10 Operational Data Store Best Practices

An Operational Data Store (ODS) is a type of database used to store data from multiple sources for operational reporting and analysis. It is designed to provide a single source of truth for operational data and is used to support operational decision-making.

In this article, we will discuss 10 best practices for designing and implementing an Operational Data Store. We will cover topics such as data modeling, data integration, data security, and more. By following these best practices, you can ensure that your ODS is optimized for performance and accuracy.

1. Define the purpose of your ODS

An ODS is a database that stores data from multiple sources and provides access to it in real-time. It’s used for operational reporting, analytics, and decision making. Knowing the purpose of your ODS will help you determine which data sources should be included, how often they should be updated, and what type of data should be stored. This will ensure that your ODS is optimized for its intended use and can provide accurate and timely insights.

2. Choose a database that can handle high volumes of data

An operational data store is used to store and process large amounts of data quickly, so it’s important that the database you choose can handle this.

For example, if your organization needs to store and analyze customer data in real-time, then a relational database like MySQL or PostgreSQL may not be able to keep up with the demand. In this case, you should consider using a NoSQL database such as MongoDB or Cassandra which are designed for high performance and scalability.

No matter what type of database you choose, make sure it has the features and capabilities necessary to meet your organization’s needs. This includes things like security, reliability, scalability, and flexibility.

3. Determine how you will integrate and transform data into your ODS

Integrating data into your ODS is essential for ensuring that the data is accurate and up-to-date. Without integration, you may end up with outdated or incorrect information in your ODS. Additionally, transforming data ensures that it is consistent across all sources and can be used to generate meaningful insights.

To ensure successful integration and transformation of data into your ODS, consider using an ETL (extract, transform, load) tool. An ETL tool will allow you to easily extract data from multiple sources, transform it into a unified format, and then load it into your ODS. This process helps to streamline the data integration process and makes sure that all data is accurately represented in your ODS.

4. Decide who will have access to the ODS

The ODS is a powerful tool that can be used to store and analyze data from multiple sources. It’s important to ensure that only authorized personnel have access to the ODS, as it contains sensitive information about your business operations.

You should also consider setting up different levels of access for different users. For example, you may want to give certain users read-only access while others are allowed to make changes or add new data. This will help protect the integrity of the data in the ODS and prevent unauthorized changes.

5. Create an architecture for your ODS

An ODS architecture should be designed to ensure that the data is accurate, secure, and accessible. It should also provide a way for users to easily access the data they need without having to go through multiple steps or systems.

The architecture should include components such as an ETL process, data warehouse, and reporting tools. The ETL process will extract, transform, and load data from various sources into the ODS. The data warehouse will store the data in a structured format so it can be accessed quickly and efficiently. Finally, the reporting tools will allow users to analyze the data and create reports. By creating an architecture for your ODS, you can ensure that all of these components work together seamlessly to provide the best possible experience for your users.

6. Build your ODS in stages

Building an ODS in stages allows you to test and refine each stage before moving on to the next. This helps ensure that your data is accurate, complete, and up-to-date. It also gives you the opportunity to identify any potential issues or areas for improvement early on, so they can be addressed before they become major problems.

Additionally, building an ODS in stages allows you to prioritize which data sets are most important to your organization. This ensures that you’re focusing on the data that will have the greatest impact on your operations and bottom line.

7. Designate someone to manage your ODS

Having a designated person to manage your ODS ensures that the data is accurate and up-to-date. This person can also be responsible for monitoring performance, troubleshooting any issues, and ensuring that all users have access to the data they need. Additionally, this individual can help ensure that the ODS meets compliance requirements and security standards. Having someone dedicated to managing the ODS will make it easier to keep track of changes and updates, as well as identify potential problems before they become major issues.

8. Make sure your ODS is secure

An ODS is a centralized repository of data that can be accessed by multiple users and applications. This means it’s important to ensure the security of the data stored in the ODS, as well as the access controls for who can view or modify the data.

To secure your ODS, you should use encryption techniques such as SSL/TLS, two-factor authentication, and role-based access control (RBAC). Additionally, you should regularly audit user activity and monitor system logs for any suspicious behavior. Finally, make sure to keep your software up to date with the latest security patches.

9. Keep your ODS up-to-date

An ODS is designed to provide real-time access to data, so it needs to be constantly updated with the latest information. If your ODS isn’t up-to-date, then you won’t have an accurate picture of what’s happening in your business.

To ensure that your ODS is always up-to-date, make sure that all relevant systems are integrated and that any changes made to those systems are reflected in the ODS. Additionally, set up a regular schedule for refreshing the data in the ODS, such as daily or weekly. This will help ensure that your ODS remains current and reliable.

10. Monitor your ODS’s performance

An ODS is a critical component of your data architecture, and it needs to be running optimally in order for the rest of your system to function properly.

Monitoring your ODS’s performance will help you identify any issues before they become major problems. You should monitor things like query response times, disk space usage, CPU utilization, memory usage, and network traffic. If you notice anything out of the ordinary, you can take steps to address the issue quickly. This will ensure that your ODS remains reliable and efficient.