Data Orchestration: The Key to Streamlining Your Business Workflows

Modern businesses need to manage data spread across multiple siloed sources. The typical retail business needs to pull in and analyze data from sales, inventory, suppliers, marketing and promotions, finance, employees—and much more. In fact, 1 in 4 enterprises leverage 100+ applications in their operational processes. If not managed properly, the data can easily get scattered, leading to inefficiencies and missed opportunities.

Data needs to be organized, arranged, and expressed in a way that makes sense. That’s where data orchestration comes in. By coordinating and synchronizing various data streams in an optimized time and order, a data orchestration layer enables smoother workflows, makes governance easier, and offers more insightful analytics.

In this article, we will explain what data orchestration is, its benefits, and potential challenges. We will also list a few popular data orchestration tools to help you get the most value from your data analysis.

What is data orchestration?

Data orchestration is an automated process for managing and coordinating data from multiple sources, combining and organizing it so it can be analyzed. It transforms fragmented data into a cohesive resource that can be used to drive actionable insights. It ensures that data from all parts of the organization is accessible and usable, supporting everything from daily operations to strategic planning.

Data orchestration harmonizes disparate data sources, giving businesses a unified view of their data and facilitating more informed decision-making. Without data orchestration, businesses risk making misguided decisions due to incomplete or outdated information, which can lead to costly mistakes.

Data orchestration vs. ETL

While data orchestration and ETL (extract, transform, load) are both important functions within data management, they serve different purposes and operate in distinct ways:

ETL is a time-honored process that involves extracting data from various sources, transforming it into a suitable format or structure for analysis, and then loading it into a target system, such as a data warehouse. This process is typically batch-oriented, meaning it handles large volumes of data at scheduled intervals. It’s a reliable method for consolidating data and preparing it for detailed analysis and reporting.

Data orchestration is a broader and more dynamic process. It involves managing the flow of data across multiple systems and ensuring that it is continuously organized and made available for analysis in real-time or near real-time. Data orchestration goes beyond the ETL process by integrating various data pipelines, automating workflows, and providing a unified view of the data landscape. It supports more complex data environments and is crucial for businesses that require timely insights from their data.

We’ve broken out some of the key differences below:

• Scope: ETL is specific to data extraction, transformation, and loading; data orchestration encompasses the entire data flow and management process.
• Timing: ETL is typically batch-oriented, while data orchestration supports real-time or near-real-time data processing.
• Flexibility: Data orchestration is more flexible and can handle diverse data sources and formats. ETL processes, on the other hand, are pretty rigid and can’t adapt to changing parameters.
• Automation: Data orchestration centers its process around automated workflows that can adjust to changing data conditions; ETL processes are typically performed with consistent parameters, making them more static.

5 Benefits of data orchestration

Data orchestration offers big benefits to any data-driven organization, regardless of size:

• Improved data governance: Data orchestration ensures data consistency, quality, and compliance across the organization. Automating data workflows and centralizing control allows businesses to maintain better oversight of their data assets, reducing the risk of errors and ensuring that data policies are applied uniformly. For instance, a retail company can use data orchestration to ensure that customer information is updated consistently across all channels, maintaining accuracy and data protection.
• Reduced roadblocks: Data orchestration helps eliminate bottlenecks that often occur in data processing by integrating and automating data flows. This creates smoother and more efficient workflows, enabling teams to access the data they need without delays and allowing IT departments to focus on strategic initiatives rather than routine data management tasks. For example, in an e-commerce business, data orchestration can streamline the process of updating product information across multiple platforms, reducing manual intervention and speeding up time-to-market for new products.
• Reduced costs: Effective data orchestration reduces the need for manual work, lowers the chances of costly errors, and makes better use of existing infrastructure. For instance, a financial services firm can automate the reconciliation of transaction data, reducing the time and cost associated with manual reconciliation processes and decreasing the risk of financial discrepancies.
• Faster time to insights: With data orchestration, businesses can achieve faster time to insights by ensuring that data is always up-to-date and readily available for analysis. Real-time or near-real-time data processing enables quicker decision-making and more agile business strategies, where timely insights can make a significant difference. For example, a marketing team can use real-time data to adjust campaigns on the fly, optimizing for better performance and higher return on investment.
• Enhanced scalability: As organizations grow and data volumes increase, managing and processing this data becomes more complex. Data orchestration can handle larger datasets and more complex workflows without compromising performance. This scalability ensures that businesses can continue to derive value from their data even as their needs evolve. For example, a growing online retailer can use data orchestration to manage increasing amounts of customer data, sales transactions, and inventory levels to maintain efficient operations and provide a seamless customer experience, even during peak shopping seasons.

Challenges of data orchestration

While data orchestration provides numerous benefits, it also comes with potential challenges. Understanding what they are can help businesses prepare and implement effective strategies to overcome them.

• Data silos: Many organizations store data in isolated systems or departments, making it difficult to integrate and access. This fragmentation can hinder the orchestration process, as it requires significant effort to connect and unify disparate data sources. For instance, a company might have customer data spread across CRM systems, marketing platforms, and sales databases, each with different structures and access protocols.

The solution? Adopt a holistic approach to data management to ensure that all data systems are interconnected and accessible. Implementing a centralized data management strategy and using integration tools can help break down these silos, easing data orchestration efforts. Regular audits of data sources can also identify and mitigate any potential barriers to data integration.

• Issues with data quality: Incomplete, conflicting, or inaccurate data can lead to faulty insights and misguided decisions. For example, discrepancies in product information across different departments can result in errors in inventory management and customer dissatisfaction.

The solution? Ensure high data quality with robust data governance practices, regular data cleaning, and validation processes. Implementing automated quality checks and establishing clear data standards can help maintain data integrity within the orchestration process.

• Data misalignment: Data misalignment occurs when different data sources use varying formats, structures, or terminologies, hindering the effectiveness of data orchestration. For instance, one department might use a different naming convention for products than another, causing confusion and errors in reports.

The solution? Standardize data formats, conventions, and terminologies across the organization. Leveraging data transformation tools and techniques can help align disparate data sources, ensuring consistency and coherence. Conducting regular workshops and training sessions can help ensure that all departments are on the same page regarding data standards.

Data orchestration workflow in 5 steps

Here’s a typical step-by-step data orchestration workflow:

1. Data collection and organization: The first step in data orchestration is collecting the data from various sources and then organizing it for processing. This involves identifying all relevant data sources, including databases, cloud storage, and third-party applications. Ensuring that users have the necessary permissions and access to these data sources helps ease the process.
2. Data transformation: Once collected, the data then needs to be transformed into a format that can be easily analyzed. This involves cleaning the data to remove any inaccuracies or inconsistencies, as well as standardizing it to ensure compatibility across different systems. Data transformation may include additional tasks such as filtering out duplicates, converting data types, and aggregating data from various sources.
3. Data integration: The next step is to integrate the transformed data into a centralized repository, such as a data warehouse or a data lake. This ensures that all relevant data is accessible from a single location, facilitating easier analysis and reporting. Various integration tools and platforms are available to automate this process and ensure seamless data flow.
4. Data validation: Before the data can be used for analysis, it needs to be validated to ensure its accuracy and reliability. Checking the data for errors, inconsistencies, and outliers is a critical step for maintaining data quality and ensuring trustworthy insights. Automated validation tools can help streamline this process and reduce the risk of human error.
5. Data analysis and visualization: The final step in this workflow is analyzing and visualizing the data to derive actionable insights using analytical tools and platforms to process the data and generate reports, dashboards, and visualizations. By presenting the data in a clear and accessible format, businesses can make informed decisions and drive strategic initiatives. Data analysis and visualization tools can also help identify trends, patterns, and anomalies that might otherwise go unnoticed.

Data orchestration tools

Good data orchestration tools tend to be scalable and flexible, with a user-friendly interface and plenty of features that support data quality, security, and real-time analysis. Here are just a few widely used data orchestration tools that can help your business streamline its data workflows.

Metaflow, developed by Netflix, is a human-centric framework for data science applications. It simplifies the process of building and managing real-life data science projects. Metaflow integrates seamlessly with Amazon Web Services (AWS) and offers robust data orchestration, versioning, and workflow management features.

Stitch is an ETL service built for data teams. It supports various data sources and destinations, enabling businesses to consolidate their data into a central repository. Stitch is known for its simplicity, scalability, and flexibility, making it a popular choice for data orchestration.

Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. It is highly customizable and supports complex workflows with dependencies. Airflow’s strong community support and extensive integrations make it a versatile tool for data orchestration.

Prefect is a modern data workflow orchestration tool designed to simplify the scheduling and monitoring of data workflows. It offers a Python-based platform that integrates well with various data tools and supports both local and cloud deployments. Prefect's focus on data quality and observability makes it an excellent choice for managing complex data workflows.

Luigi, created by Spotify, is a Python module designed to build complex batch job pipelines. It effectively manages dependency resolution, workflow management, and visualization, simplifying the administration of long-running tasks. Luigi is particularly useful for businesses with complex ETL processes and large-scale data workflows.

Facilitate the process with CData

Implementing data orchestration can be complex, but using the right tools can simplify and streamline the process. CData Connect Cloud provides seamless connectivity and integration to boost your data orchestration processes and drive faster, more accurate decision-making.