Real-Time Data Integration Best Practices and Architecture

8 minutes read

real time data integration

Article Summary

Real-time data integration is increasingly becoming the new gold standard in data integration. It immediately processes and stores data as it is collected from various sources, making sure that users have up-to-the-minute information on what is happening within their organization.

Without proper data integration, organizations struggle to make their operations efficient and agile, attain more imaginative customer engagement, and make informed business decisions. The modern enterprise is under a lot of pressure from users demanding better performance and faster data flows.

In a study conducted by Vanson Bourne, 49% of IT decision-makers said their top challenge is integrating data across different environments. Organizations have little choice but to embrace forward-leaning data integration strategies that react quickly to changing market and customer needs to compete in today's cutthroat business environment.

As some organizations struggle with outdated technologies, tools, and spreadsheets, others are innovating by leveraging data integration best practices that incorporate automated data pipelines, cloud-based data lake houses, and real-time data streaming to deliver improved business agility. Leveraging real-time data integration techniques is increasingly becoming the new normal for businesses that want to thrive in today’s marketplace; real-time data integration is the new gold standard in data integration.

What Is Real-Time Data Integration?

You cannot sufficiently answer the question: what is real-time data integration without defining data integration. Data integration refers to merging different data types from several sources into a single location, such as an application, database, or file system, to be viewed together, processed, analyzed, and shared.

On the other hand, real-time data integration combines data from multiple sources and makes it available for analysis in near real-time. This type of data integration is essential for organizations that need to make decisions based on the most up-to-date information.

Real-time data integration can be used to combine data from various sources, including applications, databases, ERP systems, and CRM systems. The data can then be accessed through a single interface, making it easy for users to see all the most relevant information in one place. For example, a retailer could use real-time data integration to track inventory levels across its stores and warehouses. This would allow the retailer to see where stocks are low and decide where to ship products.

Real-Time vs. Batch Data Integration: What's the Difference?

real time vs batch data integration

Data integration can be either real-time or batch. In batch data integration, data is collected from various sources and then processed and stored in a central location at set intervals. This means there can be a significant delay between when an event occurs and when it is reflected in the data.

On the other hand, real-time data integration immediately processes and stores data as it is collected from various sources. This allows users to have up-to-the-minute information on what is happening within their organization.

Critical Data Organizations Need to Integrate

Organizations need to integrate many different types of data to make informed decisions. Some of the most critical data sets that they should integrate are:

  • Financial data, including revenue data, expenses, and profits.
  • Customer data, including customer names, contact information, and purchase history.
  • Operational data, including inventory levels, production schedules, and shipping information.
  • Product or item information, including product descriptions, SKUs, and pricing information.
  • Supply chain data, including supplier information, lead times, and order status.
  • Market data, including market trends, competitor information, and customer sentiment.

Challenges in Data Integration

Organizations face many challenges when trying to integrate data from multiple sources. Some of the most common challenges are:

  • Many data sources across the organization: There can be hundreds of data sources across different departments and business units. This can make it difficult to identify all the data sets that need to be integrated.
  • A variety of data formats: Data can be in a variety of formats, such as structured (e.g., CSV), semi-structured (e.g., XML), or unstructured (e.g., log files). This can make it difficult to standardize the data for integration.
  • Poor data quality: Data from different sources can vary in quality, making it difficult to trust the data and use it for decision-making.
  • A lack of governance: There can be a lack of standards and procedures for collecting, processing, and storing data, leading to errors and inconsistencies.
  • Migrating, transforming, and processing data takes a lot of time: It can take a significant amount of time and resources to migrate data from one system to another, transform it into the correct format, and process it. This can delay decisions and result in missed opportunities.
  • No unified view of data for employees: Data is spread across different departments and business units, making it difficult for employees to get a complete picture of what is happening across the organization.
  • Challenges with demand forecasting and planning: Without a complete and accurate view of all the data, it can be difficult to forecast demand and plan for future needs accurately.
  • Challenges with identifying risk or fraud happening within a process: Without real-time data, identifying risk or fraud as it is happening can be challenging, leading to losses for the organization.
real-time data integration best practices and architecture

How Real-Time Data Integration Solves These Challenges

Real-time data integration overcomes these challenges by providing a single platform for integrating data from multiple sources. This platform can collect data from different sources, standardize it, and then provide a unified view of the data for employees. Using real-time data integration tools allows organizations to instantly synchronize the data sources giving all employees the same view of the data.

Real-time data integration can also help forecast demand and plan for future needs. With real-time data, organizations can identify trends and patterns as they happen. Businesses can use this information to adjust plans and processes to avoid disruptions.

Real-time data integration eliminates the risk of manual errors, allowing organizations to set up alerts that notify them of errors in the data. What's more, real-time data integration can help identify risk or fraud as it is happening. By having a complete and accurate view of all the data, organizations can quickly identify any anomalies and use this information to take corrective action.

Real-Time Data Integration Best Practices and Architecture

Data integration is a critical piece of most business intelligence and data warehousing initiatives. To make your data integration initiative as effective as possible, you should follow best practices and use a data integration architecture well suited to the organization's needs.

The best data integration architecture best practices help organizations improve the way they manage data. Perhaps the most essential best practice is to choose the right data integration platform for your needs. There are many different data integration platforms available, and you must select one that will meet your organization's specific needs.

Another best practice is establishing clear goals and objectives for your data integration initiative. What do you hope to accomplish by integrating data? Once you have set your goals, you can develop a plan to achieve them.

In addition, you must monitor and evaluate your data integration efforts on an ongoing basis. By constantly monitoring your data integration architecture, you can ensure that it meets your organizational needs.

It would help if you also considered using a data warehouse appliance such as a data stage. Data warehouse appliances are purpose-built for data warehousing and offer many benefits, including pre-tuned architectures, optimized data loading, and the ability to scale easily.

Another data integration best practice is to use a data federation. Data federation allows organizations to access data from multiple disparate sources and combine it into a single logical view. This eliminates the need to physically merge data from different sources, which can be time-consuming and error-prone.

Finally, it would be best to consider security when designing a data integration solution. Ensure that all your sensitive data is encrypted in transit and at rest, and tightly control its access. By following these best practices, organizations can ensure that their data integration solutions are secure, efficient, and effective.

Real-Time Integration: A Case Study

To illustrate how organizations  benefit from  using real-time data integration best practices, let's consider a case study:

Imagine an online store where customers make orders over the internet. Performing pricing and inventory checks would need to be real-time because supply is limited. The store might receive customer orders from its website, mobile app, and call center. The data from all these sources would need to be integrated in real-time to provide accurate pricing and inventory information.

Real-time data integration would provide the online store with a complete and accurate view of its pricing and inventory data. This would allow the store to provide accurate information to its customers and prevent any disruptions in the ordering process.

Key Take-Aways

In summary, the benefits of real-time data integration are worth looking into. Real-time data integration is critical for organizations that need to make decisions quickly. 

It overcomes data integration challenges by providing a single platform for integrating data from multiple sources. This platform can collect data from different sources, standardize it, and then provide a unified view of the data for employees.

Real-time data integration is a must when managing a business and keeping internal operations running smoothly. It has a variety of advantages, you get access to the consolidated and unified view of all data within the organization, optimized and streamlined processes, and no risk of manual errors.

Put It Forward's real-time data integration solutions integrate data from various sources and transform it into high-level business insights. Business leaders benefit from using this accurate information to make decisions faster, minimize expenditures, and improve the enterprise's overall efficiency.

Need some help with your data integration? Consider a data integration solution that goes beyond traditional tools with real-time insights and predictive analytics. Reach out now; Our experts are here for you!

Elsa Petterson

About Author
Elsa Petterson

Partner success manager that specializes in connecting her work with finding new ways on how to effectively engage customers, understand their preferences, communicate faster, and close new deals. She has been expanding her expertise at Put It Forward since 2016. Passionate about data automation, integration, and predictive analytics for revenue generation, Elsa is an expert in finding solutions that help organizations operate effectively in marketing, finance, sales, and other departments.

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