Boost Business Analytics with an Operational Data Store

Boost Business Analytics with an Operational Data Store

An ODS can become an integral hub for your business information systems by providing a central repository of data from multiple sources.

• By Edward Huskin
• April 16, 2021

Data is the lifeblood of every business today, and its management and governance are main factors that can help deliver efficiency through business analytics, but data management processes can be complicated. Data preparation has also become a hot topic among IT teams and users who must ensure that data is ready for business intelligence (BI) and analytics. By itself, raw data is often relatively useless in the realm of business because it must undergo several processes that transform it into insights that support business decision-making.

Data preparation begins once raw structured and unstructured data is ingested; data prep complexity increases as volumes of data grow and user needs become increasingly diverse. This results in more complicated processes to:

• Standardize how data is defined for users and applications
• Improve overall data quality
• Transform data for Bl and analytics.

One of the main data-preparation challenges is that most organizations use several systems to handle data; this leads to a need for more sophisticated data integration methods and complicated data processing procedures. Fortunately, an operational data store (ODS) can provide the necessary capabilities that will allow an organization to integrate data from disparate, even geographically distant, sources.

An ODS focuses on current data, which makes it ideal for operational decision-making. Designed to focus on a single product, service, or customer, the data in an ODS is constantly updated to ensure that it has the most current data. This means that data is updated several times each day without storing the history of changes.

An ODS often sits between the data sources and the data warehouse as an interim area where records are inserted continuously for aggregation across historical views. Companies may use the centralized repository of a data warehouse for businesswide strategies, but they turn to a modern ODS when taking a tactical approach to operations.

Because it’s designed to address granular queries, an ODS updates data frequently on any given day, providing real-time or near real-time operational reports. It’s not optimized to handle historical data and trends analysis. An ODS often acts as a data source for the data warehouse, which manages historical data and other more complex queries. Modern ODS systems also allow for synchronization of data to external applications, even if the application resides outside the organization’s systems.
Organizations planning a digital transformation can save valuable time and money by investing in an ODS because it will help refocus an organization’s digital solutions approach from traditional offline databases to real-time applications and cloud-based architectures.

Operational Data Store for Business Analytics

An ODS delivers the best available instance of a data element at any given time. It provides the most recent snapshot of an organization’s data to help achieve the following:

• Provide a unified data repository that will help improve communication of IT systems by connecting all data sources to the data warehouse, which identifies the “single source of truth”
• Provide access to unaggregated, less-complicated data at a granular level so it can be analyzed without the need for operational systems and provide quick insights without accessing historical data; analysis should not use multilevel joins against data in an ODS to minimize complexity but should include simple queries. An ODS is also not designed for real-time API serving, high user concurrency, low latency, and has a tendency toward stale data reporting,
• Provide a merged view of data integrated from disparate systems to help organizations generate reports that analyze data across an enterprise
• 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.

From Data to Insights

An operational data store should be part of an organization’s data systems because it can provide a central hub or repository of data from multiple sources. Data can then be optimized and organized into a single format via a series of ETL (extract, transform, load) operations.

Business decision-making becomes easier because it is data-driven, making it less complicated and eliminating the guesswork for increased operational efficiency. Modern organizations are heavily dependent on data; as such, how they handle data and what systems they use for data management will have an effect on their overall performance. Data preparation is a vital step in the process, and an ODS will help make it, and every other step in the data management process, simpler and quicker by efficiently bridging the gap between the data warehouse and the data sources, no matter how disparate they may be.