Data Operations

Definition of Data Operations

Data Operations is the practice (e.g., frameworks, methods, capabilities, resources, processes, and architecture) for delivering data to create insights and analytics with greater speed, scale, consistency, reliability, governance, security, and cost effectiveness using modern cloud-based data platforms and tools applying agile principles. Data Operations shares core concepts with Software Development Operations (focusing on software product development, deployment, and delivery), including the need for effective, scalable, reliable, governed, secure delivery of data from source to target. 

Data Operations recognizes the need for a system to manage ever-increasing amounts of data from many different sources delivered to many constituents with diverse needs, representing an evolution from the typical centralized, monolithic model of enterprise data warehouse to a distributed model incorporating a data mesh for federated insights creation supported by centralized, automated, self-service oriented services using a cloud-based data lakehouse (e.g., centralized repository of ready-to-analyze, discoverable, governed sources, both fully modeled and integrated as well as individual). 

Purpose

The purpose of data operations is to deliver data with greater speed, scale, reliability, consistency, governance, and cost effectiveness using modern cloud-based data platforms, applying agile principles. Fundamentally, Data Operations recognizes that data volume, velocity and variety is increasing, as are the needs of an ever-increasing audience of diverse users: data analysts, data scientists and decision makers – creating the need to move from centralized, monolithic enterprise data stores / data warehouse to a distributed model where insights and analytics are created by users using centralized self-service oriented infrastructure and tools that are secure and governed.

Key characteristics of effective data operations are as follows:

• Agile – Data Ops applies agile principles to continually evolve to address emerging challenges and needs with speed, flexibility, and ever-increasing maturity.
• Cloud-based – Data Ops is particularly useful as companies move to the cloud, including hybrids that may include multiple public and private clouds.
• Distributed – Data Ops is particularly effective when configured to support a distributed model of delivery, where centralized infrastructure for data access, data preparation, insights, and analytics creation tools (particularly self-service), governance, and security support decentralized insights and analytics creation. Terms like Data Fabric can be used to describe the centralized aspects of the infrastructure, whereas Data Mesh refers to the distributed nature of insights and analytics creation: business users own responsibility for the data they use and the insights and analytics they create.
• Secure – Data Ops works to ensure that all aspects of data and the resulting creation and distribution of insights and analytics are secure, following principles, policies, and processes that ensure secure data storage and transmission that is efficient and effective.
• Governed – Data Ops is implemented, deploying effective data governance: having policies and procedures for data, insights and analytics creation and usage, as well as data, both sourced, derived and published for insights and analytics creation, including availability, quality, discoverability, observability, access, sharing, changing and publishing. More recent technologies like data catalogs, feature / metric stores and the use of a semantic layer help to improve data governance across the entire data-to-insights lifecycle, including source, derived, and published levels. 
• Standardized / Automated – Processes are standardized and automated, supporting ease of development, use, and operations, including for self-service to reduce resource dependencies and eliminate hand-offs between multiple functions.
• Self-Service – Processes are standardized and automated, supporting ease of development, use, and operations, including for self-service by data analysts, data scientists, business analysts, and end users to reduce resource dependencies and eliminate hand-offs between multiple functions.
• Discoverable / Shareable / Reusable – Source Data, Ready-to-analyze data, insights-ready data, metrics, features, and semantic layer models are available, discoverable, accessible (governed), shareable, and reusable to ensure effective utilization and improvement without incurring duplication. 

Recent research from Nexla (2022), surveyed data professionals about the need for data operations, highlighted below:

•  85% of respondents say their companies have teams working on ML or AI. This is up from 70% in 2021
• 73% of respondents say their company has plans to hire in DataOps in the next year
• Data professionals are only spending 14% of their time on analysis. The rest of the time is spent on required but low value-add tasks like data integration, data cleanup, and troubleshooting.
• Data engineers spend 18% of their time on troubleshooting. That works out to 9.3 weeks a year!
• Data pros are longing for automation in their jobs. We asked data pros what tasks in their current role would benefit from automation:
  • The majority, 56%, unsurprisingly said that data clean up would benefit from automation
  •  Analysis was the second-most cited task, at 47%
  • Data integration was close behind at 46%, and building data pipelines at 41%

Primary Uses of Data Operations

Major data operation activities are defined below:

Cleansing – Data cleansing involves removing or altering source data that is not necessary, complete or accurate. Cleansing ensures that data is relevant, useful and understandable.

Transformation – Data transformation is the method for transforming data to conform definitions across dimensions, such as aggregating data that is sourced at a day level to be aggregated at different levels, such as month, quarter, year-to-date, etc. Aggregation ensures that the data is defined consistently based on how the data is used by the business. Aggregation also improves query speed and performance. Other methods for data transformation include attributes, grouping, and normalization.

Available / Discoverable – Data operations make data — including source, derived, integrated, and published versions — discoverable and reusable through tools such as data catalog, metric / feature store, and semantic layer.

The Key Pillars for DataOps Success

There are four key pillars for implementing an effective DataOps approach:

1. Adopting data integration as code by using data integration tools that can be managed as code, rather than a manual approach for managing the data pipeline. This enables version control and CI/CD techniques that treat data as a product that’s delivered to data consumers.
2. Leverage continuous integration and continuous deployment (CI/CD) by implementing automated tooling to continuously develop and maintain data assets. This helps organizations accelerate and streamline the delivery of relevant data to data consumers.
3. Improving collaboration by bringing together teams across the organization to communicate more effectively. Along with collaboration tools, common metadata definitions and shared metrics can improve data consistency throughout the business.
4. Enhancing monitoring by tracking data usage and performance for data pipelines and user query behavior in a similar way to tracking product usage. This enables organizations to better estimate the business impact of data.

The key to DataOps and a great self-service data architecture is to think about data assets as products — just like you’d think about creating software products. By implementing these four pillars, organizations can deliver high-quality data products to data consumers more efficiently.

Benefits of Integrated Data Operations

Data operations revolutionize how companies handle information assets. This approach converts scattered data into actionable assets that drive innovation. A unified framework for systems, tools, and workflows delivers these critical benefits:

• Accelerated Decision-Making – Unified data streams provide real-time visibility into operations. Teams gain immediate access to insights that drive timely and informed actions.
• Cost Optimization – Automated pipelines minimize manual data handling. Organizations lower operational expenses while reducing errors that lead to rework.
• Enterprise-Wide Data Quality – Standardized validation rules maintain consistent accuracy across sources. Trusted analytics foundations support compliance and reliable outcomes.
• Cross-Functional Collaboration – Centralized access breaks down departmental silos. Teams align strategies using shared metrics and real-time dashboards.
• Scalable AI/ML Readiness – Preprocessed, governed data feeds directly into machine learning models. This streamlined approach accelerates time-to-value for predictive analytics.
• Enhanced Security Posture – Role-based access controls and encryption protocols protect data across systems. Risks associated with fragmented architectures decrease substantially.
• Future-Proofed Infrastructure – Modular frameworks adapt seamlessly to new data sources and formats. Organizations avoid costly reengineering as requirements evolve.

These benefits create compounding value over time. Organizations strengthen their ability to innovate and outperform competitors through responsive, data-driven strategies.

Common DataOps Roles and Responsibilities 

DataOps teams combine technical expertise with process optimization through these critical roles:

• DataOps Engineer – DataOps Engineers design and maintain automated data pipelines. They ensure ETL and ELT processes run smoothly from source to destination, and implement monitoring systems for pipeline performance and data quality.
• Data Operations Manager – This critical role aligns data infrastructure with organizational goals. DataOps Managers oversee resource allocation and budget management for cloud systems, and they coordinate with analytics teams to prioritize high-impact use cases.
• Data Reliability Engineer – These engineers establish service-level agreements for data accuracy and freshness. They diagnose pipeline failures, implement preventive solutions, and optimize resources for cost-performance.
• Data Governance Specialist – Specialists define access controls and audit trails for regulatory compliance. They maintain metadata repositories for consistent classification and data lineage tracking to ensure transparency across transformations.
• Automation Architect – Architects create reusable templates for data ingestion and transformation tasks. They integrate DevOps tools into workflow orchestration. Standardized testing frameworks for schema validation are also their responsibility.
• Collaboration Facilitator – These professionals document workflows for cross-team understanding. Facilitators organize training sessions for self-service tool adoption, and they bridge communication gaps between technical and business teams.

Together, these roles create agile systems that treat data as a dynamic asset. They enable organizations to scale operations while maintaining security and governance standards.

Key Business Processes Enhanced by Data Operations

Data operations serve as the foundation for a wide range of critical workflows that drive organizational efficiency and innovation. These processes demonstrate how technical capabilities translate into tangible business outcomes:

• Demand Forecasting and Inventory Optimization – Automated data pipelines aggregate sales histories, market trends, and supplier lead times. Machine learning models process this information to predict regional demand spikes. Retailers like Walmart use these insights to reduce stockouts while minimizing overstock waste.
• Customer Experience Personalization – Real-time data integration combines CRM records, web interactions, and support tickets. Segmentation engines identify high-value customer cohorts and predict churn risks. Streaming analytics enable tailored promotions, improving retention rates through timely interventions.
• Regulatory Compliance Automation – Governance frameworks automatically classify sensitive data across cloud and on-premises systems. Audit trails document every access event and transformation step. Financial institutions leverage these systems to cut compliance reporting time and reduce human error.
• Supply Chain Risk Mitigation – IoT sensors and vendor APIs feed logistics data into centralized warehouses. Predictive analytics flag potential disruptions like port delays or raw material shortages. Manufacturers preemptively reroute shipments, maintaining production schedules despite external volatility.
• Operational Cost Benchmarking – Unified metrics track energy consumption, equipment uptime, and labor efficiency across facilities. Comparative analytics identify underperforming units and quantify optimization opportunities.
• AI-Driven Product Development – Cross-functional data lakes consolidate research data, customer feedback, and quality control records. Natural language processing tools analyze unstructured feedback to guide feature prioritization. 
• Dynamic Pricing Strategy – Marketplace operators integrate competitor pricing, inventory levels, and buyer behavior data. Reinforcement learning algorithms adjust prices in real time while maintaining margin targets.

These processes illustrate how mature data operations turn raw information into competitive weapons. Organizations achieve faster cycle times, improved resource allocation, and enhanced strategic agility by embedding analytics into daily workflows.

In a podcast conversation between Sanjeev Mohan, Principal of SanjMO, and Dave Mariani, CTO and Founder of AtScale, Mohan states, “Data ops is a layer that you put on top of data management. You can put it on top of your building, your business logic, or you can put it on top of your maybe cloud migration initiative. So that’s why data ops is so important.”

AtScale’s semantic layer accelerates these processes by providing consistent, governed metrics across all analytical tools. Its virtualized queries enable real-time insights without data duplication, which ensures stakeholders have access to accurate information whether they use Power BI, Tableau, or custom ML models. This unified approach eliminates the friction between data engineering and business teams.

Common Technologies Categories Associated with Data Operations

Technologies involved with data operations are as follows:

• Data Engineering – Data engineering is the process and technology required to move data securely from source to target in a way that it is easily available and accessible.
• Data Transformation – Data transformation involves altering the data from its raw form to a structured form that is easy to analyze via queries. Transformation also involves enhancing the data to provide attributes and references that increase standardization and ease of integration with other data sources.
• Data Preparation – Data preparation involves enhancing it and aggregating it to make it ready for analysis, including to address a specific set of business questions. 
• Data Modeling – Data modeling involves creating structure and consistency as well as standardization of the data via adding dimensionality, attributes, metrics and aggregation. Data models are both logical (reference) and physical. Data models ensure that data is structured in such a way that it can be stored and queried with transparency and effectiveness.
• Database – Databases store data for easy access, profiling, structuring and querying. Databases come in many forms to store many types of data.
• Data Warehouse – Data warehouses store data used frequently and extensively by the business for reporting and analysis.  Data warehouses are constructed to store the data in a way that is integrated, secure and easily accessible for standard and ad-hoc queries for many users.
• Data Lake – Data lakes are centralized data storage facilities that automate and standardize the process for acquiring, storing, and making data available for profiling, preparation, data modeling, analysis, and reporting / publishing. Data lakes are often created using cloud technology, making data storage inexpensive, flexible, and elastic.

DataOps Trends and Future Outlook 

The rapid evolution of DataOps is reshaping how organizations manage and leverage data. Key trends are driving this transformation, each addressing critical demands for agility, intelligence, and compliance in modern enterprises. 

1. AI-Driven Pipeline Automation

Artificial intelligence is revolutionizing data workflows by automating tasks like error detection, resource allocation, and performance optimization. Machine learning models now proactively identify bottlenecks and reroute data flows, reducing manual oversight in advanced implementations. This shift allows teams to focus on strategic initiatives rather than routine maintenance, accelerating time-to-insight while minimizing operational costs.

2. Real-Time Data for AI and Decision-Making

Organizations are prioritizing instant analytics to power AI models and business decisions. Enterprises now integrate streaming data pipelines to analyze information as it’s generated, enabling dynamic responses to market shifts and customer behavior. Industries like retail and finance use these capabilities to adjust pricing, detect fraud, and personalize experiences, all within milliseconds of data ingestion.

3. Convergence of DataOps, DevOps, and MLOps

Siloed workflows are giving way to unified frameworks that synchronize data engineering, application development, and machine learning. Cross-functional teams share governance protocols and testing processes, accelerating deployment cycles by 40% in organizations adopting this approach. This alignment ensures consistent data quality while accelerating AI model deployment, creating a seamless pipeline from raw data to production-ready insights.

4. Automated Compliance at Scale

Global regulations and data privacy laws are driving demand for embedded governance tools. Automated systems now classify sensitive data, enforce access policies, and generate audit trails without manual intervention. Advanced platforms use synthetic data generation to safely test workflows while maintaining compliance. In turn, this can minimize audit preparation time in regulated industries like healthcare and finance.

AtScale and Data Operations

The AtScale semantic layer platform revolutionizes DataOps by acting as a unifying force between raw data infrastructure and actionable business insights. By virtualizing queries and centralizing metric definitions, AtScale eliminates redundant data pipelines while ensuring real-time access to governed, consistent metrics across BI tools and AI frameworks. This approach streamlines DataOps workflows, reducing ETL complexity, enforcing security policies, and enabling cross-functional collaboration through a single source of truth.

The semantic layer’s ability to abstract technical complexities allows organizations to scale analytics and machine learning initiatives efficiently, thereby turning fragmented data operations into agile, insight-driven processes. With AtScale, enterprises achieve faster time-to-insight, reduced cloud costs, and seamless governance, transforming DataOps from a backend necessity into a strategic accelerator for data-driven innovation. Book a demo to learn more.