In today’s data-driven world, organizations are faced with an overwhelming amount of data, yet many struggle to harness its true potential. Despite the abundance of data, a significant portion of organizations face challenges in effectively utilizing it for business intelligence and analytics. According to a 2018 survey by Gartner, a staggering 87% of organizations suffer from low business intelligence and analytics maturity.
This isn’t due to a shortage of data but rather a failure to effectively orchestrate and utilize it. In this blog, we’ll explore the concept of data orchestration and how it can help organizations overcome common data challenges and achieve harmony in their data processes.
Understanding Data Orchestration
Data orchestration is the process of systematically managing, unifying, and activating data from various sources within an organization. It addresses several common challenges that organizations face when dealing with data:
- Difficulties in Locating Data: Data is often scattered across various systems, making it challenging to locate and access.
- Data Silos: Data tends to get trapped in silos, where different departments or systems don’t communicate effectively with each other.
- Outdated or Inaccurate Data: Inaccurate or outdated data can lead to incorrect insights and decisions.
- Data Security Concerns: Ensuring data security and protection is crucial, especially with the increasing threat of data breaches.
- Lack of Data Analytics Expertise: Many organizations lack proficient data analytics resources to derive meaningful insights from their data.
To overcome these challenges, data orchestration involves a three-step process:
Systemization:This step involves enabling data engineers to locate, comprehend, and manage existing and incoming data. Data orchestration tools can access and systemize data from various sources, including legacy systems, cloud-based applications, data warehouses, and data lakes.
Unification:Once data is accessed, it needs to be converted into a standardized format that can be easily understood by analytics tools. This step involves transforming data variables into a consistent format for better organization and analysis.
Activation:The final step is making the systemized and unified data available to analytics tools in real-time. This ensures that organizations can access the data they need when they need it without delays caused by data loading.
By automating these three steps, data orchestration tools streamline the data management process, making it easier for organizations to derive valuable insights from their data.
The Benefits of Data Orchestration
Implementing data orchestration offers several benefits for organizations:
A Single Source of Truth:
Data orchestration ensures that organizations have a single, reliable source of data that is accessible, shareable, and easy to update. This leads to more accurate insights and better decision-making.
Increased Operational Efficiency:
Improved data accuracy and integrity reduce operating costs by enabling greater automation, saving time and money.
Real-time Insights:
Automated data orchestration eliminates bottlenecks in accessing high-quality data, allowing analysts to extract and analyze data in real time.
Scalability:
As organizations scale, the volume of data increases significantly. Data orchestration enables data teams to access, prepare, and manage data pipelines at scale.
Data orchestration helps organizations overcome data challenges, access fragmented data pipelines, and gain valuable insights for agile decision-making.
Techniques in Data Orchestration
Data orchestration involves several techniques that are common to all data orchestration workflows:
Collecting and Organizing Data:
Identifying all data pipelines and accessing existing and incoming data is the first step. Data orchestration tools use software development kits (SDKs) and APIs to collect data from various sources, including client touchpoints, cloud storage, and applications. Data is then labeled and checked for integrity before moving to the next stage.
Data Transformation:
Data transformation standardizes data into a format that analytics tools can understand. This step is crucial because data may exist in different formats across multiple pipelines. Automation tools streamline this process, ensuring consistent data formatting.
Data Unification:
Once data is internally compatible, it needs to be unified into a master dataset. This unified view is essential for both B2C and B2B companies, as it eliminates duplicate data, defines source fields, and links data to tables and fields.
Data Activation:
Activation involves making orchestrated data available to analytic and BI tools in real-time. This eliminates the need for time-consuming data-loading processes and ensures users can access data on their preferred platforms instantly.
By automating these techniques, data orchestration tools free up data engineers to focus on higher-value tasks like analysis and big data projects.
Integrating Automation in Data Pipelines
Automating data pipelines plays a crucial role in improving efficiency. It allows data engineers to redirect their efforts towards high-value tasks like analysis, presentation, and managing big data projects. Shifting from manual data pipeline management to automated workflow management offers several advantages:
- Reduced Complexity: Automation tools simplify data pipeline management, eliminating coding errors and shortening development time.
- Streamlined Workflows: Integrating automation provides teams with a holistic view of data pipelines, reducing complexity and streamlining workflows.
- Improved SLA Performance: Automation helps identify and resolve errors and process failures within existing deadlines, enhancing Service Level Agreement (SLA) performance.
Aligning ETL with Data Orchestration
ETL (Extract, Transform, Load) processes are fundamental in data management. Many organizations build ETL processes around their data pipelines. These ETL transformations are custom-coded by data engineers to accommodate specific data sources and formats. Integrating ETL processes with data orchestration ensures that data pipelines are efficiently managed and transformed to meet analytics needs.
Leveraging APIs for Data Synchronization
API integration plays a crucial role in enabling data synchronization between different applications and systems. APIs facilitate seamless communication and data exchange among diverse applications. Many companies rely on multiple API gateway providers to address their integration needs efficiently.
Best Practices in Data Orchestration
To achieve the best outcomes from data orchestration, organizations should follow best practices. Here are three key recommendations:
Select the Right Data Orchestration Tools:
Choose data orchestration tools that align with your organization’s needs and workflow processes. Look for tools that offer flexibility, scalability, and integration with popular cloud services like Google Cloud, Microsoft Azure, and others. Ensure that the selected platform provides user-friendly features, such as directed acyclic graph (DAG) implementation and hybrid execution, to protect confidential data.
Implement Effective Data Governance:
Data governance is crucial for managing data consistently. Assign specific team members to implement data policies, define data processing and sharing policies, establish naming and storage conventions, and determine metrics for data cleanliness and usability. Data governance should connect people, technology, and processes to maintain data integrity and quality.
Develop a Data Culture:
Fostering a data culture within your organization is essential for effective data management. Define objectives for using big data in business operations, encourage data-driven decision-making, and align organizational goals with data orchestration efforts. A robust data culture enhances the effectiveness of data orchestration and drives better results.
The Future of Data Orchestration
Data orchestration is evolving rapidly, and several trends are shaping its future:
- Data as a Product: Organizations are shifting towards treating data as a product, leading to data-aware pipelines that understand the intricacies of tasks.
- Declarative Pipelines: The trend is moving towards creating declarative and data-aware pipelines that can adapt to changes in upstream data assets.
- Abstractions for Code Reusability: Abstractions are being used to reuse code in complex multi-cloud environments, making pipelines more data-aware.
- Python Dominance: Python is becoming the primary language for functional data engineering, driving more data-related tasks and processes.
Data Orchestration’s Impact on Business Intelligence
Data orchestration plays a crucial role in enhancing business intelligence (BI) within organizations. It enables quick access to insights needed for decision-making, leading to improved alignment between departments, more accurate forecasting, and cost reduction.
Some additional BI benefits of data orchestration include:
- Accurate Client Profiling: Data orchestration enables accurate segmentation and profiling of clients, leading to more effective marketing strategies.
- Enhanced ROI Attribution: High-quality data and automated workflows improve return on investment (ROI) attribution.
Leveraging Data Orchestration for Machine Learning and AI
Combining data orchestration with artificial intelligence (AI) and machine learning (ML) can optimize processes and reduce costs. Automation and orchestration tools help automate repetitive tasks, provide real-time insights, and enhance business value. Here’s how data orchestration can benefit AI and ML:
- Cost Reduction: Automation reduces costs by handling high-volume, low-value tasks.
- Granular Insights: Real-time data orchestration provides granular insights, improving root-cause analysis and performance monitoring.
- Human-in-the-Loop Automation: Orchestrating human-in-the-loop (HITL) automation can enhance chatbots’ capabilities and knowledge management.
End Note
Data orchestration is the key to unlocking the full potential of your organization’s data. By implementing effective data orchestration strategies and selecting the right tools, you can harness the power of data to drive growth and make informed decisions. Embrace data orchestration and embark on a journey to achieve harmony with your data.