How a Next Generation Operational Data Store (ODS) Drives Digital Transformation

 

In a previous blog post, we discussed the rising concept of what Gartner has coined a Digital Integration Hub (DIH). In fact, many people view the DIH as an evolved Operational Data Store (ODS) for historical reasons. In this post, we’ll explore the origin of the ODS, and its evolution into a driver of digital transformation. What is an Operational Data Store and How is it Used. read more. webcomputerworld

The Operational Data Store (ODS) concept is not new. It’s actually been around for a couple of decades, continuously evolving with the technology innovations, and is perceived differently by different people. So let’s first try to clarify the common definition. In a nutshell, an ODS aggregates transactional data from multiple sources. Traditionally the ODS was designed and optimized for operational reporting, and was refreshed on a daily or even an hourly basis. Usually the ODS only stores a short time window worth of data.

Why do Organizations Deploy an Operational Data Store?

A main benefit of an ODS is the ability to aggregate data from multiple sources. Many organizations use different systems of record to manage various aspects of their data. Reporting on each data source separately provides a siloed view of the data. The ODS allows for reporting across multiple systems of records for a more complete view of the data. In addition, some systems of record offer limited reporting capabilities, so an ODS is a way for users to gain more comprehensive reporting. Another aspect has to do with database security. Access to systems of record is usually restricted to a select few users. The ODS opens up reporting capabilities to a broader audience within the organization. The Shortcomings of a Traditional Operational Data Store

Traditional Operational Data Stores pose challenges for enterprise, especially as they embark on digital transformation initiatives:

Designed for operational reporting, not for real-time API serving – while a traditional ODS supports operational reporting use cases, it does not offer real-time API services to access the systems of record. Thus it’s unsuitable to meet the requirements of new digital applications.

High latency – the traditional ODS is based on a relational database, or sometimes on a disk-based NoSQL database. These database systems can not provide high performance when handling large amounts of data, and thus can’t support demanding low-latency applications.

Low concurrency – as traditional databases offer limited scalability, they pose a challenge when it comes to user concurrency. Once multiple concurrent users are accessing the data store, the performance takes a hit and as a result, the ODS can not support a high level of concurrent users beyond a certain threshold.

Stale data reporting –  a traditional ODS is not a real-time replication of the systems of record because data is refreshed only periodically – hourly or sometimes daily. While this refresh rate is acceptable for end-of-day reporting scenarios, it is not suitable for digital applications that require real-time data such as in trade risk analysis and reporting, e-commerce, fraud, dynamic pricing and more.

The Evolution of Operational Data Stores – A Paradigm Shift

Digital transformation is driving a paradigm shift, signified by the many organizations that are introducing new real-time digital applications to replace previously offline services. A new breed of technology companies in areas such as fintech and insurtech are introducing new business models and new services that require more than what a traditional ODS can offer. New digital banks are differentiating themselves with continuous innovation and the introduction of new online services. New digital insurance companies are leaving “offline” behind, issuing insurance policies in 90 seconds, and paying claims in 3 minutes. read more. healthnutritionhints