’t be overlooked. It’s a game-changer that will give rise to new rules and methods of doing business. Companies would do well to make data an integral part of their processes sooner rather than later.

Published: February 18, 2021

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