Operational Data Store: Everything You Need to Know in 2023

Updated November 09th, 2023
Operational Data Store

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Operational data store provides a consolidated and up-to-date repository of operational data from various sources. This helps organizations to have a single source of truth and facilitates real-time data access and integration.

In many businesses, data is often distributed across different systems, making it challenging to obtain a comprehensive and real-time view of their operations. Operational data store helps in bringing this data together, which is crucial for making informed, timely decisions.

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This article has provided an in-depth exploration of operational data store, shedding light on its key concepts and its critical role in modern organizations.

Lets dive in!

Table of contents

  1. What is an operational data store?
  2. Why use an operational data store?
  3. Types of operational data store
  4. Operational data store vs. data warehouse vs. data lake
  5. Summarizing it all together
  6. Operational data store: Related reads

What is an operational data store? Explained with example

An operational data store (ODS) is a centralized repository of integrated, subject-oriented, and detailed data that is specifically designed to support operational and tactical decision-making within an organization.

It plays a crucial role in providing real-time visibility and access to the data necessary for day-to-day business operations and decision-making processes.

Key characteristics and purposes of an ODS

  1. Integration: An ODS integrates data from various source systems, such as transactional databases, external data feeds, and other operational data sources. This integration helps ensure that data from different parts of the organization is available in one place.
  2. Subject-oriented: An ODS is typically organized around specific subjects or business areas, making it easier for business users to access the data they need based on their specific needs. For example, a retail ODS might focus on sales, inventory, and customer data.
  3. Detailed data: An ODS stores granular, detailed data, as opposed to data warehouses or data marts, which often contain aggregated or summarized data. This granularity allows for a more in-depth analysis and better support for operational decision-making.
  4. Real-time or Near real-time: In industries like retail, financial services, and manufacturing, where quick decisions are crucial, ODS is often updated in real-time or near real-time. This ensures that the data is always current, enabling timely decisions.
  5. Tactical decision support: ODS primarily serves the needs of operational and tactical decision-makers. It provides data that helps answer questions related to day-to-day operations, such as inventory management, order processing, and customer interactions.

Examples of operational data store

  1. Retail industry
    • Inventory management: In a retail ODS, data about inventory levels, item sales, and restocking orders are stored and updated in real-time. This data helps the store manager make decisions about when to reorder products and how to optimize shelf space.
    • Customer insights: Retailers can use an ODS to track customer behavior, such as purchase history and preferences, in real-time. This data can be used for personalized marketing and promotions.
  2. Financial services
    • Transaction processing: In a financial services ODS, transaction data, including withdrawals, deposits, and fund transfers, is stored and updated in real-time. This supports immediate account updates and fraud detection.
    • Credit risk management: Banks use ODS to monitor and assess credit risk by analyzing customer loan applications and financial data. This helps in making real-time decisions regarding credit approvals.
  3. Manufacturing
    • Production monitoring: Manufacturing companies utilize ODS to track production line data, including machine status, production rates, and defect rates. This data enables real-time adjustments to maintain production efficiency.
    • Supply chain visibility: ODS can be used to monitor the supply chain, including the status of orders, shipments, and inventory levels. This information is critical for just-in-time manufacturing and supply chain optimization.

In each of these examples, the ODS serves as a crucial data repository that enables organizations to have immediate access to the information needed to make operational and tactical decisions. It ensures that decision-makers have the most up-to-date data at their fingertips to respond quickly to changing circumstances in fast-paced industries.

Why use an operational data store?

Using an operational data store provides several advantages that are crucial for organizations, especially those operating in fast-paced industries. Here are some of the key reasons to use an ODS:

  • Real-time visibility
  • Tactical decision support
  • Data integration
  • Subject-oriented data
  • Efficient data processing
  • Data quality and accuracy
  • Cost-effective
  • Support for compliance

Let us understand each of them in detail.

1. Real-time visibility

ODS offers real-time or near real-time access to current and detailed data. This immediate access to data is essential for industries where swift decisions are crucial.

It enables organizations to stay up-to-date with operational processes, track changing conditions, and respond quickly to emerging situations. For example, in stock trading, a real-time ODS ensures that traders have the most current market data to make informed decisions.

2. Tactical decision support

ODS is specifically designed to support operational and tactical decision-making. It provides detailed data at a granular level, making it suitable for answering questions related to day-to-day operations.

Retailers, for instance, rely on ODS to manage inventory, adjust pricing, and personalize promotions based on real-time sales data.

3. Data integration

ODS integrates data from various sources within an organization, including transactional databases, external data feeds, and other operational systems. This integration helps eliminate data silos and ensures that data from different parts of the organization is available in one place.

This promotes data consistency and eliminates the need for decision-makers to access and combine data from disparate sources manually.

4. Subject-oriented data

ODS is typically organized around specific subjects or business areas. This makes it easier for business users to access the data they need based on their specific requirements.

For example, a manufacturing ODS might have sections dedicated to production, quality control, and equipment performance, ensuring that relevant data is readily available to the respective teams.

5. Efficient data processing

ODS often requires minimal data transformation compared to data warehouses. This means that data can be made available for immediate use without complex processing. It reduces the time and resources needed to prepare data for analysis.

6. Data quality and accuracy

ODS places a strong emphasis on data accuracy and consistency. This ensures that the data used for tactical decisions is reliable and trustworthy.

For instance, in the financial services industry, ODS data is crucial for immediate transaction processing and must be highly accurate to prevent errors and fraud.

7. Cost-effective

ODS typically doesn’t require the same level of storage and processing as data warehouses or data lakes. This made it a cost-effective solution for organizations that need real-time or near-real-time access to operational data.

The focus on operational and tactical data needs also helps reduce unnecessary data storage costs.

8. Support for compliance

In regulated industries like finance and healthcare, ODS can help organizations meet compliance requirements by providing a centralized repository of accurate and traceable data.

This is essential for auditing, reporting, and demonstrating adherence to industry regulations.

In short, using an ODS is essential for organizations that need to make rapid, data-driven operational decisions. It offers real-time access to high-quality data, promotes data integration, and is structured to meet the specific needs of tactical decision-makers across various business areas.

5 Types of operational data store you should know!

Operational data stores come in various types, each designed to serve different purposes and business needs. The choice of ODS type depends on the specific requirements of an organization.

Here are some of the main types of operational data stores (ODS):

  1. Near-real-time ODS
  2. Batch ODS
  3. Analytical ODS
  4. Data mart
  5. Data consolidation ODS

Let us understand each of them in detail.

1. Near-real-time ODS

  • Near-real-time ODS is similar to real-time ODS but typically involves slightly delayed updates, which can range from a few seconds to a few minutes. This type of ODS is suitable when instant updates are not critical but data still needs to be very current for tactical decision-making.
  • Use cases
    • Inventory management systems that can tolerate a short delay in inventory updates.
    • Manufacturing systems that need to monitor production and equipment performance with a slight delay.
    • Customer relationship management (CRM) systems that track customer interactions in near real-time.

2. Batch ODS

  • Batch ODS, as the name suggests, updates data in batches or at scheduled intervals, rather than in real-time. Data updates may occur daily, hourly, or at other predefined intervals. Batch ODS is a cost-effective solution for organizations with less stringent real-time data requirements.
  • Use cases
    • Daily financial reports for accounting and auditing purposes.
    • Daily inventory reconciliations for retail businesses.
    • Regularly updated marketing campaign performance reports.

3. Analytical ODS

  • Analytical ODS focuses on storing and providing data for analytical and reporting purposes. It is designed to support data analysis, data mining, and reporting requirements. Analytical ODS often contains historical data and may include aggregated data for complex reporting and analysis.
  • Use cases
    • Business intelligence (BI) systems that generate reports, dashboards, and data visualizations.
    • Data warehouses that store historical data for trend analysis.
    • Customer segmentation and market analysis for marketing departments.

4. Data mart

  • A data mart is a subset of an ODS that is specifically tailored to a particular business function or department. Data marts may contain a subset of the data from the ODS, organized to support the unique needs of a specific group, such as marketing, sales, or finance.
  • Use cases
    • Sales data mart for the sales team to track sales performance.
    • Marketing data mart for marketing analysts to study campaign effectiveness.
    • Finance data mart for financial analysts to perform financial analysis and budgeting.

5. Data consolidation ODS

  • Data consolidation ODS focuses on integrating data from multiple sources into a single, unified repository. It aims to eliminate data silos and provide a centralized view of data from various systems.
  • Use cases
    • Merging customer data from various touchpoints (e.g., website, mobile app, call center) to create a single customer profile.
    • Integrating data from different operational systems in a merger or acquisition scenario.

The choice of ODS type depends on factors such as the organization’s data requirements, the importance of real-time data, budget constraints, and the specific business processes it aims to support. Some organizations may even combine multiple ODS types to meet different needs across the enterprise.

Operational data store vs. data warehouse vs. data lake: Tabular difference

Here’s a comparison of Operational Data Stores (ODS), data warehouses, and data lakes in tabular form, highlighting their key differences:

AspectOperational Data Store (ODS)Data warehouseData lake
Data typeDetailed and real-timeHistorical and summarizedRaw and structured/unstructured
PurposeTactical and operationalAnalytical and reportingStoring and processing big data
Data freshnessReal-time or near real-timePeriodic batch updatesVariable - can be near real-time
Data structureHighly structuredStructuredFlexible (structured/unstructured)
Data storageTypically short-termLong-term archivalLong-term archival
Query and analysisSupports real-time queriesSupports complex analyticsRequires extensive processing
Schema designSubject-orientedStar or snowflake schemaSchema-on-read or schema-on-write
Data qualityEmphasizes data accuracyFocuses on data consistencyData quality can vary widely
Data sourceOperational systemsVarious operational systemsVarious data sources
Data transformationMinimal transformationSignificant transformationLimited to no transformation
User baseOperational and tacticalBusiness analysts and reportData scientists and analysts
Tools and technologiesOften uses ETL toolsOLAP tools, BI platformsBig data frameworks (e.g., Hadoop)
ScalabilityScales as needed for real-timeScalable for analyticsScales to handle big data
CostModerate to highModerate to highCan be cost-effective
Use casesInventory management, real-time monitoringHistorical analysis, reportingBig data analytics, machine learning

While these distinctions help clarify the differences between ODS, data warehouses, and data lakes, it’s important to note that in some organizations, these systems may complement each other. The choice of which data architecture to use often depends on an organization’s specific data needs and objectives.

Summarizing it all together

In the ever-evolving landscape of data management and analytics, the significance of an operational data store (ODS) in 2023 cannot be overstated. Its purpose is to store and consolidate real-time operational data, providing a reliable source for decision-making.

ODS facilitates real-time data access and integration, allowing organizations to make informed decisions based on the latest information from various sources. ODS plays a pivotal role in ensuring data accuracy and consistency by acting as a data cleansing and transformation layer before the data is moved to data warehouses or data marts.

In conclusion, the operational data store remains an indispensable component of the data architecture in 2023. Its role in ensuring real-time data availability, data quality, and operational efficiency positions it as a key enabler for organizations aiming to make data-driven decisions in the present and the future.

As businesses continue to navigate the complexities of the data landscape, understanding and implementing ODS will be a fundamental step towards success in the digital age.

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