What is DataOps? Optimize the data journey by uniting tech, teams, and workflows

Just by its name, many development, database, and DevOps professionals assume they grasp DataOps. Isn’t it just DevOps for the data pipeline? No. 

But also, yes. 

DataOps, or data operations, does embrace many DevOps-aligned principles, but it's not about enabling the development or deployment of applications. It’s about confidence, speed, and reliability in data analytics and the products it supports. 

What is DataOps? Optimize the data journey by uniting tech, teams, and workflows 

DataOps is the strategic management of an organization’s data journey to ensure high integrity, reduce time-to-insight, and continuously optimize the power, precision, and reliability of data analytics. Tapping many of the principles of DevOps, agile, and lean methodologies, the primary goal of DataOps is to improve the speed, efficiency, and value of data pipelines. In service to data analytics and related products and platforms like AI/ML, DataOps maintains a stable and trustworthy system in the age of endlessly scalable on-demand cloud storage and compute resources. 

Like DevOps and other related frameworks, DataOps strives to safely keep pace with its users’ desired speed of analytics and innovation. In every industry from ecommerce to logistics to the Internet of Things, the need for nearly instant data collection and analytics pushes organizations to build bigger, stronger, and more complex data pipelines and products. 

It’s the role of DataOps to orchestrate collaboration, communication, and innovation of:

• Data science and engineering
• Data analytics
• Data transformation (e.g., ETL/ELT)
• Data quality and governance
• Data storage and evolution
• IT Infrastructure

Across these elements and more, DataOps emphasizes automation, standardization, and efficiency for the entire data journey – from the moment the data is created or ingested, through its processing and storage, and to delivery, monitoring, and beyond. 

Core elements of DataOps: The data journey

While equating DataOps and DevOps isn’t entirely accurate, it does provide a familiar philosophical starting point. DataOps focuses on ingesting, integrating, storing, testing, monitoring, processing, and governing an organization’s data pipeline to maintain data integrity while improving system performance for data users. The data journey stretches from every data collection and creation point through to its querying or use in a data model, etc. 

Let’s break down the data journey. 

Data ingestion

Data ingestion is the initial step of the data journey, involving the collection of raw data from various sources. This data can come from databases, APIs, IoT devices, and other data streams. Efficient data ingestion ensures that data is captured in real-time or batch-processed and fed into the data pipeline for further processing.

Data source integration

Data source integration involves combining data from different sources into a unified pipeline. This step ensures that all the different internal, ephemeral, and third-party data sets are aligned and transformed to be used together, enabling comprehensive analysis. Effective integration minimizes data silos and ensures consistency across the organization’s data landscape, but it can be challenging to maintain integration as database schemas evolve more quickly. 

Data storage & security

Data storage and security focuses on accessibility and protection. This includes choosing the appropriate storage solutions (such as cloud storage, data lakes, warehouses, etc.) and then putting in place the right protections, access controls, and other elements to ensure security and compliance.

Continuous data quality testing

Continuous data quality testing involves automated and standardized processes for checking data accuracy, consistency, and reliability. This should provide immediate feedback to address errors and ensure the data is trustworthy and useful for analysis. High-quality data is critical for making properly informed business decisions.

Data processing

Data processing can mean any number of minor or significant transformations, normalizatiosn, or other adjustments to raw data. The goal is to get it ready for analytics and insights. By cleaning, enriching, aggregating, and otherwise preparing data for reporting, analytics, and machine learning models, organizations derive actionable insights sooner.

Data monitoring & observability

Data monitoring and observability entail continuously tracking data pipeline performance and health. This includes setting up alerts and dashboards to detect anomalies, performance issues, or bottlenecks. Effective monitoring ensures that data flows smoothly and any issues are quickly identified and addressed.

Data governance

Data governance is the framework for managing data availability, usability, integrity, and security. It involves establishing policies, procedures, and standards to process data safely and appropriately on its entire journey. Strong governance practices ensure compliance with regulations and build trust in the organization’s data assets.

These steps collectively ensure that data is efficiently managed, from its initial capture to its final use in analytics and decision-making processes.

DevOps vs DataOps

As should be clear now, DataOps is not just DevOps applied to data pipelines. Yes, both disciplines focus on automation, continuous improvement, collaboration, and core techniques like version control, tracking (observability), and agile deployments. 

Yet while DevOps streamlines application development and deployment, DataOps focuses on the management of data pipelines for analytics, reporting, machine learning, and other data platforms, products, and uses. Distinct and specialized teams manage each discipline on their own – although close collaboration is the key to maximizing the benefits of both methodologies. 

For teams that want to accelerate their DataOps maturity, their DevOps cohorts can surely share some relevant advice.    

Embrace DataOps for better data-driven business results

Embracing DataOps can revolutionize how your organization handles and leverages data, driving better business results by breaking down data silos. 

Faster time to insight

By automating and streamlining data processes, DataOps brings down the time to value of data – reducing the time it takes to extract meaningful insights. This speed enables organizations to make timely, data-driven decisions that can enhance competitiveness and respond more swiftly to market changes.

Higher quality data (and decisions)

Continuous data quality testing and monitoring ensure that your data is accurate, consistent, and reliable. The higher an organization’s data quality, the better its data-driven decisions, user experiences, and regulatory compliance posture. With DataOps, data quality issues are identified and resolved promptly, preventing potential negative impacts on business operations.

More agile and flexible pipelines

DataOps integrates agile and lean methodologies, allowing organizations to pivot as needed to evolving business needs and industry advancements. This flexibility ensures that data pipelines can scale and evolve with the growing demands of data analytics, machine learning, and other data-driven initiatives.

More valuable collaboration & communication

DataOps fosters closer collaboration between data engineers, data scientists, IT, and other related stakeholders from the development team to the C-suite. By strategically managing this communication, DataOps ensures that everyone is working towards common goals and adequately served by technology, leading to more efficient and effective data projects.

Stronger security, easier compliance

With end-to-end data governance frameworks, DataOps incorporates security measures and compliance requirements into automated, standardized workflows. This reduces the risk of data breaches and fines, protecting the organization’s reputation, finances, and competitive position.

Lower costs, better ROI

By optimizing data workflows and automating repetitive tasks, DataOps can reduce operational costs, especially in the face of cloud platform sprawl. Efficient data management and processing lead to better resource utilization and lower expenses, freeing up budget for other strategic initiatives. DataOps also maximizes data related investments by improving productivity, spreading value to more stakeholders, and eliminating inefficiencies. 

A mature DataOps organization nearly eliminates the barrier to entry for new data initiatives with its streamlined processes. 

More innovation

DataOps supports the rapid development and deployment of data-driven products and services from inventory recommendation engines to advanced AI agents. By ensuring reliable and high-quality data, organizations can experiment, innovate, and bring new solutions to market faster, driving business growth and differentiation while protecting themselves from risk. 

Adopting DataOps not only enhances the technical management of data but also transforms how your organization can utilize data to achieve strategic objectives. By improving speed, quality, and collaboration, DataOps empowers businesses to harness the full potential of their data assets.

DataOps for the database: integrating change management

In the data journey, databases are the backbone of storage, retrieval, and transformation. They’re essential for maintaining the integrity, performance, and reliability of the entire data journey. By extending DataOps principles to database change management, you can transform and improve a typically slow, manual, and error-prone region of the data journey. 

DataOps for databases includes automating database schema changes and maintaining robust version control for every deployment. A complete database DevOps platform that supports DataOps, Liquibase enables governance and tracking for complete control and visibility that benefits the entire data journey. 

By incorporating these practices for collaborative, self-service database change management, organizations can achieve faster, more reliable, and more consistent data journeys.

Discover the opportunities unlocked by automation, governance, and observability in data pipeline change management.