- November 19, 2020
Just like in a symphony where the conductor makes specific hand movements to cue all the instruments to be timed and aligned to produce a perfect piece of music, data orchestration is the process where siloed data is organized, transformed, and made available to the data analysis tools of a company to achieve data maturity.
If you are a company that is finding it hard to breakdown data silos, linking different data systems together, and making it available to your data analysis tools – you are not alone. These are some of the biggest challenges that are affecting several companies, preventing them from getting the best out of their data. The good news is that all these challenges can be effectively overcome by deploying a single and powerful process called optimized data orchestration. Let us see what data orchestration is, its uses, importance, and impact.
What is Data Orchestration?
In a nutshell, data orchestration is a process where software takes siloed data from multiple storage systems, links them together, and presents them (or makes them available) to the existing data analysis tools deployed by a company.
Data orchestration creates a bridge between your different storage systems making way for your data analysis tools to quickly access a specific storage system just and when it is needed. And the best part is that the platforms that manage the data orchestration process do not work as another storage system, instead, they work as an independent piece of technology, giving them the needed power to efficiently break down data silos. In other words, it brings your data closer to your data analysis tools, clusters, and regions.
Data Orchestration Process
The data orchestration process contains three steps:
Orchestration software first needs to understand both your existing data and the incoming ones to organize them. It could be your data in cloud-based tools, in your legacy systems, or in your data warehouses, the data orchestration tools should be able to access and understand what type of data it is to begin the organizing process.
Once it organizes the data it is time to transform them. As data come in different formats, the orchestration software takes all the data and quickly transforms them into one standard format, which otherwise would consume an enormous amount of time manually reconciling them. For example, the name, address, and date of birth of a person may appear in different formats, like the month first, date second, year third.
Joe Smith, “5 e”, 12th st, NY, 10.12.1977
The above information can appear in different ways, which can be confusing for the analysis tools. However, data orchestration can transform this scattered information into a single standard format making it easy for analysis tools to access them.
Joe Smith, 5 E, 12 Street, NY, 10–DEC-1977
Activation is the most important part of the orchestration process, as it makes the data readily available to the required tools that need it. This way every time your company tools require data it is there ready to be accessed — without having any need for data loading.
During the data orchestration process, all the above three steps are carried out simultaneously and in real-time, significantly speeding up data analysis. This real-time orchestration becomes crucial for enterprises as they deal with millions of data points every second.
Uses and Importance of Data Orchestration
There is a clear need for data orchestration due to the increasing complexity of data ecosystems and the adoption of new data frameworks. One of the biggest challenges that companies face to achieve data orchestration is the rapid technology changes. This is particularly true with data technologies. As it happens, data technologies go through a major change every two to six years, which means they use about 6 or 7 different data systems in the span of twenty years. And the upshot of all this? A huge amount of scattered data across these 6 or 7 different data systems.
That’s where data orchestration steps into the change the game. Data orchestration platform is the most viable solution where it neither requires any massive migrations nor any additional data storage locations. Here are three different areas where data orchestration is widely used.
1. Data Governance
Governing data becomes all the more difficult when it is spread across multiple data systems, which makes it hard for companies to enforce a data governance strategy. Also, when you are using a tracking plan, data orchestration will make sure the data you have collected is in sync with your tracking plan, and if it is not, the orchestration can quarantine data that does comply with our plan, thus helping you in achieving good data governance.
2. Helps Adhere to Data Privacy Laws
Data privacy laws such as GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act) want companies to justify how, when, and where they procure their data, and above all if it is collected in an ethical way. So, with the data orchestration in place, you can have a clear understanding of how your data was collected, making it easy to comply with the current data privacy laws.
3. Automatic Handling of Heavy Data
If you have multiple data storage systems and want to run queries on them, a non-orchestrated data setup might pose a big challenge, putting your team in a laborious task of running queries on your data repository and then manually transform them. With orchestrated data software, your data is automatically acquired, analyzed, transformed, and prepared, helping you jump through several barriers and save an enormous amount of time and money.
As data is becoming the lifeblood of modern businesses, the concept of ‘data orchestration’ is steadily picking up momentum. Its ecosystem is evolving as new players are taking a data-centric approach. The data workloads are getting higher and more complex. With all these happening concurrently, it appears that adopting data orchestration will be the most sensible decision for companies if they want to be future-ready.
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