What is data orchestration and how does it help deliver insights that drive organizational efficiency and growth
The roadmap for implementing data orchestration and best practices
Combining data orchestration with machine learning and AI to scale its impact
WHAT IS DATA ORCHESTRATION?
According to a 2018 survey by Gartner, 87% of organizations have low business intelligence and analytics maturity. This is not due to a lack of data but rather an inability to unlock the true potential of big data, and the five most common reasons for this challenge include:
Difficulties in locating data
Data being trapped in silos
Outdated data or inaccuracies
Insufficient data security and protection
A lack of proficient data analytic resources
To drive actionable outcomes with data, you need to first ensure access by removing silos and implementing a standardized process for data scrubbing before utilizing analytic tools to extract granular insights. This process is known as data orchestration and leverages automation to facilitate analysis.
Today, data orchestration tools are an integral part of a company’s tech stack and are essential for gleaning business insights that help drive organizational growth and efficiency.
Systemization: This is the process of enabling data engineers to locate, comprehend, and manage existing and incoming data. In most organizations, data is often scattered across legacy systems, cloud-based applications, geographically dispersed data warehouses, silos, and data lakes. Data orchestration tools can quickly access, comprehend, and systemize these data pools.
Unification: The second step is to convert the data into a standardized format for analytics tools. Data variables that exist across multiple formats and data pools can be unified through conversion into one standardized format to facilitate organization and analysis.
Activation: The final step of data orchestration is providing analytics tools instant access to the systemized and unified data in real time. This empowers organizations with the data they need daily on demand without the lag time taken for data loading.
Data orchestration tools automate all three steps and facilitate analysis to provide rich data insights.
Data orchestration eliminates the problem of fragmented and low-quality data without requiring extensive migrations or creating an additional storage location that could quickly become another data silo.
A single source of truth: Data becomes more accessible, shareable, and easier to update. A single source of truth ensures more accurate insights and decisions.
Increases operational efficiency: Improved data accuracy and integrity help reduce operating costs by enabling greater automation, saving time and money.
Real-time insights: Automated data orchestration eliminates bottlenecks in accessing high-quality data, enabling analysts to extract and analyze data in real time.
Facilitates organizational scaling: As a company scales, the volume of data increases exponentially. Data orchestration enables your data team to access, prepare, and manage data pipelines at scale.
In short, data orchestration helps companies access data from fragmented data pipelines to determine rich insights for driving agile decision-making.
Collecting and organizing data: The first step is to identify all the data pipelines to gain access to existing and incoming data. The data orchestration tools use different software development kits (SDKs)and application programming interfaces (APIs) to locate and collect data from the data pipelines. This includes client touchpoints, cloud storage, apps, and other data pipelines. Once the data is accessed and collected, it undergoes an organization process where it is labeled and checked for integrity before moving it to the next stage.
Data transformation: The transformation process standardizes the data into a format or language that analytics tools understand. The same data can be generated in different formats in multiple pipelines. For instance, data for the date can take different formats. The date in one system can be formatted as May 24, 2022, while another might format it as 05/24/2022 and yet another as 24/05/22. Now spread this across the thousands of data types for an idea on the scale of the problem. Trying to sort this manually is tedious and inefficient. Orchestration tools automate the transformation and standardization while streamlining task management.
Data unification: Once the data is internally compatible, it needs to be unified into a master dataset for a unified view. For B2C companies, this means a unified view of individual customers, while for B2B businesses, it gives you an end-to-end view of each account with its leads and prospects. The unification helps define source fields, eliminate duplicate data, and matching conditions to link the data to tables and fields.
Data activation: Activation is the process of making the orchestrated data available to your analytic and BI tools. Since data orchestration eliminates the process of data loading, users can access the data on a platform of their choice in real time. Data orchestration also eliminates dark data (data that is not visible or accessible).
At most companies, data engineers spend the bulk of their time manually managing data, and data orchestration enables them to devote more time to high-value tasks like analyzing and utilizing the data.
Data orchestration combines two processes, data orchestration, and data synchronization. While both processes access, clean, and standardize data from multiple sources, their primary purpose differs.
Data integration focuses on creating a unified view of data, while data synchronization is focused on keeping data consistent across multiple systems.
Depending on business use cases, data synchronization is most commonly implemented in real time. However, synchronization can also be achieved in near real-time. Real-time synchronization updates the data as soon as it is changed or there is an update, while near real-time synchronization updates data at periodic intervals. On the other hand, data integration typically involves batch processing and updating data regularly.
Automating data pipelines can drive efficiency, including the ability of data engineers to focus on reassigning at least 15–20 percent of data engineers to high-value tasks like analysis, presentation, and handling big data projects. Switching from managing data pipelines (with manual scripting and point automation tools) to automated workflow management tools reduces complexity, eliminates coding errors, and shortens development time.
Integrating automation in data management workflows gives teams a holistic view and an end-to-end view of pipelines. By consolidating process steps, workflows can be streamlined and reduced in number. Finally, automation improves SLA performance because now teams can identify and resolve root-cause errors and process failures within the existing deadlines.
ETL is an acronym for the process of extracting, transforming, and loading. In the context of data management, most companies achieve this for their data pipelines by building ETL processes around them. ETL transformations are custom-coded by data engineers, because each data source and type will need specific implementation protocols relevant to its formats and schemas.
API integration is the link between different applications that enables them to communicate and seamlessly exchange data from different applications and systems. API integration therefore plays a critical role in empowering data synchronization to deliver insights that can enhance revenue and productivity. Most companies today use multiple API gateway providers to address their diverse integration needs.
Following best practices in data management is essential to achieving the best outcomes from data orchestration.
While data orchestration tools deliver similar goals, it is essential to note that each has different capabilities, follows different process maps, and offers varying functionalities. Selecting the tool that best aligns with your company’s needs and workflow processes is therefore critical.
Start by looking at tools that offer flexibility, scalability, and have extensive features. For example, the ongoing trend of migrating data systems to the cloud makes it imperative for orchestration tools to provide effective integration with dependable cloud services such as Google Cloud, Microsoft Azure, Oracle Cloud, Amazon Web Services (AWS), and IBM Cloud.
Next, ensure that the data orchestration platform is user-friendly and allows directed acyclic graph (DAG) implementation and hybrid execution for protecting your confidential data. Ensuring that the data orchestration platform delivers these basic functionalities will make your data more usable for analysis and insights.
Data governance strategies focus on effectively managing daily tasks to ensure that the data in your pipelines and systems is usable, protected, and understandable. This consists of planning and defining the holistic requirements of managing your data consistently.
Some basic elements of data governance include:
Assigning responsibility to specific team members for implementing policies
Clearly defining policies for processing and sharing data
Establishing policies for how you name and store data
Determining the metrics for keeping your data clean and usable
Your data governance strategy should build a framework for connecting people to technology and processes. This should include assigning responsibilities of critical tasks and accountability of relevant data domains to specific team members. Your data governance policy should ensure data integrity by keeping data accurate, clean, and usable with well-defined procedures and documentation policies for collecting and managing data.
Developing a data culture at your company is an integral part of effective data management and starts by defining objectives. Developing a data culture also helps direct how big data is weaved into your business operations, and how teams implement best practices while achieving data-driven decision making across the organization.
An IDC report highlights the role played by a robust data culture in helping businesses realize true data governance and the untapped value of data. Data culture also helps to connect organizational goals and teams while implementing data orchestration, improving its effectiveness.
With so many data orchestration tools available today, choosing the one best suited to your needs becomes difficult.
Keboola is an end-to-end and fully managed data platform offered as a service. Keboola also integrates storage, ELT, orchestration, and analysis into one platform.
Pros:
Keboola provides extensive integration coverage with over 250+ pre-built connectors for automating data extraction and loading with a few clicks. You can use the Generic Extractor or Generic Writer to build your data pipelines for data sources or destinations that are not covered.
Customize your orchestration triggers with Keboola’s orchestration engine and choose between real-time updates, scheduling by date and time, or fully manual triggers.
The low-code or no-code feature makes the tool accessible to data managers and business experts. Developers can choose between languages like Python, R, Julia, and SQL. At the same time, the no-coding business experts can quickly create data pipeline orchestrations with features like the Visual Flow Builder and the no-code transformations.
Cons:
Some error messages can be hard to understand; however, Keboola is aware of the issue and is beta-testing an AI solution that converts errors into human-readable ones.
Pricing:
The freemium plan gives you 60 free computing minutes per month to run your pipelines; after that, the charge is $0.14 per minute.
G2 Rating
Google Cloud Functions (GCF) is a cloud-based orchestration environment for scaling and connecting cloud services. Cloud Functions lets you write simple, single-purpose event-based functions from your cloud infrastructure and services. Your function automatically gets triggered when the event being watched is fired.
Pros:
GCF offers multiple runtimes across popular languages like Java, Go, Python, and PHP.
Cloud Functions are oriented for granular and single-task operations, making them easy to manage, debug, and control.
Cloud functions offer real-time and scalable solutions. The GCP infrastructure makes it easy to run parallel commands and provides easy scalability.
Cons:
Lacks a no-code feature. The product targets developers; hence, the UI does not feature a drag-and-drop option.
Cloud Functions are great for running small tasks, and daisy-chaining them to handle complex data pipelines with multiple components is challenging. A big drawback is the lack of visualization to assist you while trying to integrate multiple cloud pipelines into a single end-to-end data workflow.
Pricing:
Cloud Functions give you 2 million free invocations every month, and after that, you get billed at the rate of $0.40 per million invocations. There are additional nominal charges for computing runtime and networking costs. For more details, check the GCP’s pricing calculator for details.
G2 Rating
Prefect is an open-source data orchestration platform that enables you to orchestrate and run complex data pipelines across different data systems, including data lakes, data warehouses, and machine learning models.
Pros:
Prefect’s in-built parametrization functions enable you to create and build dynamic workflows.
Prefect offers easy scalability with parallelized execution using Kubernetes.
With Prefect, you can custom-define events that trigger your data workflow orchestrations, thus enabling you to build real-time data flows.
Cons:
Python is the only language you can use to code and design data flows. Since no other language is available, this can create performance issues for memory-intensive tasks.
The free version of Prefect has limited to no observability. However, the paid version has a rich and comprehensive suite of features.
Pricing:
Prefect offers two pricing tiers: the free, open-source edition with limited features and visibility and the paid cloud version. The paid version starts at $450 monthly, and costs rise as you add functionalities.
G2 Rating
Data orchestration is a fast-evolving technology with questions concerning its future entering in popular debateIndeed, the question now for many is how to get a high-level view of the collected and stored data.
Several trends support this evolution:
A shift toward thinking about data as a product with data-aware pipelines that know about the inner life of a task
The rising trend of creating declarative and data-aware pipelines
The use of abstractions for reusing code within a complex multi-cloud environment
Python steadily becoming the primary language for functional data engineering
The focus is shifting from data pipelines to data products and using declarative code that enables traditional data pipelines to update themselves when upstream data assets change. Using abstractions in big and open-source data helps pipelines become more data-aware and better aligned with the data mesh mission.
The impact of data orchestration on business intelligence
Data orchestration enables organizations to create end-to-end data management processes to help business teams quickly access insights for decision-making. Data orchestration improves the business intelligence posture of companies with benefits that lead to better alignment between sales and marketing, greater accuracy in forecasting, and reduced costs. Here are some more BI benefits of adopting data orchestration:
Greater accuracy in client profiling with accurate segmentation
Better capitalization of the total accessible market (TAM) by identifying growth opportunities and accessing new markets with custom-tailored go-to-market (GTM) strategies
Greater ROI attribution with high-quality data and automated workflows
Leveraging data orchestration for machine learning and AI
AI and machine learning, coupled with data automation and orchestration tools, help to empower optimized and cheaper processes. When paired with automation and orchestration tools, it helps cut costs by automating high-volume and low-value tasks. It also provides real-time and granular insights that enhance business value.
By orchestrating human-in-the-loop (HITL) automation, you can make chatbots more human-like and improve knowledge management across the company, and by using machine learning, you can catch performance degradation while improving root-cause analysis.
The volume and complexity of data is scaling at a rapid pace. Without data orchestration, you cannot tap into the full potential of your data, and leverage valuable data insights, impacting growth and, ultimately, the outcomes you are able to achieve.
Considering the ease of data collection and difficulties of effective data analysis, data orchestration is a critical strategy to transform big data into insights that can guide decision making.
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Complexity: Orchestration processes are complex even when you use the latest technologies. Implementation requires a team of well-trained data engineers.
Heterogeneous architectures: Navigating the myriad storage and computing infrastructures across different database platforms and cloud configurations is challenging.
Automating data cleaning and stitching: To gain an omnichannel view of data requires accurate cleaning and stitching of data from different locations and systems, which have their own limitations and configurations.
How does data orchestration contribute to data privacy and security?
Security and data privacy are woven into data orchestration. All security solutions are integrated into a single system for streamlined management. This includes security solutions working together to increase the efficiency of each component.
Are there any open-source data orchestration tools available?
There are many open-source data orchestration tools available today. Here are five popular open-source orchestration tools:
