What is data orchestration? definition, key stages, automation and best practices

In today’s corporate world, everything relies on data. Organizations shouldn’t expect to grow if they aren’t using data to influence key business decisions. Companies have to collect myriads of customer data to drive their business principles, causing a proliferation of data-driven applications in the data technology space. 

And it’s growing fast. Data generation is expected to increase by more than  10 times that of 2016, growing from 33 Zettabytes (ZB) in 2018 to 175 ZB by 2025, according to an Forbes. Manually dealing with several data sources and data types will increase the propensity of human errors, leading to grave consequences.

Using data orchestration tools will help to clean, sort, arrange and publish your data into a data store. The aim of this technique is for authoring, scheduling and monitoring data pipelines by accepting data from numerous storage places.

What is data orchestration?

Data orchestration is defined as the act of collecting and organizing siloed data from numerous data storage points and making it accessible and prepared for processing by data analysis tools. Just as data is essential to understanding and refining, business processes, it is also essential for decision makers to understand data orchestration, the key stages involved in the orchestration process, why organizations need data orchestration as well as best practices for its implementation.

Data orchestration software is novel game-changers in the data technology environment that helps to break down data silos at a faster rate by aggregating storage systems. 

Data storage systems for a business can be numerous, creating difficulties for the data analysis tools to access when it’s required to do so. The various storage systems are integrated by data orchestration software, allowing for seamless accessibility for the analysis tools.

Key stages of data orchestration based on technology maturity

Data orchestration helps to make meaning from your data stack by aggregating sources, eliminating ambiguity in the analysis process. Achieving this involves a process.  The key stages of data orchestration include;

• Organization

The first stage is to organize the data. In doing this, all data that your organization has in its storage platforms are important to the process — including pre-existing data and the most recent data collected. 

Data orchestration tools explore your business’ cloud-storage platforms for data, perusing obsolete computing hardware and software in your organization that’s still in use in your data stores or warehouses.

To understand the types of data that exist in your organization and create an association to their source, the data orchestration tool must get all business data from wherever it may have been stored. This will help to sort and organize the data for analysis. 

• Transformation

The data that has been sorted and organized is taken to the transformation stage. Here, the goal is to make data in a singular format that can accelerate the rate of data analysis. In an ideal situation, business data can be in varying formats and still be distinguishable. 

For example, a date can be taken in more than one format (DD/MM/YY & MM/DD/YY) on the organization system. It can be taxing and time-consuming to sort this kind of data manually, leading to errors that can affect the data analysis.

Different forms of a single data are transformed into one format for easy representation and uniformity, improving efficacy and the return rate of analysis. 

• Activation

Activation is the availability of data to the tools that will use the data. It’s the most important stage of the orchestration process. The end of every data orchestration process is to make coherent data available for business’ everyday use. Activation ensures that data loading isn’t necessary because the data you need is already available when you need it. 

Data orchestration quickens the data analysis process in real-time. When data is activated this way, it can be examined immediately as it’s been processed. Imagine your organization that’s large and collects millions of data every second, this will be an advantage and of interest to you.

Why your organization needs data orchestration

Saying that data orchestration can improve the efficiency of your organization might sound cliché. However, this is why your organization needs orchestration;

• Cost reduction

Every organization’s goal is to reduce costs while maximizing profit. Sorting data from various warehouses can be time-consuming and exhausting, translating to work hours for IT professionals in your organization. Over time, this limits the efficiency of teams and impacts your business wage bill. 

Data orchestration reduces the cost of paying teams to process data manually. The cost saved can be channeled into innovative pursuits that will advance your business. 

• Data privacy laws compliance

The function of data orchestration in organizing data sets that your organization collects helps businesses stay in conformity to data privacy laws like the GDPR and CCPA. The legislation protects customer data collected by organizations and mandates that data collection times, location and the reason for data collection are specified.

It’s easy for your business to run into troubles or contravene available data privacy laws if your data sets are not organized. Failure to establish ethical gathering of data has huge consequences for your organization. 

For instance, your data collection can be halted and previous data can be deleted from your data stores/systems. 

How do you fulfill these obligations if you have no idea where specific data is sourced and stored in the first place? 

Data orchestration provides insight into where all datasets are stored and from what sources they emanate. This information becomes an advantage if you need to delete customer data on request in compliance with a GDPR sanction, helping you to save 4% of your global revenue.

• Ensuring data governance

Your organization must have clear policies and standards that guide the use of data acquired. Data governance is concerned with regulating data use in corporate organizations by setting up standards and policies to manage data consumption. 

When your data pipeline is stretched across several platforms, data governance becomes challenging. It’s easy for a data orchestration tool to enforce a governance plan because it integrates all platforms.

Data orchestration can also stick to your bespoke data strategy framework. This ensures that collected data aligns with your tracking plan. Orchestration tools quarantine sources that don’t match your plan until you verify such data sources and can figure out how anything infiltrated  your tracking strategy.

Adherence to your organization’s data governance also ensures the retention of trust in your data byaiding the improvement of data analytics. 

• Removing data bottlenecks

A data bottleneck is a point where the flow of data in your organization is broken or halted. This is typically due to lower data handling capacity coupled with heavy traffic requests. 

According to a recent survey by Forbes, 80% of the efforts associated with data analysis are spent gathering data and then further processing it, which can cause bottlenecks. 

Utilizing data orchestration automates your business’ data sorting, preparation and organization — considerably minimizing time spent on data acquisition and preparation.

Data orchestration tools, allow you to  delve into data analysis since the, complex data is transformed into workable formats with orchestration.

Data management challenges solved by orchestration and automation

Many activities increase as the customer base increases, making it challenging to cope with the high volume of data. Here are three major challenges that your organization can solve using data orchestration;

• Manual data administration

Manual data management by your organization’s data IT professionals isn’t the way to go if you want efficient data management. This becomes cumbersome as the business grows because demands and customer base increase as does the volume of data..

If you’re dependent on code, manually writing thecode is challenged by bugs and lags from regular freezes, which may make it difficult for your team to keep up with the influx of increasing real-time data. 

Data orchestration tools automate the processes and take the large data burdens off your technicians’ hands.

• Poor results from quick decision-making

Data-driven decision-making can be hampered if your organization isn’t proactive. Organizations must be aware immediately when issues spring up and then take the appropriate next steps without jeopardizing activities by late decision-making. 

The automation and integration of new data in real-time by these orchestration tools prepare and make new data available for analysis. This proactive decision-making can be the decider in establishing products’ dominance and capturing new markets among competitive businesses.

According to McKinsey, quick decision-making strongly correlates with good decisions. Imagine making  fast decisions that are data-driven and  backed up by faster accessibility and quick analysis from data orchestration. You can expect better results than rapid decision-making without utilizing orchestration.

• Scattered and siloed data

The inability to keep up with large data volumes has led many organizations to leave their data scattered in silos. It can be difficult to migrate siloed data between locations because organizations have trouble grasping the conclusions from the data.

Despite its constant use, 97% of business executives that use data silos believe that they have negative impacts. Data silos waste time and increase overhead costs, jeopardizing the decision-making process. 

Data orchestration tackles this problem by permitting the combination of new sources of data with data silos that have been in existence. Connectors that are pre-installed and low-code API adapters provide the needed access, solving the data silos problem efficiently.

Top10 best practices for data orchestration in 2022

Data orchestration comprises different processes. You must align with global best practices in data management and data orchestration to make the best decisions from your data. Here are 10 best practices that guarantee efficient data orchestration;

• Select the right tools

The data technology space provides various options, and data orchestration tools have different capabilities and follow different processes in their functionalities.

Look for tools that offer flexibility, are extensible and are scalable. Consider robust integration with dependable cloud services like Microsoft Azure, Amazon Web Services (AWS), Oracle Cloud, IBM Cloud and Google Cloud.  

Identify data orchestration platforms that are user-friendly, allow directed acyclic graph (DAG) implementation and some with hybrid execution for confidential data protection. The appropriate data orchestration tools will provide efficient data management, needed functionalities and flexibility, making your data more understandable and prepared for analysis. 

• Have data experts in your team

Ensure your organization has a team of data experts. While there are data orchestration platforms that offer enough flexibility for staff with little or no knowledge of data orchestration, having a data expert is pertinent to valuing data orchestration.

Data experts monitor the data throughout its life cycle andprovide insights to your organization on how best to operationalize the orchestrated data with emerging use cases, to generate the best marketing use decisions.

Expertise is also needed to decide on the right data orchestration platforms and capabilities specific to your business data objectives. A data expert will help to track data issues, troubleshoot and recommend solutions.

• Integrate data and update systems 

Updating your old data architectures should be a practice that decision-makers  prioritize in 2022. For data orchestration, limit siloed data and the interference of legacy systems. Older technologies of legacy data stores don’t support newer technologies and impede the data orchestration process.

Integrate scattered data to enjoy the benefits of data management. Consider using cloud computing and other novel technologies. Upgrading your systems to be new-technology proficient ensures easy inflow of new data sources that are relevant to your organization, increase optimization and help your data orchestration process.

• Be observant 

The data orchestration environment is dynamic. Being observant helps to counter a key data orchestration challenge. 

Detecting errors in data orchestration can be challenging because it can easily go unnoticed unless you’re observant. Teams must be watchful, observing metrics that show data issues. If no one is being notified, orchestration has failed. 

Use cases in data orchestration including SRE, Analytics, devops require observance to detect data errors and repair them promptly.

• Think data security

Data orchestration allows you to get data into various systems so they can do their jobs. However, the different systems may not all have the same security needs. The systems can be located throughout the network, accessible to a variety of users and have varying levels of protection. 

It’s vital to ensure that the data is safe, regardless of where it is stored. Dynamic access control must be planned into the security management of your organization for data orchestration. Consider both the security of your network and that of the data. Data security breaches are rampant and increasing in frequency, so businesses must secure customer data at all costs. 

• Incorporate artificial intelligence (AI)

In 2022, you must tend your organization towards AI-powered systems adoption, even in data orchestration. AI is gaining ground in the data management space, despite being a newer field for its integration. 

Select orchestration tools that deploy AI in managing your organization’s data. This  allows for efficient orchestration, data organization and monitoring, as well as reducing dependencies on human intervention. According to Mike Ferguson,managing director of intelligent business strategies, prediction, automation and optimization are three capabilities of AI to data management and analysis. 

• Interconnect your data ecosystem

Because of the high volume of data organizations have to process, connecting to and building cloud ecosystems for data management and storage is a best practice for organizations in  2022. A cloud-based ecosystem gives an inter-connectedness of data and increases data accessibility. 

Connecting systems manually for a robust data channel can be difficult. Businesses frequently have to move large data sets around systems for specific business processes and analysis, which can lead to data replication and damage from human error. Make conscious efforts to interconnect your data storage systems.

• Practice documentation 

Documentation is also a best practice for data orchestration. It’s important to capture processes in a detailed manner. This helps your team to establish confidence in solving issues that may arise in the data orchestration process.

Document what to do, how to do it and why you’re doing it the way you’ve specified. Specific data manipulations, aggregation of storage systems, network security protocols, systems upgrade status and data orchestration procedures should be included in the documentation.   

Create different levels of documentation for the entire data management process. 

• Develop a data culture 

Defining a data best practices should also include any data orchestration objectives the organization has. Data culture helps to direct how data is woven into business operations, influences teams’ behavior to implement best practices and emphasizes data-driven decision-making.

An IDC report shows that the development of data culture helps businesses realize the inestimable value of their data. Essentially, data governance gets its influence from data culture and it’s connected to how the organization and dedicated teams  perform a thorough data orchestration. 

 Overall, data culture will determine the importance of  data orchestration and the sophistication of tools employed.

• Commit to team training

Data orchestration is dynamic, with novel storage pipelines, multifunctional tools bringing new real-time integration techniques and capabilities. 

Consistent training will ensure that teams stay updated about the technical knowledge of data orchestration tools. Teams should be trained to remain competent in handling process clusters, various data centers, storage infrastructure challenges, new technologies and their complexities. Strengthen the team’s competence by committing to their training.

Data rules the corporate world today. Organizations have to collect heaps of customer data to drive their business principles, causing a proliferation of data-driven applications in the data technology market. This data can be in multiple storage pipelines and can be hard to track – this is where data orchestration comes in.

Data orchestration organizes data faster – reducing human error and cost, ensuring compliance with  data privacy laws and guaranteeing data governance while managing challenges that include the manual administration of both scattered and siloed data

These best practices will lead to effective harnessing of data orchestration in your organization, which ultimately drives business growth.