In today’s business landscape, data is the foundation for growth and innovation. From customer preferences and market trends to employee productivity and financial performance, trusted data empowers organizations to make smarter decisions, uncover new opportunities, and operate with greater efficiency. As data volumes continue to expand, the ability to orchestrate and activate that data will set successful businesses apart.
Research suggests data will grow from 33 zettabytes (ZB) in 2018 to 175 ZB by 2025. With such growing data demands, your organization must orchestrate its data to make it accessible and reliable.
In this article, we break down data orchestration and why it’s fundamental for your business:
What Is Data Orchestration?
Data orchestration is the automated coordination of data movement, transformation, and integration across systems and workflows. It ensures that the right data is delivered to the right place at the right time, enabling streamlined analytics, faster insights, and operational efficiency.
Businesses use data orchestration to streamline operations, improve efficiency, and gain real-time insights.
As you accumulate widespread data, data orchestration integrates, manages, and makes your data available for analysis. Also, data orchestration eliminates inefficiencies, enhances automation, and supports quick decision-making.
How Does Data Orchestration Work?
Data orchestration works by automating data collection, transformation, and integration across multiple systems. It uses workflows to ensure data consistency, quality, and accessibility.
The process involves extracting data from sources, processing it through pipelines, and delivering it to applications or analytics tools in real time for decision-making.
Data orchestration is a systematic approach to managing and optimizing data flows across different sources and destinations, ensuring the correct data is available at the right time.
The 3 Steps of Data Orchestration
Successful data orchestration follows a structured approach. It ensures data flows efficiently through an organization’s ecosystem.
Here are the three main steps of data orchestration:
1. Organizing Data
The first step involves aggregating and centralizing data from various sources, such as cloud applications, databases, APIs, and third-party platforms. Proper organization ensures that this data is structured, accessible, free from redundancies, and easier to analyze.
2. Transforming Data
Once you have organized the data, you must transform it into a standardized format. This step involves data cleansing, normalization, and enrichment to maintain accuracy and consistency.
Transformation processes include applying business rules, handling missing values, and converting data into a format compatible with analytics and machine learning models.
3. Data Activation
The final step is making the processed data available in analytics, reporting, and business intelligence applications. Activated data integrates into dashboards, predictive models, and decision-making tools, ensuring stakeholders receive actionable insights now.
What Is The Importance Of Data Orchestration?
Data comes from diverse sources like databases, cloud storage, and APIs. Unfortunately, the data is typically ineffective in its raw format, leaving companies with limited data insights.
However, that’s where data orchestration can benefit your business.
For example, it improves data integration and quality, enhances operational efficiency and scalability, and enables real-time processing. It also ensures better decision-making, higher data security, stricter compliance, and robust governance.
Why Enterprises Need Data Orchestration
Data orchestration is essential to optimize your data strategies. It ensures data flows efficiently through different systems by eliminating silos and enhancing accessibility.
With automated data workflows, you can improve operational efficiency, enhance compliance, and gain deeper insights into customer behavior and market trends.
Data orchestration allows businesses to automate and facilitate decisions based on data analysis. It will connect the business’s storage outlets into a single pool so that data analysis tools can easily access and analyze large volumes of data whenever needed.
Enterprises that practice intricate data management have uninterrupted access to both existing and incoming data, making it easier for data analysis tools to process information faster. Data orchestration makes decision-making more efficient and accurate.
Benefits of Data Orchestration
Data orchestration can boost the efficiency levels of your organization. Advantages to this part of data management include reducing costs, being compliant with data privacy laws, and more.
Here are the 5 main benefits of data orchestration.
1. Reduced Costs
Cost reduction is the most appealing trait of data orchestration. Companies aim to minimize their costs while expanding their profits. To do that, companies need to harvest substantial amounts of data from different outlets, which is not an easy task.
The IT experts within your company can lose hours extracting, organizing, and categorizing data for data analysis tools to process the information. Other than being a strenuous task, if done manually, it can cause large error margins, which is why companies prefer to stay on the safe side and practice data management.
Besides shrinking the window for errors, data management will ultimately help your business reduce the compensation bill. Moreover, you won’t have to hire more staff to perform data analysis – the software will carry out the task.
2. Eliminated Data Bottlenecks
Data bottlenecks are the stumbling blocks preventing data flow from coursing through filters and rendering accurate information. Mainly, bottlenecks appear as the result of mishandled data or lack of data-handling capacities, especially in heavy data traffic.
Using data orchestration enables your business to automate the data sorting process while also prepping and organizing data. This leads to less time spent on data harvesting and preparation.
3. Ensured Data Governance
Adhering to data governance standards is an important practice for enterprises. Data management refers to the process of regulating the data used in corporate organizations by using standards and policies.
If your data comes from several different outlets, keeping data flow in check and monitoring the proper processing of data becomes challenging. By using data orchestration tools, your company’s governance plan can easily connect all outlets while simultaneously complying with your data-processing strategy.
4. Improved Data Quality
Effective data orchestration significantly enhances data quality. It achieves this by automating validation rules, reducing duplication, and ensuring consistency across all data sources.
Furthermore, orchestration tools enable end-to-end data lineage tracking to understand and verify the origins of their data.
5. Real-Time Processing Capabilities
Data orchestration lets you process and analyze data rapidly, ensuring you can respond to evolving market conditions, customer demands, and operational challenges.
With real-time streaming capabilities, you can detect trends, anomalies, and critical insights as they occur. This leads to faster decision-making and improved agility, which will benefit industries like finance, healthcare, and e-commerce.
Common Data Orchestration Challenges
Data orchestration is a highly effective way to improve your data management, but that doesn’t mean it’s always effective. There are 5 significant challenges of data orchestration:
1. Ensuring Data Quality
Maintaining data quality is critical for sufficient data orchestration. Inaccurate or incomplete data can cause bad insights and decisions, which damages business operations.
Robust cleansing, validation, and monitoring processes can ensure data accuracy. However, this involves identifying and rectifying errors, removing duplicates, and standardizing formats to satisfy quality standards.
Also, continuous monitoring and governance are essential to upholding data integrity over time.
2. Overcoming Data Silos
Data silos arise when departments, systems, or applications isolate data. These silos cause duplication of efforts, inconsistency in reporting, and missed opportunities for data insights.
That said, overcoming data silos involves breaking down barriers through integration, standardization, and data governance initiatives.
This means implementing centralized data repositories, establishing data-sharing protocols, and promoting a culture of collaboration across the organization.
3. Handling Integration Complexities
Integrating data from diverse sources with different formats, structures, and protocols can be complicated. For example, challenges arise due to incompatible systems, data migration issues, or conflicting data models.
As a result, you should invest in scalable integration solutions to support interoperability and flexibility. This process involves choosing appropriate integration tools, defining clear data integration strategies, and ensuring compatibility with existing infrastructure.
4. Scaling Infrastructure
As data volumes grow exponentially, you must ensure that infrastructure can scale efficiently while maintaining performance, reliability, and cost-effectiveness.
However, scaling requires robust cloud-based architectures, distributed computing, and intelligent resource allocation to prevent bottlenecks.
5. Ensuring Data Quality
Managing data security, privacy, and compliance with global regulations is a complex challenge requiring continuous vigilance. You must implement stringent access controls, encryption techniques, and regular audits to safeguard sensitive information.
Limitations of Cloud Data Warehouse
Storing your enterprise’s data in a cloud warehouse is more than essential for the success of your business. Namely, the warehouse is the main depository for external and internal corporate data sources.
The main use of a data warehouse is to back strategic business decisions by providing data analysis. However, there are a few limitations to using data warehouses, like:
- Non-unified cost scheme;
- Lack of skill options;
- Extended cloud security and governance;
- Movement of data;
- Missing standardization.
Data Orchestration Process/Framework
The term orchestration framework (OF) refers to a tool that automates the data orchestration process. Nowadays, many artificial intelligence programs use data orchestration to deliver clean data.
A configurable framework governed by business rules, the orchestration framework is used to determine the business scenarios in question. It also automates logistics processes and unifies data integration.
Data orchestration relies on advanced tools that automate and streamline data workflows. These tools provide scalable solutions to manage data pipelines and integrations while ensuring seamless data movement across systems.
Data Orchestration vs. Data Visualization
Data visualization refers to the process of visually presenting information using graphics, highlighting schemes, and following data trends. All of the actions help the user gain insight at a faster pace.
Rather than opposing data orchestration, data visualization can help data analysis tools understand different data patterns. Ultimately, such actions will make the data analysis process easier.
Data Orchestration vs. ETL
Data orchestration has more data integration capabilities than ETL. Although ETL focuses on batch processing and consolidation of structured data, data orchestration can handle batch and real-time integration of structured and unstructured data.
Data orchestration also has a broad approach to managing and optimizing data workflows. Unlike traditional ETL processes—which focus on batch processing and transforming structured data—data orchestration involves orchestrating the entire data pipeline. This includes data ingestion, transformation, validation, and delivery.
Furthermore, data orchestrations enable businesses to integrate data from many sources, such as databases, cloud services, APIs, and streaming platforms, in real-time or batch mode.
Additionally, data orchestration often involves automation and workflow management tools to streamline and optimize data processes; this makes them more adaptable to dynamic data environments and growing business needs.
Data Orchestration vs. Data Pipeline
Data pipelines transport large data volumes from one source to another, ensuring a seamless flow of information across systems.
Once the data has traveled through data pipelines, data management works by extracting useful insights from the raw data and preparing it for the next stage.
After that, data orchestration tools organize and segment the prepared data for efficient processing and analysis. This phase involves cleansing, transforming, and enriching the raw data to make it valuable.
Together, these stages ensure data moves smoothly from collection to actionable insights, which is essential for your business.
Data Orchestration vs Data Automation
Data orchestration and data automation have different functions within the data processing ecosystem.
For example, data orchestration manages the complex workflows and dependencies required to move and process data through various stages and systems. As a result, this ensures tasks are executed in the correct order and at the right time.
In contrast, data automation streamlines individual tasks within this workflow, automating repetitive and manual processes such as data extraction, transformation, and loading.
Together, orchestration ensures smooth and efficient data flow across the entire pipeline, whereas automation enhances efficiency by handling specific tasks more quickly and reliably.
Data Orchestration Tools
Tools or data orchestration platforms for data management allow businesses to automate their data-driven processes. Strategic and targeted audience decision-making are some of the crucial actions a business can take to accelerate revenue or reach a broader audience.
By using data orchestration tools or platforms, all of your data will be harvested, categorized, merged, when and if needed, and prepared for analysis. Moreover, data orchestration platforms are suitable for machine learning, too.
Practicing data orchestration for your enterprise can be the most cost-efficient business transaction you make. Furthermore, it helps you make more accurate and result-driven business decisions.
Managing and orchestrating your data can be complex and can really hinder your work if not done right. See how Boomi Enterprise Platform can help manage data integration and orchestration with our demo.