What is data orchestration? Learn how it simplifies data pipelines and discover the right data orchestration platform to manage your workflows.
Just as money is the raw material for the global financial system, data is currency for modern organizations, especially those steeped in analytics and AI.
Data orchestration is the backbone of modern data engineering because it provides a way to automate and manage the flow of data, also known as a data pipeline. The process includes organizing, transforming and activating data to make it readily usable in business applications, in turn informing sound decision-making.
In banking terms, you can think of data orchestration like a giant virtual cash register that holds currencies from every country, organized numerically by value and alphabetically by nation. Automating the way this money is managed and organized means that whenever more cash comes in, it gets sorted automatically by denomination, and the dollar totals by country are updated.
Data orchestration comprises automated activities and processes around the coordination and management of complex data pipelines and workflows — gathering, transforming and processing a diverse array of data so it can be used in AI and analytics. The automation part of that definition is key. Large organizations have massive stores of data that are often siloed among the groups that generate and use it daily. Using orchestration tools to automate various tasks within data workflows brings order and consistency to raw data handling, enhancing data quality and availability while supporting compliance with data security regulations.
Most data-driven enterprises can benefit from data orchestration because automating data pipelines speeds the process of harnessing data from different sources and systems within the business while helping improve your data’s quality and machine readability.
The larger your organization, the more complex your data universe is likely to be. Data orchestration helps you harness that data and prepare it for use in AI and analytics systems that drive decision-making, operate more efficiently, help keep data secure and better serve customers. Put simply, data orchestration is essential for managing operational complexity, ensuring reliability and strengthening stakeholder trust.
Data orchestration is not merely the practice of storing or managing data. It is a series of processes in which tools work with raw data from diverse sources and prepare it for an array of data-driven business uses. In detail:
The first step in data orchestration involves pulling data from the relevant systems and sources, then centralizing it. Data sources may include on-premises or cloud-based internal CRM or ERP systems, for example, or external sources, such as social media or news websites. Organizing data includes aggregating similar data in one place — all sales data, for example, or all phone numbers of prospective new customers — so it’s ready to be optimized in the next step.
In the transformation step, the orchestration tool takes raw data and standardizes it by applying business rules and consistent formatting. If there is missing information, the tool may need to enrich the data by filling the gaps and correcting any errors. The goal is to end up with accurate, consistent data that is in a standard format so machine learning models and analytics tools can more easily use it — and so organizations can then manipulate this data and readily share the information via dashboards and visualization tools.
Once you’ve normalized data so it’s in a readable format and reasonably free of errors, you need to make it available to the systems that will analyze it or serve it to visualization and dashboard tools. This is called data activation.
Data orchestration tools make data more accessible for use in business decision-making, data analysis or AI applications. These tools help automate the steps described above and extract more value from raw business data, reducing the time it takes to turn data into insights. Let’s explore these benefits.
When you feed cleansed, accurate data into machine learning and data analysis, you get more reliable, consistent results.
The larger and more complex your organization is, the more data it will generate. Data orchestration tools can help break down data silos to make this data easier to access. They also help you improve data quality, which is essential for scaling data-driven initiatives and remaining agile.
Tools that automate and optimize data orchestration help you quickly drill down on specific pieces of information, making it easier to spot anomalies, improving data quality over time.
Time to data (TTD) refers to the time it takes from when data is created to when you can access it for use as a business asset. (It’s sometimes also referred to as data latency.) Data orchestration tools help reduce latency, which supports easier and faster data processing. Faster TTD also shortens the time it takes to extract value from data, improving the speed of the overall decision-making process.
Global enterprises are subject to data security and privacy regulations, such as GDPR, HIPAA and CCPA. Data orchestration tools can help automate certain data security controls to simplify compliance with these rules.
Data orchestration may also have its share of potential pitfalls, which can be exacerbated by ever-growing data volumes and complexity. Here’s a look at three common challenges.
Data orchestration tools themselves can be difficult to work with — and so can tools that teams use for automating parts of the process. Further, tools do not always work easily together, so integrating them all may require customizing software or altering some data-handling processes to meet specific needs.
In programming, dependencies refer to tasks that must be completed in a set order. Dependencies occur when the ability to complete a step in a linear process depends on the results of a previous operation. The more complex your data workflows are, the more dependencies there are to keep track of. Data engineers use directed acyclic graphs (DAGs) to visually map data flows and illustrate dependencies.
The more data you have coming in from different systems, the bigger the job is to streamline all the processes required to run that data through the various data orchestration steps. In other words, data storage has to scale, and so does the orchestration function itself.
Data systems — and your data itself — may not always be easy to integrate. As noted earlier, data systems don’t always work well together without some customization or retooling, and integrating data from different sources is not always straightforward, with data format incompatibilities a common issue. You may also hit roadblocks combining data from legacy systems with newer data that has been optimized for modern, cloud-based data platforms.
Following data orchestration best practices can help you overcome many of the above challenges. Here are five key tips to follow.
Data workflows are instructions for how data moves and undergoes transformation across your enterprise IT systems. Clearly defining the sequences and steps that cover this process, from extraction to organization to activation, will help set you up for success.
Similarly, you need to select the data orchestration tool that best meets your overall organizational and specific project needs. To assist in this choice, consider the tool’s user-friendliness, as well as its compatibility with your existing systems and whether — and how well — it can handle complex workflows.
How will you know if data orchestration is working? Monitor the process to look for bottlenecks, data-handling errors and ways to fine-tune everything.
If you’re just getting started with data orchestration, narrow your scope for the first couple of projects. Use proofs of concept to assess how well your workflows and tools are performing. Then iterate with different improvements to learn what works best.
It’s always a good idea to document processes used in your data orchestration workflows. Having this record makes it easy to revisit workflows regularly, document errors and make necessary changes.
Data orchestration can help improve your chances of successfully using data to make strategic improvements in your business, and the ability to define and automate workflows for gathering, transforming and activating data is a hallmark of any high-performing, data-driven organization. Data orchestration goes beyond data management, offering intelligent, integrated systems that reduce complexity and help you derive value from your data faster and more reliably.
Data orchestration tools automate the data orchestration process. They integrate with other data-handling and visualization tools to speed up the process of gaining insights from raw data.
If you’ve used any streaming media service, you’ve experienced data orchestration in action. That’s because the media you’re consuming is data, and the services are using orchestration to serve it to you based on your searches, your preferences and what you have liked or added to your lists.
Another example you’ve likely experienced firsthand: When retailers need to synchronize online and physical store inventory, they use orchestration to pull sales data from both locations, which includes updated inventory data from physical stores and warehouses that source online sales. This helps them track demand patterns, which in turn informs how best to maintain their inventory.
Data ingestion and data orchestration are different but related components of data management. Ingestion is just one component of data orchestration, describing the process of gathering raw data from wherever it resides in your enterprise. Data orchestration is the broader process of ingesting, organizing and processing data so it can be used in AI or other data-driven systems.
