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Practical Applications of an Operational Data Store

Big data is the big thing in today’s modern business landscape, with organizations generating and consuming up to 2.5 quintillion bytes of data in a single day alone. With the amount of data created, it can be challenging to catch up and transform this data into business intelligence. One of the main reasons for this is the storage of data; the volume of data continues to increase, forcing many organizations to store data into several, often disparate, data stores. Multiple data stores add to the complexity of overall data preparation, storage, and analysis, leading to longer processing times and system slowdowns. Big data helps improve businesses, however, and a forward-thinking organization should have an appropriate data management system in place.

Fortunately, innovations in data technology have made simple data management possible through the use of an operational data store (ODS). Having one in place helps businesses make sense of gathered data by putting the most important and recent data closer to the user for easy access. Many organizations realize the value of data, but only a number of them possess the needed technology and systems to sort, prepare, and analyze large amounts of data. An ODS works by acting as an intermediary to a data warehouse, storing the latest operational data without storing the history of changes. This helps ensure that data in the ODS stays current at all times. Data tiering is also a key feature, because it helps ensure that pertinent data is kept in the fastest data tier, which also comes with customizable event triggers in case of a security breach or any changes made to stored data. An ODS also helps scale an organization’s capabilities to support transactional and analytical workloads, which is especially useful when migrating into a cloud-based or hybrid platform. By having a modern ODS in place, businesses can get the data they need when they need it and make operational decisions on the fly—without needing to access the other data residing in the data warehouse.

Below are a few operational data store examples that will help illustrate the value of the platform for businesses looking to thrive in the current data-driven landscape.

ODS for Business Analytics

Fresh data is the core of business analytics because its main goal is to analyze and transform data into actionable insights, and that can’t be achieved with stale data. A modern ODS is the ideal solution for business analytics because, architecturally speaking, it’s specifically designed to focus on a single product or service. This enables the platform to constantly update stored data, even several times a day if needed, to keep it current. Even the change history isn’t stored to ensure that the ODS only has the latest version of data at any given time. An ODS also plays well with external applications by synchronizing data even if it resides outside an organization’s systems. Not to mention, it has been designed to specifically address the woes of traditional data management solutions—high latency, which often results in stale data reporting. By providing a snapshot of an organization’s most recent data, an ODS helps achieve the following:

  • Provide a unified data repository that will help improve communication of IT systems.
  • Provide access to non-aggregated, less complicated data so it can be analyzed without the need for operational systems; analysis should not include multi-level joins to minimize complexity but should include simple queries.
  • Provide a merged view of data integrated from disparate systems to help organizations generate reports that provide a general perspective on operational processes.
  • Query data in near real-time to enhance reporting and analysis.
  • Work through time-sensitive business rules to automate processes and significantly improve overall efficiency.
  • Address complex business requirements through a practical structural design.
  • Enhance data privacy and protect the organization from cyberattacks by not storing, and therefore eliminating potential unauthorized access to, historical operations and data.
  • Simplify diagnosis of issues by providing an updated view of the status of operations.

ODS for Financial Services

The financial services industry benefits from technological innovations in data analytics because it helps provide insight into what strategies work through the analysis of gathered data about credit information, spending patterns, and even social media activities. Customer sentiment is vital in financial services as can be seen in the industry’s 22% usage of big data for a variety of purposes, most importantly, risk management, targeted marketing, and improved customer experiences. Financial institutions would do well to leverage modern ODS systems in the management of both static and dynamic data. The platform’s architecture allows for the retention of relational links to datasets that come from different domains. This provides easier control of data elements and, ultimately, the simplification of typically complex, time-consuming processes.

An ODS also helps in performing business intelligence (BI) tasks like customer monitoring, order tracking, and logistics management. By bringing multiple systems or applications together, it provides a single repository of current data wherein the data can also be consolidated without affecting overall system performance. Integrating financial systems also helps maintain a continuous flow of data in between applications so that changes within the data are detected and business rules are created to appropriately address them.

ODS for eCommerce

eCommerce has experienced somewhat of a boom recently due, in no small portion, to the sudden change in consumer shopping behavior. The pandemic has caused unprecedented changes in the market and the way consumers react to trends, so much so that online retailers are now trying to find ways to adapt to the influx of online shoppers. Businesses need to find ways to manage increasing website traffic, and a number of them have turned to in-memory computing to help make data processing faster and enable more efficient data analytics. An ODS helps in the management of mixed workloads within the same architecture without the need to separate analytics databases from transactional databases. It also helps businesses provide personalized customer experiences, implement location-based marketing strategies, intelligently manage supply chains, and facilitate dynamic pricing schemes.

ODS as the Driver of Business Success

Major advances in data processing technologies have made data the core of today’s businesses. To thrive in this digital, data-driven market, organizations need to implement systems that will manage gathered data, making it useful in making sound business decisions and driving growth. This also requires a rethinking of data strategies and operating models; a resilient, data-first model should be at the crux of an enterprise-level data management system like an ODS. By leveraging current and upcoming data systems and methodologies, businesses can get the best out of available data while also minimizing operational costs.

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