This is part two of a six part series on "Simplifying Data Orchestration: What is Data Orchestration?" Expertise is not found by using complexity, but in the ability to take a complex topic and break it down for broader audiences.
Introduction:
In the first article in this series, I mentioned the discovery questions. Some common discovery questions are: who does data orchestration, what is data orchestration, when does data orchestration happen, where does data orchestration happen, why does data orchestration exist, and how do you do data orchestration?
These are hard to find concrete answers to when evaluating a new product, tool, or way of doing things. In this article, I'll answer the "what" question. Having a solid definition for your data term dictionary is important. I reached out to a few sources from many different data backgrounds to help answer this question for me.
What is Data Orchestration?: (community version)
Here was the prompt given to the data community members:
"In 2-3 sentences, what is data orchestration?"
That's it! Looking back, I'm glad that I implemented a length limit. Some people could have written much more! Without further ado, in no particular order, here are their responses (drumroll):
“Data orchestration is a process that facilitates a consistent refresh of end-to-end data models - from ingestion of data from external sources all the way to the implementation of source of truth business logic concepts in the data warehouse - on an expected cadence and at an accepted quality, freshness, at completeness level.” - Elena Dyachkova (Associate Director of Data Science, Spring Health)
"Data orchestration is bringing together multiple sources or systems to make new things possible that wouldn't be possible with a single source or system. For example, understanding a user's journey, even if the underlying data comes from different systems, or understanding the risk of a transaction by incorporating multiple inputs to get a more predictive model." - Zach Hendlin (CEO, Zing Data)
“Data orchestration is like the nucleus, it's the brain. It controls the order of operations to properly execute activities so you can get from point A to point B in your workflow.” - Monica Miller (Developer Advocate, Starburst)
“Data Orchestration is the process of establishing cadence, dependencies, scalability and stability into data pipelines and processes. When done correctly, an orchestration platform can also be used as a self referential blueprint for all the data pipelines and process that are used.” - Steven J. Pope (Architect | Manager, InfoCepts)
"Data Orchestration is the process of triggering workflows using changes in data that lives in business systems. However, because the underlying data is often spread across multiple systems, has different levels of ‘freshness’, a sophisticated system is required to keep everything in sync and run timely transformation to keep the data accurate, to ensure workflows are triggered correctly. This is where data orchestrators often shine.” - Rez Kahn (Chief Product Officer, Pace)
“Data orchestration is the bringing together of disparate sources and types of data in order to solve a business problem. The types of data can be very different, but they must be focused and synchronized on the problem that is to be solved.” - Bill Inmon (Best-selling Author & CEO, Forest Rim Technology)
Just for fun, I gave ChatGPT the exact same prompt:
"Data orchestration refers to the process of integrating, coordinating, and automating the flow of data between different systems, applications, and processes. It involves managing data pipelines, ensuring seamless data integration, transformation, and delivery throughout an organization, enabling efficient data management and utilization." - ChatGPT
Wow! It was fun to see how people from different roles and experiences responded. Some gave detailed definitions and examples while others were brief and generalized. All the responses were fantastic. Do you feel like you understand what data orchestration is now? What definition was your favorite?
*ENHANCE*
What if you want to show someone else a nice, bulleted list to describe what data orchestration is? We've got you. Let's take the action phrases from all those definitions and put them together. This could even be a presentation slide! They are great points to answer a more nuanced question - what does data orchestration do? Here's what we've got:
- brings together disparate sources and types of data to solve a business problem
- gets you from A to B in your workflow
- controls the order of operations to properly execute activities
- facilitates a consistent refresh of end-to-end data models
- establishes cadence, dependencies, scalability and stability into data pipelines
- triggers workflows using changes in data from business systems
- makes new things possible that wouldn't be possible with a single source
*ENHANCE*
What happens if we want to make it even *more* simple. Shall we zoom in even further? What are some of the top words that come to mind after summarizing those data orchestration definitions? To answer that, here's a word cloud of the top 10 words from the community definitions.
How are we feeling now? A little better? We've pulled quotes, created action bullet points, and generated a word cloud! That's lots of great material to help you (and others) understand what data orchestration is.
What is Data Orchestration?: (T̶a̶y̶l̶o̶r̶'̶s̶ Shipyard's version)
To harness the potential of data, businesses need to manage and integrate it across various systems and processes. This is where data orchestration plays a pivotal role.
Data orchestration is the coordination and automation of the flow of data between tools, systems, and processes. It's like a control tower for your data pipeline - tracking all the movements and changing course when needed. Data practitioners are the air traffic controllers and luggage logistics managers. They make sure the airplanes (or data) land where they're supposed to, when they're supposed to, and with the right luggage.
Sub- Definitions: Key Components of Data Orchestration
1. Data Integration
Data orchestration provides a high-level view of all sources in your pipeline. Data can come from applications, APIs, streaming platforms, data storage systems, and more. Within the data orchestrator is where the magic happens to see them all come together. You cannot overstate the business value of integration. Connecting disparate data sources provides business users with additional metrics that couldn't be generated from a single source alone.
2. Data Transformation
Data often requires cleansing, normalization, aggregation, and enrichment. Data orchestration can provide many of the transformations within the orchestration tool itself. It can also direct data to transformation tools that can handle more complexity and version control. Apply data transformation rules and business logic to ensure data consistency and relevance.
3. Workflow Automation
Automation is a critical aspect of data orchestration. It enables organizations to streamline workflows, reduce manual efforts, and save time. Automated workflows ensure data pipelines get executed on a set schedule or by a triggering event. Error handling can also be automated. Set triggers and tests along the pipeline, and make sure it stops the pipeline and sends an alert when an error is encountered.
4. Data Quality
The best way to ensure data quality is to stop the pipeline from sending bad data before it reaches the end. With orchestration, you can set your expectations on both the execution and output of a step in the pipeline. When troubleshooting an issue, check the logs of your pipeline to see why the error happened or if one of your guardrails failed. Not only does orchestration help maintain high data quality for end users, it speeds up fixing data quality issues.
Conclusion
Now you know how to answer the question, "what is data orchestration?" That's wonderful, but are you left feeling like it got cut short? There must be more to say, right? How does this all work? Indeed, that's a great question, and also the question we'll be tackling in the next article.
Stay tuned for part three of this blog series, where we tackle "how does data orchestration work?"
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