What is data orchestration?

Why is data orchestration important?

There is simply too much important data for a modern business to manage casually, manually or ad hoc.

In decades past, a typical business maintained traditional data -- documents, images and customer data. Network access and storage capacity were limited. Security and compliance obligations were light, if they existed at all. Employees had a clear picture of data and its stored location, such as drive volumes or folders. Data quality and integrity had little noticeable business impact on competitiveness, governance and profitability.

All of that has changed.

A modern business's lifeblood is the vast quantity of data it possesses and uses from varied sources, including customer web portals, transaction records and feedback surveys. Fleets of IoT devices gather real-time data. Data brokers, web scraping, data aggregation and even burgeoning public records provide valuable business data. A company extensively analyzes this seemingly unrelated data, seeking foresight into new competitive opportunities, forecasting needs and performance, and creating ever-more powerful business systems such as ML and AI platforms.

Data is the key to all of this, particularly its quality and organization. Fragmented, outdated or invalid data, as well as data with questionable provenance, leads to inaccurate analysis, biased or improperly trained ML models, poor customer interactions and undesirable business outcomes.

Yet even the most talented IT staff can't possibly know what -- or how much -- data is available, how that data all relates or the resources needed to ensure that ever-rising ocean of data is complete, accurate and timely. Data orchestration delivers well-organized data to business systems when it's needed, seamlessly managing the flow of data and its organization, validation, use and governance.

How does data orchestration work?

Data orchestration features clear and systematic workflows, called data pipelines, designed and automated to collect, integrate, transform and deliver data as needed to analytics, ML tools or other business applications. Data orchestration tools and platforms schedule and automate all these tasks, recognizing the required data dependencies and their precise delivery timing to the proper application. Consequently, a data orchestration platform requires several well-defined steps, such as the following:

• Data collection or ingestion. The ingestion process gathers data from various common sources -- data warehouses, database systems, cloud-based storage and third-party sources such as government data or commercial data providers -- to ensure all data intended for orchestration resides locally.
• Data integration or normalization. The ingestion process, rarely smooth or seamless, requires integration of multiple data sources with differing elements or data components. For example, data in one database presents one or more columns the data in a second database does not possess. The normalization, or integration, process assembles ingested data in a collective fashion, even though some data elements are missing or incomplete. Proper data integration reduces duplication, simplifies queries and establishes data consistency.
• Data cleansing or scrubbing. Integrated data must be reviewed and cleansed or scrubbed. Data cleansing is a key part of the data transformation process -- and central to any data quality effort -- and handles erroneous, incomplete, duplicated or inconsistent collected data. Data scrubbing requires some preprocessing to identify and fix non-compliant data elements. For example, scientists ensure all temperature measurements are in degrees Celsius rather than degrees Fahrenheit. Although automation is the goal, data orchestration workflows sometimes require manual intervention to address certain data quality issues.
• Data enrichment. Data enrichment, another data quality process, supplements collected data with new information that provides more well-rounded, accurate and valuable business data, such as tagging data. Enrichment gives organizations deeper insight from data collection to make better-informed decisions.
• Data delivery or activation. Once properly prepared and organized, collected data is parsed and delivered to various business applications, including analytics, ML training, reporting and BI tools. Delivery, or activation, focuses on gleaning insights to improve business results.
• Data monitoring and management. Even the best automated processes demand careful oversight. Data orchestration tools provide comprehensive process monitoring and management features. Administrators track process status, see and understand process errors and intervene manually to correct process errors. Monitoring and management ensure data orchestration workflows run properly.

Benefits of data orchestration

Modern organizations depend on a high volume of accurate data delivered in a timely manner. Common benefits of data orchestration include:

• Cost savings. Manual data management is a laborious and error-prone task requiring long hours from data scientists and data engineers. The automation of data extraction, organization, categorization, tagging and other rote data tasks not only saves time and reduces human error, it frees talented professionals to work on more strategic analyses and optimizations for the data engineering team.
• Better data quality. An old IT axiom garbage in, garbage out is particularly applicable to data management tasks. Manually organizing and reviewing enormous volumes of data sometimes overlooks outdated or incorrect data. Human error translates into poor analytics, incomplete ML training and other sub-optimal outcomes. On the other hand, data orchestration's consistent rules automate data validation, deduplicate data and ensure data consistency from all sources integrated in the collection.
• Improved data governance. Proper data governance is almost impossible through manual processes. However, data orchestration tools introduce business rules to ensure the acquisition, processing, protection and application of data fulfill current data management strategies and regulatory obligations.
• Flexible and timely outcomes. Manual data handling requires significant time investments, delaying data analytics and its accompanying insights. Data orchestration processes data much faster -- often in real time -- and businesses find trends, identify problems and obtain analyses or insights far sooner than with manual efforts. Ultimately, data orchestration enhances enterprise agility and business competitiveness.

Challenges of data orchestration

Although its benefits are compelling, there is no guarantee that a data orchestration initiative will succeed. Among its challenges are the following:

• Data quality concerns. Poor data causes poor outcomes, underscoring data quality's critical role in any data management effort. While data orchestration automates many mundane and error-prone data quality tasks, automation alone does not ensure quality data or proper governance. The data team must introduce and enforce the proper rules, identify errors, remove duplicate data, standardize formats and check other data quality characteristics. Indeed, the onus remains on the data team to ensure data orchestration steps impact the data as intended.
• Data silos. A central premise of data orchestration is collecting and centralizing data, demanding interdepartmental cooperation and collaboration, as well as good integration between systems and applications. Omitted data sometimes results in missed insights or business opportunities. Any data orchestration design must work to eliminate data silos through standard formats, solid integration and clear, consistent data governance policies.
• Complex integrations. Systems and applications do not guarantee interoperable data. Different formats, structures and protocols result in integration problems and data orchestration failures. A careful integration assessment must precede any data orchestration initiative. Additional integration tools further support smooth data exchanges.
• Scalability. Data orchestration requires infrastructure: storage, network and computing capacity. Consider the volume of collected and processed data now and in the future. Assess the infrastructure needed to maintain storage reliability and network and compute performance. Consider cost constraints. Identify potential bottlenecks and evaluate possible solutions, such as cloud-based architectures or distributed computing.
• Security and compliance. An organized and well-prepared centralized data repository presents a particularly attractive target to attackers. This threat requires attention to data security and privacy, data access authentication and authorization, data retention and protection policies, as well as compliance with regulations such as the California Consumer Privacy Act and General Data Protection Regulation.

Popular data orchestration tools

Data orchestration depends on software tools or platforms. It's software that lets organizations access data sources, define workflows, set rules and build, transform and assign destinations for data -- all with a high level of automation.

However, no two tools are alike, leaving business leaders to weigh the organization's specific data orchestration capabilities, needs and goals before committing to one tool over another. Common considerations include proprietary vs. open source, local vs. cloud, managed or not, flexibility vs. scalability, available integrations and testing and management capabilities. An online search of current data orchestration tools includes the following results:

• Apache Airflow.
• Apache NiFi.
• Argo.
• Astronomer.
• Atlan Luigi.
• AWS Glue.
• AWS Step Functions.
• Azure Data Factory.
• BMC Control-M.
• Dagster.
• Databricks.
• Flyte.
• Google Cloud Composer (Managed Airflow).
• Google Workflows.
• Keboola.
• Kestra.
• Mage AI.
• Metaflow.
• Prefect.
• Rivery.
• Shipyard.

Where is data orchestration headed?

Already, data orchestration technologies and tools provide a range of expanded capabilities for data-centric businesses, while simplifying deployment and use for data scientists and other professionals.

Expect future data orchestration tools to couple more closely with AI capabilities. As AI learns usage patterns and gleans context, it delivers even faster data pipeline creation, plus a more robust and resilient operation capable of responding to unexpected problems and making pipeline changes in real time.

Integration is a cornerstone of data orchestration, giving its tools ready access to data in vastly different formats from a growing array of platforms and systems, including extract, transform and load, or ETL, tools, data lakes and data warehouses, as well as ever-expanding ML and AI platforms. For example, organizations can expect future data orchestration tools to offer direct compatibility with commonly used ML training systems, greatly speeding and simplifying data management. A composable data system, connecting more diverse systems while handling a wider assortment of data types and sources, becomes an increasingly attractive, flexible integration goal.

The cloud also plays a larger role in future data orchestration. Public cloud providers already offer data orchestration as a service, as well as software as a service. But data orchestration services are coupled well with cloud storage, computing, database, event-driven computing and other services already well represented in the cloud, making it a natural fit for data orchestration services. Moreover, the cloud alleviates significant software deployment and maintenance headaches for businesses.

Finally, expect data orchestration tools to support a wider range of data-related practices, such as DataOps and data mesh approaches. DataOps, combining data and operations, provides faster and more robust data workflow creation. Data mesh, meanwhile, decentralizes data ownership while maintaining control over data management and automation.