Data Orchestration Meaning: What it is And What it Means For Your Business
Modern businesses generate a lot of data in their daily dealings. This includes information on their customers, as well as internal data that lets them know how well they’re performing at any given time.
The best companies leverage this data to give them the advantage they need. Data orchestration is integral to doing this well, but what exactly is it, how does it work, and why does your business need to use it? We’ll look into the topic at length below, starting with a quick definition of the topic at hand.
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What is data orchestration?
Big data is a multi-billion-dollar industry that will only keep expanding, which tells us that there’s tons of data out there for businesses to harness and use to their advantage. The question is how to go about doing that on a practical level.
This is where data orchestration comes in. Put simply, data orchestration is the process by which data that’s siloed in more than one storage location is combined and presented to data analysis tools. It’s an unmissable process for anyone looking to go from having access to large volumes of data to making the most of that information.
Data orchestration on its own doesn’t necessarily convert the data into a format that can immediately be analyzed by humans, though. It’ll first have to be put through analytics software. Data orchestration just helps make sure it’s ready for that step.
How does data orchestration work?
Now that we’ve established what data orchestration is, it’s time to consider how it’s put into practice. The actual process of data orchestration is split into three main steps. These are, in order: organizing, transforming, and then activating the data it’s working with. We’ll walk through each of these stages in turn.
As mentioned earlier, data orchestration involves compiling data from multiple sources. Your data orchestration processes will always start with instructing your programs about the nature of the data they’ll be working with, as well as its nature and quantity.
These details are essential for data orchestration programs to properly understand and, as a result, organize the information they’ll be processing.
When your data comes from more than one silo, it’s likely that it won’t all be in exactly the same format.
For example, if you’re working with numbers, you could see the same integer written in multiple different ways. Consider the difference between 1,000, 1000, one thousand, a thousand, and one thousand. Each of these means the same thing, but they don’t look the same.
This stage focuses on transforming all the data into the same format. This makes the next stage easier and saves a lot of time further down the line.
Lastly, it’s time to activate the data. This stage basically involves converting the compiled and transformed data into formats that are compatible with the tools it’ll be used by.
During the activation stage, the data you’ve selected is sent to the applications and tools your company needs it to be in. That might include analytics tools, for example, or other software designed to process and synthesize data into usable forms.
Data orchestration and automation
Data orchestration makes it much easier to take advantage of the benefits of automating development. That’s because it can be fully or partially automated, saving a lot of time for everyone involved and ensuring minimal resources must be committed to the orchestration process.
You can use intelligent automation in your data orchestration to effectively take those workflows out of the hands of human employees and pass them to machines. This is hugely time-saving and makes it much easier to plan and allocate resources.
The kind of automation that works best with data orchestration relies on machine learning, which means you can use a convolutional neural network. We’ll go into more depth on that shortly.
Why do you need data orchestration?
To put it as plainly as possible, you need data orchestration because it saves you time, effort, and resources. It’s effective, it’s efficient, and it frees up your resources to be committed to other places where they’re more dearly needed.
Data orchestration is necessary for turning your raw data into something you can use. It’s possible to make that happen without automated data orchestration, but relying on someone (or even a team) to do that by hand is inefficient. It’s also a big drain on the time and efforts any given group or individual can put in.
Instead, letting data orchestration handle things for you is much better. Let’s consider it via an analogy. Say you’ve got a directory of everyone who contacts your company regularly. It includes absolutely everything: fixed VoIP numbers, mobile numbers, email addresses, physical addresses, and so on.
You could ask someone to be your operator and manually match the data of incoming contacts to the information in your directory. Or you could use a service that does this for you, turning all that disparate information into something you can use and understand easily.
Data orchestration and neural networks
Neural networks are a type of computer model that relies on layered structures. Since these structures are reminiscent of the kind of neural network that’s typically found in the human brain, they share a name.
It’s important to note that neural networks rely on deep learning and machine learning technology, much like data orchestration tools. This means they’re self-improving.
Where data orchestration organizes information and gives it shape and structure, neural networks filter or sort information. This means that data entering a neural network has been passed through data orchestration tools.
Who benefits from data orchestration?
There are a few key types of businesses that can take particular advantage of the benefits that come with using data orchestration. These are the most prominent ones:
Whether they’re creating mobile games, android performance apps, or anything in between, app developers get a lot of mileage out of data orchestration tools.
That’s because the data they collect on their own apps can be used to improve those programs.
For example, let’s say you’ve created an app and sent it into the beta testing phase. You’ll rely on your testers’ reports throughout that step, but you can also automatically log crashes and similar information. That’s a lot of data – and the more of it you’re able to use, the better the final product will be as a result.
With data orchestration, it’s easy to compile all the data you need. It can then be processed into actionable insights that directly lead to creating a higher-quality app.
The main thing that sets an e-commerce business apart from its non-online counterparts is the fact that e-commerce companies are based exclusively online. This means they deal solely with online shoppers, whose numbers are rapidly increasing and whose behavior is much easier to track.
Because of this, e-commerce businesses have access to a wealth of data on their customers. From the exact makeup of their shopping carts to the number of promotions and deals they respond to, to the frequency with which they visit the site, there are plenty of sources of data.
All of those data points have the potential to help e-commerce businesses understand their customers better, optimize strategies, and boost their bottom line. Thanks to this, data orchestration is particularly important to e-commerce businesses.
Depending on how many data collection points a given e-commerce company has, they could have lots of discrete silos of data. These would then have to be collated, which is where data orchestration comes in.
Companies geared toward growth
When you’re looking to grow your company in any capacity, data is always your friend. Gathering data can make it clear where you’re underperforming as a business, as well as which areas you’re excelling in. This helps with choosing where to focus your attention, which in turn makes it significantly easier to ensure you’re allocating resources appropriately. At the same time, data makes your progress quantifiable.
You can use data orchestration, particularly the automated variant, to make your growth-related workflows as seamless as possible.
For example, you could join the 39% of companies that automate sales prospecting, using data orchestration on the resulting data to optimize and refine your prospecting strategies.
In short, if you’re looking to grow your company in a quantifiable, sustainable, and measurable way based on facts and information, data orchestration is a crucial tool to have.
If you work with a lot of data, you’re going to want to rely on data orchestration. There are plenty of advantages and uses of tools to compile your data into usable formats. Those include the ones listed in this article and then some.
Collecting data is already enough work. Processing it after that should be made as easy as possible, and that’s what data orchestration helps ensure. It gathers your disparate data into one place and converts it all into the same format, preventing a human employee from needing to do as much by hand.
In a word, data orchestration is time-saving. It’s useful for exactly this reason.
About the Writer
Pohan Lin is a Senior Web Marketing and Localizations Manager at Databricks, an MLOps company. Pohan specializes in demonstrating the impact of massive-scale data engineering, data analysis, and collaborative data science. With over 18 years of experience in web marketing, online SaaS business, and eCommerce growth, Pohan is dedicated to innovating the way we use data in marketing. Here is his LinkedIn.