About the Author
Can Goktug Ozdem is the founder of Datrick. He is a data engineer with over nine years of experience in the field. He is a big fan of remote work and is passionate about bringing insights through data while traveling to different parts of the world.
DataOps is an orchestration practice for analytics, increasing the degree to which insightful analytics are delivered, atop robust frameworks and systems.
For too long, the industry has focused on raw performance, measuring success by how quickly systems can process data. But speed is only one part of the equation. The other is accuracy, and that’s where DataOps comes in.– Forbes Tech Council, from IOPS To DataOps, 2023
Without proper integration and interpretation of data, companies risk making faulty decisions and losing out to their competitors.
DataOps helps organizations manage large amounts of data generated by various sources while also improving collaboration among different stakeholders. It provides insights into how teams can use analytics to improve decision-making. A research paper by Sahoo and Premchand 2019, as featured in the Fraunhofer ISST report, provides a useful summary of the practice of DataOps.
The collaborative data management practice through a DataOps culture aims to improve the communication, integration, and automation of data flows across an organization in order to move data from its source to its target destination. Its purpose is to accelerate the delivery of high-quality data from various sources.
As a practice, adopting Dataops brings a constellation of benefits to your business – an increase in efficiency, reduction in costs, simplified communication, and automated processes, while avoiding bloated processes to drive business performance. In addition, it enables your organization to detect anomalies quickly so your teams can take corrective action promptly.
All these benefits make developing a strong DataOps culture necessary for any organization looking to capitalize on its existing data assets or create new ones.
How DataOps Delivers Business Value
Developing a DataOps culture is a methodology combining agile development, DevOps, and data management. This innovative approach goes beyond just integrating and automating workflows; it prioritizes collaboration within teams.
This is a crucial aspect as the seamless exchange of knowledge between team members is paramount for the success of a project.
By fostering a culture of collaboration, DataOps helps teams to work more efficiently and effectively, which ultimately leads to the delivery of better-quality data products.
In addition, practicing a strong DataOps culture offers an array of perks:
- Faster insights: DataOps streamlines data ingestion through process automation, reliable data engineering, and workflow optimization—a principle based on DevOps rules, in particular, DataOps to bring DevOps benefits to data analytics. This allows organizations to make data-based decisions faster.
- Improved data quality: DataOps culture implements data validation, monitoring, and governance with continuous error detection which makes it easier to maintain data quality and accuracy throughout your data pipeline.
- Shorter cycle times: DataOps culture works on the principle of agile development and shorter release cycles. This allows your organization to quickly iterate its data processes and effortlessly adjust to changing business needs.
- Reduced risk: DataOps helps reduce errors and manage risks related to data processing and analytics by incorporating data versioning, tracking data lineage, and automating testing.
- Increased efficiency: By automating tasks, DataOps reduces manual and repetitive work for data professionals. This allows them to focus on more valuable activities, leading to improved efficiency overall.
- Greater scalability: DataOps helps organizations effectively scale their data processes by handling bigger data volumes. It also supports the integration of new data sources.
- Compliance and security: DataOps is a practice that helps ensure compliance with regulations. It maintains high levels of security for sensitive information, reducing the risk of data breaches.
Align Your Business Goals And DataOps Principles
Ensuring a successful DataOps culture requires a shift in mindset, with a focus on collaboration, transparency, and continuous improvement. It involves aligning and orchestrating people, processes, and technologies to improve, accelerate, and refine the quality of the data integration processes.
To build a successful DataOps culture, it is vital to align business goals with the overall strategy of DataOps. To align business goals with DataOps, follow these steps:
Step 1: Define clear business objectives
What are the main goals and strategic priorities that your organization is striving to accomplish? These goals should be specific, measurable, attainable, relevant, and time-bound (SMART).
Step 2: Decide the stakeholders you would like to get involved in
Collaborate with different stakeholders such as business leaders, data engineers, data scientists, and analysts. It is important to ensure that everyone comprehends the business objectives and their contribution toward accomplishing them. This collaborative approach ensures alignment and shared ownership of the objectives.
Step 3: Map your data requirements
To achieve your business objectives, determine which types and formats of data sources you need. To better understand possible challenges and opportunities, evaluate the accessibility, availability, and quality of your data.
Step 4: Develop a data strategy
Create a detailed plan for managing data that includes all the necessary procedures, software, and hardware for acquiring, storing, organizing, analyzing, and presenting data. The plan should cover all aspects of data management. The strategy should align with the overall business goals of your organization and allow for smooth integration with current systems.
Step 5: Implement DataOps principles
Adopt DataOps best practices, including agile development, automation, continuous integration, and collaboration. One of the tried and tested approaches is the Eckerson DataOps Framework, for building a strong foundation for a successful DataOps culture and practice.
Step 6: Monitor and adapt
Make sure to consistently keep track of how well your data processes are performing. Track the progress towards achieving your business goals based on the predetermined KPIs and metrics. It is important to regularly reassess and modify your data strategy in response to changes in business needs and to fix any deficiencies or weaknesses.
Step 7: Foster a data-driven culture
Provide training, resources, and support to employees at all levels to foster a mindset that is based on data.
Build A Cross-Functional Team
The first step in creating a successful DataOps culture within your organization is the formation of a cross-functional team.
This team should include members from different departments and bring together diverse skills, knowledge, and experience. Such interdisciplinary collaboration allows for improved communication, understanding of complex issues, and enhanced efficiency.
Creating this type of collaborative environment requires commitment from both management and individual staff to ensure that objectives are met. It also requires effective leadership as well as strategies to overcome any potential barriers such as budget constraints or varying levels of expertise between team members.
The following points highlight some key aspects of forming successful cross-functional teams:
- Identify the common goal: All personnel must be clear on what they aim to achieve with their work.
- Assign roles and responsibilities: Each member should have specific tasks set out for them.
- Set expectations and deadlines: These should be realistic yet challenging enough to motivate everyone involved.
- Recognize achievements: It’s important to reward effort so that individuals feel valued.
With an effective approach, organizations can create a productive working environment where multiple disciplines can come together towards achieving shared goals.
Proper implementation of these principles will help develop trust amongst colleagues while fostering innovation through the use of new technologies or processes. In turn, this encourages employees to take ownership of their projects, allowing for greater autonomy which leads to increased motivation and job satisfaction among workers.
Implement Continuous Integration And Deployment
Continuous integration (CI) and continuous deployment (CD) are two popular aspects of DataOps. Your organization can use this to foster a DataOps culture that encourages collaboraticameron, knowledge sharing, and continuous improvement.
CI is the process of integrating code from various developers in an automated fashion. This allows for quick bug fixes and feature enhancements. It ensures that all changes are tested quickly before they are released into production. CD automates the release process after successful tests have been completed. It allows your teams to deploy new features with minimal effort and risk.
Your organization must define clear processes around CI/CD in order to ensure it is adopted properly within the organization. This includes establishing roles and responsibilities among members of the team as well as setting up appropriate tools and technologies needed for implementation.
It should also involve developing guidelines on how often releases take place, what types of tests need to be performed, how long each phase should take, etc.
Furthermore, your company may want to consider investing in resources such as training or consulting services. A successful DataOps culture requires frequent communication between different parties involved in the process.
To facilitate this dialogue, organizations should focus on creating a feedback loop between developers, testers, operations staff, product managers, and others who play a role in your development cycle. Automated reporting systems are useful to keep track of progress throughout the lifecycle so that everyone has visibility into any issues or delays.
With these steps taken care of, your organization will have a much better chance of achieving success with its CI/CD initiatives.
Automate And Orchestrate Strategies
The idea that automation and orchestration are effective ways to build a DataOps culture in an organization has become increasingly popular. The concept can be traced back to the early 2000s when companies began exploring how automated processes could simplify IT operations, improve compliance with standards, decrease downtime, and reduce costs.
Automation and orchestration strategies aim to streamline tasks by automating repetitive manual processes. They are also helpful in coordinating different systems so they work together efficiently. Here are several models of automation and orchestration.
- Infrastructure-as-Code (IaC): IaC helps to provision your complex infrastructures faster.
- Configuration Management tools such as Chef, Puppet, Ansible, or SaltStack are useful for deploying software configurations rapidly.
- Continuous Integration/Continuous Delivery pipelines help your teams test code before deployment.
- Test Automation tools can be used to run tests on applications regularly.
- Rules Engines are great for specifying rules around specific tasks.
- AI/ML algorithms to optimize resource usage in cloud environments.
All these techniques enable your organization to create agile development process cycles while also reducing labor costs associated with manual operations.
Moreover, these approaches help increase process reliability.
Your users can monitor all components in the process ensuring uptime availability and scalability. This allows your org. to quickly deploy new services or updates without disrupting existing deployments leading to improved customer satisfaction levels.
Implement Data Governance And Compliance
Data governance and compliance is an essential components of any successful DataOps culture.
It provides a framework for ensuring that your data is managed in accordance with applicable laws, regulations, policies, and procedures. Moreover, it sets clear expectations about how data should be collected, stored, used, and shared across your organization.
The purpose of effective data governance and compliance measures is to ensure that privacy requirements are met when collecting and handling personal information; compliance obligations are followed when using customer or vendor-sensitive data; security standards are adhered to when processing financial transactions; and ethical principles are applied when dealing with confidential business intelligence.
By properly implementing these measures, your organization can effectively protect its assets while promoting trust between customers and partners.
It is important to create a well-defined set of processes that encompass all aspects of governance and compliance within the organization. This includes developing internal controls, establishing roles and responsibilities related to access control systems, creating policies for the responsible use of data resources, monitoring activities on an ongoing basis, and regularly reviewing procedures for accuracy.
When implemented correctly, this will enable your organization to maintain its commitment to protecting its data assets from unauthorized access or misuse.
Set and Monitor Metrics For Success
Data governance and compliance are essential to the success of any data operation. Establishing a culture within your organization that is focused on these concepts involves both tangible and intangible elements, all of which must be addressed in order for this type of environment to thrive.
To ensure sustainable success, your organization should focus its efforts on creating a monitoring infrastructure and developing metrics to track progress. You need to create an atmosphere where decision-makers understand how data operations can provide value by leveraging existing resources in creative ways.
This requires implementing systems with strong standards for collecting data, including methods for auditing performance against those principles. Additionally, it’s important to build structures designed to measure the efficacy of process execution over time.
In this way, your company can monitor not only how effective your processes are currently but also how they’ve evolved since implementation began. By establishing such frameworks and evaluating results alongside other key performance indicators (KPIs), your organization can have greater visibility into areas where improvement would benefit them most significantly.
This allows teams to identify opportunities more quickly while understanding exactly what steps will lead them down the path toward the achievement of desired outcomes. With consistent tracking of KPIs, organizations can make smarter decisions based on solid evidence which leads directly back to increased ROI from their data initiatives.
Integrating DataOps technology into your existing workflow is a complex process, requiring both technical and cultural considerations.
It involves evaluating technology and tooling for their ability to support the data-driven objectives of your business. This evaluation should involve not only assessing the capability of specific toolsets but also determining if they are compatible with current processes and infrastructure.
The selection of technology should be driven by the needs of your organization as identified in your data strategy. The chosen solutions should enable seamless integration between data-producing systems such as databases or application servers, and analysis engines like machine learning models. In addition, they must provide scalability, reliability, and security capabilities that meet industry standards.
Furthermore, when selecting technologies it is important to consider how suitable those solutions will be for your future developmental efforts; and their compatibility with emerging technologies can ensure continued success over time.
It is essential to measure potential solutions against relevant criteria before making any decisions on which technologies to use. These criteria should include
- Ease of implementation and maintenance
- Compatibility with existing architecture
- Availability of third-party expertise and services
- User experience (UX)
- Regulatory compliance requirements
- Your security protocols; and performance metrics related to latency and throughput timescales
Careful consideration should be taken to select tools that can best meet these needs while ensuring alignment with your overall organizational goals.
DataOps provides a powerful framework for your organization to leverage data-driven insights and enable rapid, iterative development.
By aligning business goals with DataOps strategies, building a successful DataOps culture can happen by developing a cross-functional team, automating workflows and processes, implementing governance policies, and monitoring metrics of success, your organization can unlock the potential of their data assets.
The journey towards a successful DataOps culture is like taking a long voyage – it may be arduous at times; however, if navigated correctly, the rewards will be plentiful.
If you embrace this DataOps culture shift, you will undoubtedly realize tremendous value from your data investments in the long run.
If you would like to ensure that your data is handled well while you focus on developing a DataOps culture, the Datrick team of data engineers and analysts can help you choose the best repertoire of tools, deploy, and manage them. Reach out to us for a free consultation.