Data orchestration is an increasingly important factor of modern data management. But what is it and why does it matter? 

Put simply, data orchestration is a process that involves managing and processing data in an organization to make it available when and where it's needed. There are several steps in the data orchestration process to collect, transform and manage the data across an organization’s data sets and data sources. Businesses are becoming increasingly reliant on data-driven decisions, making data orchestration more necessary than ever. In this blog, we’ll be diving deeper into data orchestration and why businesses need to utilize data orchestration tools today.

Why is it important?

First, let’s look at some of the reasons data orchestration is important. We know that data orchestration helps organizations make more data-driven decisions, which would make it worthwhile on its own. But that’s oversimplifying the contributions that data orchestration can make to a business. Here are some of the main reasons more businesses are defining their data orchestration processes and dedicated resources to it:

• Data orchestration provides a single source of truth — One part of data orchestration is pulling data from all relevant and disparate data sources across an organization. Data becomes more coordinated, shareable and easier to update. With data orchestration, organizations can eliminate data silos and ensure they’re making the most of their collected data.
• Data orchestration increases operational efficiency — Data orchestration can help reduce operational costs and improve data accuracy and integrity. Furthermore, data orchestration allows more processes to be automated, saving businesses time and money. 
• Data orchestration improves data literacy and democratization — As mentioned, data-driven decisions are the norm for modern businesses. All of your employees must be able to make these decisions and understand data. Data orchestration makes data more easily accessible, and employees can better understand data.
• Data orchestration improves data-driven decisions — Data orchestration improves data access and analysis, allowing businesses to make better data-driven decisions. The unified view of data from multiple sources allows businesses to easily identify patterns, trends and insights from available data. 

Ultimately, data orchestration is essential for businesses that want to manage their data more effectively and make better decisions. By creating a single source of truth and automating processes, businesses can ensure that their data is always up-to-date and accurate while saving time and money.

What are the benefits?

Data orchestration can be invaluable for businesses and organizations of all sizes. Every business can benefit from a streamlined way to gather, store, manage and analyze data. With data orchestration, businesses can gain actionable insights from their data more quickly. Let’s take a brief look at some of the biggest benefits of data orchestration:

• Increased Efficiency — Data orchestration allows businesses to automate data-related processes and streamline data management. This increases overall efficiency and reduces operating costs.
• Improved Scalability — Along with enabling automation, data orchestration allows organizations to handle larger data sets more efficiently. This helps businesses scale and keep up with the ever-increasing amounts of data they take in.‍
• Increased Flexibility — Data orchestration allows organizations to organize and consolidate data across multiple data sources and build complex data pipelines without having to code all of them manually. This improves overall organizational flexibility and gives them more adaptability.‍
• Improved Security — Data orchestration helps organizations consolidate and manage data with more efficient processes, allowing them to keep data more secure. It also helps businesses define access protocols so that only those who are authorized can get access to data.‍
• Improved Decision-Making — As mentioned, data orchestration improves the efficacy and efficiency of data-driven decisions. By helping democratize data and ensuring that data is accurate, teams can get faster access to data when and where they need it. ‍
• Improved Collaboration — Automating data operation and giving more employees access to data allows teams to collaborate more easily on projects. They can work together, gain insights faster and automate data sharing between departments. With more streamlined communication, your teams will have more streamlined collaboration.

These are just some of the major benefits a business can reap from implementing data orchestration processes.

The 4 parts of data orchestration

There are four main components or stages of data orchestration. They include:

• Data Integration — The first part of data orchestration is data integration and ingestion. This process involves collecting data from an organization’s various data sources and putting it into a centralized repository. This could mean extracting data from databases, files, APIs and other sources to prevent data silos and make sure everyone is seeing the same data. Data integration can be done manually, but it’s much simpler to do with automated tools. Especially when you’re dealing with larger data sets and large amounts of data.
• Data Transformation — After data has been collected and centralized, the next step is data transformation. Across various data sources, data could be in different formats. The data needs to be standardized and processed so it can be used for analytics, BI or any other business processes the business plans on using the data for. The data transformation stage may involve combining different data sets, filtering out unnecessary data points, applying mathematical functions and more.
• Data Delivery and Cleansing — After the data is transformed, it needs to be delivered to the destination that requires it. This could be another application, a database or a third-party service. The data also needs to be cleansed to ensure data is accurate and reliable. This process involves eliminating corrupt data, inconsistencies, duplicates and other issues that could cause issues with data integrity.‍
• Data Monitoring and Maintenance — Finally, after data has been integrated, transformed and cleansed, it requires ongoing data monitoring and maintenance. This includes ensuring that the data is up-to-date and accurate, as well as taking any necessary steps to keep it secure. This process involves continually syncing your data with your data sources, ensuring new incoming data goes through the data orchestration process to make it usable for the organization.

Examples

Data orchestration can be seen at work in many different industries. Essentially, if you’re an organization that uses data for data-driven decisions, it can be beneficial to have data orchestration tools and processes in place. Here are some examples of industries that use data orchestration regularly:

• Healthcare — Healthcare commonly uses data orchestration to manage the various data sources and patient care systems. Data orchestration helps health care providers keep track of patient medical history, medications, treatment plans and more. Orchestration can also help with data governance and data sharing workflows between health care organizations, ensuring patient data is secure when it’s transferred.
• E-Commerce — E-commerce is another industry that utilizes data orchestration effectively. Businesses can easily manage customer information, track inventory levels, track pricing for goods and services, gain insights from customer data, identify customer preferences, observe customer behavior and more.
• Finance — The finance sector can use data orchestration to manage transactions, collect financial data, improve data-driven investments and much more.
• Energy — Companies in the energy sector can use data orchestration to monitor energy consumption by location. The data collected from energy sources can be used to forecast demand, optimize energy consumption, adjust pricing and more.
• Manufacturing — The manufacturing industry uses data orchestration in a variety of ways. Manufacturing companies may use data orchestration to track inventory levels, machine performance data on the floor and logistics data. This can help these companies optimize processes, reduce costs and improve supply chain management processes.

Overall, data orchestration can help organizations save time, money and resources by streamlining their processes and increasing efficiency. As data orchestration tools become more prevalent and advanced, they continue to offer organizations more opportunities to improve and use data to its fullest potential.

Popular data orchestration tools

Businesses that need data orchestration processes should consider data orchestration tools. These powerful tools can help make data orchestration easier to implement and automate many of the stages of data orchestration. If you’re looking for data orchestration tools for your business, consider these:

• Apache Airflow — Apache Airflow is an open-source platform for managing data pipelines and orchestrating complex data workflows. It is used for scheduling, monitoring and orchestrating ETL jobs. Apache Airflow is equipped with a powerful and intuitive UI and easily integrates with other tools in your data stack.
• Microsoft Azure Data Factory — Microsoft Azure Data Factory is a cloud-based data orchestration service that enables users to build, schedule, execute and monitor complex data pipelines. It supports a wide variety of on-premise and cloud-based data sources and can help you move large volumes of data quickly. 
• Metaflow — Metaflow is an open-source data orchestration tool built by Netflix. Metaflow takes a human-centric approach to data management projects and is Python-based. Metaflow helps to make data more accessible and gives data scientists the tools to streamline processes for their data projects.
• Prefect — Prefect is an open-source data orchestration tool. Prefect allows teams to deploy, manage and monitor data pipelines. It helps automate data orchestration processes and provides a clean and simple UI for users.
• K2View — K2View is a flexible, no-code data orchestration solution that can consolidate and transform data from any data source. K2View automates various orchestration workflows, simplifying data orchestration processes for organizations at scale.
• Secoda — Secoda is a comprehensive data enablement platform. This all-in-one tool provides data catalog, lineage and documentation workspace capabilities. With Secoda, you can more easily automate data orchestration tasks like data collection, data governance and data discovery.

Businesses can easily automate their data orchestration tasks using one of these tools, ensuring their data is up-to-date and organized in a single location. These tools can also help businesses optimize the performance of their data pipelines and reduce the manual effort associated with managing them.

Try Secoda for free

Search for a comprehensive data enablement platform? Look no further than Secoda. Secoda enables data orchestration with tools for data cataloging, data discovery, data access management and much more. Secoda enables all of your team members to harness the power of your organization’s data by making it easily searchable and consolidated in one unified repository. Secoda can pull your data across data sources, and your team members can find the data they need with the same ease as conducting a Google search. This saves your data team time and resources and gives everyone on your team the ability to make more data-driven decisions.

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If you’re ready to make the most of your data, Secoda is here to help. Secoda easily integrates with your current data stack and is perfectly suited to helping organizations automate their data orchestration processes. Best of all, you can get set up in minutes. Ready to learn more about Secoda and what it can do for your business? Sign up today and try Secoda for free.

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