Operational Data Store: What It Is and Why You Need It

Organizations depend heavily on operational data for dashboarding and reporting purposes. If you’re new to the term, operational data is simply data that’s produced by a company’s various daily operations.

For example, operational data may include inventory, customer, purchase, fleet, employee, and expense information. Operational data also forms the basis of advanced analytical data and historical reporting, among other use cases. 

Operational data typically comes from multiple transactional systems and flows into a central database called an operational data store. Keep reading to learn how operational data stores work and why they’re important.

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What Is an Operational Data Store?

An operational data store is a centralized data repository for storing and processing real-time operational data. 

Operational data stores support tactical decision-making. For the best results, they typically operate in conjunction with an enterprise data warehouse system.

This type of repository aggregates transactional data from multiple systems. As a result, operational data stores are volatile, time variant, and subject oriented. 

The purpose of an operational data store is to provide end users with timely data, enabling them to make critical operational decisions in business intelligence systems (BI systems), modeling, and reporting tools.

Who Uses Operational Data Stores?

Businesses use operational data stores for tactical reporting across numerous departments like marketing, sales, production, security, fleet tracking, and billing. What’s more, operational data stores also play a critical role in disseminating data for end users.


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How Operational Data Stores Work

Early operational data stores were legacy systems that lived on-site. Today, a growing number of organizations are implementing robust next-generation systems that are cloud based and fully hosted.

Here is a brief overview of an operational data store workflow. 

1. Data Sources Collect Information

First, data gets generated through various data sources. For example, data may originate from customer relationship management (CRM) or enterprise resource planning (ERP) tools.

These systems usually operate independently of one another, meaning they typically don’t exchange data or communicate. In fact, they are often a mix of different technologies and vendors. 

2. Data Goes Through ETL Processing

Next, data passes through the extract, transform, load (ETL) stage. During this process, data gets cleaned, standardized, and loaded into the operational data store for future analysis.

The operational data store then integrates the real-time data into all of the various applications the enterprise uses, such as BI tools or reports and dashboards. 

3. Data Is Sent to a Data Warehouse 

After data is integrated into the enterprise, the remaining information is usually sent into the data warehouse. This is done for archiving, storage, historical analysis, and reporting. 

One of the main differences between an operational data store and a data warehouse is that the former overwrites data while the latter stores data for historical analysis. 

Most operational data stores can only hold a small amount of data at a time. As such, an operational data store is not for historical reporting.


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Why Is an Operational Data Store Important?

Businesses are becoming increasingly reliant on data. However, for a business to successfully use data, it first needs to collect the data and efficiently distribute it to workers. This is where an operational data store comes into play. 

With this in mind, here are some of the top reasons for using an operational data store.

Accessing Real-Time Reporting 

Businesses can no longer rely on near real-time decisioning. Companies need to respond in real time and act as soon as information gets collected. 

Operational data stores give businesses access to the most updated information, supporting real-time applications like fleet tracking, BI, and inventory management. Operational data stores let teams act on small datasets instead of large ones.

Unifying Disparate Systems

Most companies use a wide variety of disparate data sources that do not interact with one another. As such, data often comes through in many different formats.

Operational data stores consolidate data and merge disparate data sources—providing analytics tools with clean, uniform data for analysis.

Simplifying Analysis

End users shouldn’t have to track down data manually across multiple data sources. This process can be extremely time-consuming, leaving less time for advanced analysis and reporting.

Operational data stores streamline analysis, making data analysis more straightforward for end users. 

Additionally, data is accessible through a centralized location, sparing end users from combing through disparate databases.

Providing a Complete Data View 

The more visibility companies have into their data, the more informed and effective their decisions can be.

By using an operational data store, it’s possible to view data across multiple source systems for improved visibility and complete reporting. As a result, organizational agility increases.

Enabling Secure Data Sharing

Companies often use strict data governance systems, restricting database access to specific user groups. On the one hand, this is critical for preventing data leaks and enforcing least privilege security. On the other hand, this approach also makes it harder to share analytics. 

Operational data stores are often used for secure data sharing across an enterprise, preventing users from having to tap into data sources to obtain information. Instead, data can flow from the operational data store and populate various applications.

What to Look for When Sourcing an Operational Data Store 

At the end of the day, there are several operational data store solutions to choose from. As a result, it can be difficult to decide which platform fits your needs. 

Look for the following when sourcing an operational data store.

Cloud-Native Architecture 

Teams should consider using a powerful cloud-native architecture for maximum scale, performance, and cost efficiency. This is critical for slamming queries and spiking data. It also helps ensure a superior user experience. 

Lightning-Fast Ingestion

One of the main reasons why teams deploy operational data stores is to access real-time data. Yet, not all stores enable actual real-time deliveries. 

When vetting providers, look for a solution that offers true real-time visibility and alerts. For example, Scalyr uses a purpose-built NoSQL columnar database for ultra-fast log ingestion and search functionality. 

Zero Maintenance 

Often, engineers and operations teams wind up spending excessive amounts of time managing and maintaining operational stores. In turn, this negates the reason for using one in the first place.

Remember that operational data stores are all about speed and efficiency. It’s critical to source a system that’s maintenance-free. By doing so, you can prevent team members from spending too much time on back-end tasks.

Full Support 

Chances are your team is going to have a lot of questions when using an operational data store. So it’s critical to have a comprehensive support center for accessing information and resolving issues. 

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Experience the Power of Scalyr

When it boils down to it, it’s vital to have a system in place that can handle today’s intensive workloads. This will be increasingly important looking forward as data usage and computing power increase. 

Engineers shouldn’t have to worry about overloading their operational data stores or waiting for information to process. Speed and efficiency are critical for success in today’s lightning-fast business world. 

Enter Scalyr, a one-stop shop for log analytics and observability SaaS. 

Scalyr’s cloud-native platform brings multiple functions together into a single offering—including log aggregation, analysis, search, dashboards, server metrics, and alerts—for unparalleled data management and integration.

Scalyr is also an ideal solution for businesses that need to replace Elasticsearch. Sclayr can improve performance and reduce overhead for a much leaner approach. 

Ready to see Scalyr in action? Take it for a spin today.

This post was written by Justin Reynolds. Justin is a freelance writer who enjoys telling stories about how technology, science, and creativity can help workers be more productive. In his spare time, he likes seeing or playing live music, hiking, and traveling.