In the current organizational landscape, everything is done through the storing, handling, and processing of data. The databases for companies continue to expand exponentially, and there is always more data to leverage and make use of than before. However, the massive amount of data also brings with it a lot of responsibility regarding security, governance, and also processing issues that companies have to deal with.
Organizations have various data repositories where data is organized according to the sources from which it is extracted and where it is stored. This is done to maximize efficiency for business operations, but this isn’t enough. In order to break down data silos, there is a need for data orchestration.
In this guide, you will learn:
- What is Data Orchestration?
- How Does Data Orchestration Work?
- Parts of Data Orchestration
- Data Orchestration Platforms
- Data Orchestration Limitations
This is part of our comprehensive data management guide.
What is Data Orchestration?
Data orchestration is a process that consolidates data from numerous storage locations and combines it in a rational manner so that it can be used by a company’s data analysis and management platforms.
Data orchestration is usually backed by the use of software platforms, which connect various storage systems and enable other applications to use them when needed.
How Does Data Orchestration Work?
Basically, data orchestration brings together different data sources and builds a combined data repository that can be accessed for analysis and other purposes. It is quite similar to an orchestra, where several unique instruments join together and play in perfect harmony. Each data source, like an instrument, has its own place and identity individually, but orchestration blends them together.
In many ways, data orchestration appears similar to automation, and both of them are related to each other. Automation is used to make manual tasks automatic through minimal human input, in order to maximize efficiency.
On the other hand, data orchestration manages the coordinated automation of various tasks that can work together in a streamlined process. Therefore, you can call orchestration a complex and advanced version of automation.
Parts of Data Orchestration
Although each company has different data analysis and management tools, the process is generally the same, and all of them employ certain methods or steps in order to achieve the common goal: unifying data silos from different sources into a common repository.
Let’s have a look at these components and parts.
1. Collection and Preparation of Data
In the first stage, the data has to be collected and prepared, because it is scattered and unstructured. Therefore, it has to be structured and organized before it is input into the software used for data orchestration.
This process also involves performing various checking methods to ascertain integrity, authenticity, and correctness. Moreover, the data is labeled appropriately, and any third-party data is also aligned with the current database structure.
2. Data Transformation
Since all data sources aren’t compatible in a singular system, data orchestration will be used to transform different pieces of data to make them suitable for the task at hand. This makes the data suitable for multiple applications and tasks.
The data is transformed through a data orchestration tool, thus bringing it to a standard format. For instance, there are various date formats that can be employed in a system. While one system might make use of the DDMMYYYY format, another might employ an MMDDYYYY format, and this step is used to maintain consistency.
3. Automated Enrichment and Stitching
According to the different conditions within the data, it will be combined and categorized. The data organization tool makes the data more advanced and suitable for use once it is organized.
The system also starts performing various tasks during this process, including documentation, reporting, cleanup of duplicated data, and several others. It isn’t enough to categorize the data, but it is more important to generate actionable insights that can be used for the business.
Once the transformed data is categorized and enriched, it can be leveraged for decision-making purposes, after being passed through a data orchestration schema.
According to the criteria, the data can be used to make decisions that can further organize or rank it. Data orchestration also calls for smart decision-making through the use of artificial intelligence models and machine learning.
Data Orchestration Platforms
A data orchestration platform is an application that enables organizations to manage the storage of combined data from various data sources.
- It enables faster operations for big data and artificial intelligence processes.
- It also provides a cost-effective data analysis method by eliminating duplicated data.
- It also enables users to employ new and advanced storage solutions.
Data Orchestration Limitations
Even though data orchestration offers a viable and streamlined solution for aligning data from different sources, there are certain limitations that hinder the process.
1. Platform Compatibility Issues
Data orchestration helps combine and standardize data from different sources and makes it compatible with a certain data processing or analysis tool. However, there are certain problems in this regard, because every platform has different and limited features and capabilities. This means that data orchestration tools will have to be used multiple times to manage data handling across different platforms, and this can be tedious and time-consuming.
2. Lack of Real-Time Data Awareness
Another limitation of data orchestration tools is that there is no real-time or streamlined data awareness, which refers to the visibility of data storage. The root of the problem lies in the fact that there is a massive amount of data that needs to be prepared and categorized, and it continues to grow in size with each passing day.
This concludes our guide on data orchestration and its importance. Implementing data orchestration into any organization results in several benefits, including optimized processes, lower operational costs, maximizing productivity, and also happier employees.
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