--- title: "DataOps: What It Is and Why It Is Revolutionizing Enterprise Data Management" description: DataOps helps organizations manage data pipelines, governance, and data quality through agile and automated processes. image: https://newsroom.kireygroup.com/hubfs/Data%26Ai_24.png --- [kireygroup.com](https://www.kireygroup.com/) [![](https://newsroom.kireygroup.com/hubfs/item-2020-dicembre/logo-Blog-v2.svg)](https://newsroom.kireygroup.com/en/news) - [Ita](https://newsroom.kireygroup.com/news/dataops-cos-e-perche-e-una-rivoluzione-nella-gestione-dei-dati-enterprise?hsLang=it) - Eng - [Esp](https://newsroom.kireygroup.com/es/news/dataops-que-es-y-por-que-es-una-revolucion-en-la-gestion-de-los-datos-enterprise?hsLang=es) [Kireygroup.com](https://www.kireygroup.com/) # Get your daily dose of tech! ## We Shape Your Knowledge # [Welfare: from compliance to competitiveness, digital services and technologies are key to pension funds’ future](https://newsroom.kireygroup.com/en/news/welfare-digital-services-and-technologies-are-key-to-pension-funds-future?hsLang=en) By Michela Sertorio, Business Line Manager - BU Pension Funds atKirey. [Read more](https://newsroom.kireygroup.com/en/news/welfare-digital-services-and-technologies-are-key-to-pension-funds-future?hsLang=en) 9 MIN READ 26 October 2026 ### Latest News - [Welfare: from compliance to competitiveness, digital services and technologies are key to pension funds’ future](https://newsroom.kireygroup.com/en/news/welfare-digital-services-and-technologies-are-key-to-pension-funds-future?hsLang=en) - [Inference Costs: how to prevent AI agents from driving costs up exponentially](https://newsroom.kireygroup.com/en/news/inference-costs-how-to-prevent-ai-agents-from-driving-costs-up-exponentially?hsLang=en) - [Kirey Advisory at the ABI Lab workshop on Processes, Operations and Organization](https://newsroom.kireygroup.com/en/news/kirey-advisory-at-the-abi-lab-workshop-on-processes-operations-and-organization?hsLang=en) - [Kirey Strengthens Its SAP Presence: Mind The Value Achieves Reselling Partner Status](https://newsroom.kireygroup.com/en/news/kirey-strengthens-its-sap-presence-mind-the-value-achieves-reselling-partner-status?hsLang=en) - [The Dark Side of Multicloud: how to prevent workload placement from generating technical debt](https://newsroom.kireygroup.com/en/news/the-dark-side-of-multicloud-how-to-prevent-workload-placement-from-generating-technical-debt?hsLang=en) - [Shadow AI: 4 out of 5 employees use tools not governed by the company](https://newsroom.kireygroup.com/en/news/shadow-ai-4-out-of-5-employees-use-tools-not-governed-by-the-company?hsLang=en) - [Data Value](https://newsroom.kireygroup.com/en/news/tag/data-value) / # DataOps: What It Is and Why It Is Revolutionizing Enterprise Data Management #### Kirey [![](https://newsroom.kireygroup.com/hubfs/it.svg)](https://newsroom.kireygroup.com/news/dataops-cos-e-perche-e-una-rivoluzione-nella-gestione-dei-dati-enterprise?hsLang=it)    [![](https://newsroom.kireygroup.com/hubfs/ES.svg)](https://newsroom.kireygroup.com/es/news/dataops-que-es-y-por-que-es-una-revolucion-en-la-gestion-de-los-datos-enterprise?hsLang=es) 20 MIN READ 26 June 2026 - Share on: - - - - [All](https://newsroom.kireygroup.com/news?hsLang=en) - Categories + - [Corporate](https://newsroom.kireygroup.com/en/news/tag/corporate) - [Cybersecurity](https://newsroom.kireygroup.com/en/news/tag/cybersecurity) - [Cloud](https://newsroom.kireygroup.com/en/news/tag/cloud) - [Data Value](https://newsroom.kireygroup.com/en/news/tag/data-value) - [Innovation](https://newsroom.kireygroup.com/en/news/tag/innovation) - [Artificial Intelligence](https://newsroom.kireygroup.com/en/news/tag/artificial-intelligence) - [Software Development](https://newsroom.kireygroup.com/en/news/tag/software-development) - [Corporate Social Responsibility](https://newsroom.kireygroup.com/en/news/tag/corporate-social-responsibility) - [Advisory](https://newsroom.kireygroup.com/en/news/tag/advisory) - [Welfare & Social Services](https://newsroom.kireygroup.com/en/news/tag/welfare-social-services) - [Press & PR](https://newsroom.kireygroup.com/en/news/tag/press) As organizations strive to become [truly data-driven](https://www.kireygroup.com/en/competencies/data-ai), they are increasingly running into the limitations of traditional data management methodologies, which are no longer sufficient to support the complexity of modern digital ecosystems. It is from this need that the DataOps approach emerged, to make data-related processes more agile, reliable, automated, and sustainable Key points - Organizations aim to become data-driven and AI-driven, but they must overcome challenges related to **information silos, fragile pipelines, manual processes, and slow access** to insights. - DataOps is a new operating model that **combines automation, collaboration, observability, and governance** to make data management more agile, resilient, and sustainable. - Organizations that adopt DataOps reduce time-to-insight, improve data quality and team productivity, and **effectively support advanced analytics and AI initiatives.** ## The Need to Evolve Data Management The convergence of several technological, operational, and organizational factors has made a paradigm shift in data management inevitable. 1. First and foremost, organizations have experienced an **uncontrolled proliferation of information** across all dimensions traditionally associated with big data (velocity, volume, and variety). As a result, companies now manage **enormous amounts of data distributed across isolated silos**, from SaaS systems to IoT devices, which are difficult to integrate and even more challenging to govern consistently with corporate policies and regulatory requirements. 2. **Data also changes over time**: in its schemas, at the source-system level, or, more frequently, in **the meaning of the information** itself. This ultimately **compromises traditional pipelines,** which are typically rigid and based on exact mappings. 3. There is also **an increasingly critical challenge related to speed.** Modern organizations must be able to access up-to-date insights in near real time to support operational activities, dynamic decision-making processes, and advanced artificial intelligence models. Traditional batch-based approaches, designed to process data after the fact, can no longer meet these requirements. They may still support strategic decision-making, but they are insufficient for enabling a true transformation into a data-driven company. 4. **Another significant limitation** **is organizational** in nature. In companies operating under traditional models, data-related processes involve different roles and teams, from data engineers and developers to IT operations and security specialists. However, **these groups often work in separate silos, with tools, objectives, and operating methods** that are not always aligned. The result is fragmented management of the data lifecycle. ## The Limitations of the Traditional Model: Slow, Expensive, and Unsustainable The limitations of traditional data pipelines directly impact **costs, productivity, and an organization's ability to innovate.** These inefficiencies affect specialized professionals most heavily. Data engineers and data scientists often spend a significant portion of their time manually **preparing and cleaning data instead of focusing on analytics, automation, or AI model development**. At the same time, developers continue to write highly customized and difficult-to-reuse code that becomes fragile as soon as infrastructure or application changes occur. The most visible impact, however, is on speed. In many organizations, making a new data source available or developing a new analytics application **can take months due to manual testing, validation procedures, handoffs between teams, and integration issues**. This is incompatible with environments where the business requires near real-time insights and pipelines capable of powering AI applications, automation, and decision-support systems. There is also a less visible but equally critical cost: **missed opportunities.** Slow pipelines, outdated data, manual processes, and unreliable workflows **limit an organization's ability to respond to change**, make timely decisions, and fully leverage its information assets. ## DataOps: A Paradigm Shift in Data Management As is often the case with approaches ending in the suffix "Ops," DataOps does not simply identify a new category of tools. The term describes a methodological and organizational evolution aimed at **rethinking how data is managed, integrated, validated, and made available to the business.** The underlying idea is similar to the one that led to the emergence of DevOps in software development: overcoming fragmented, highly manual, and poorly collaborative models by replacing them with **continuous, automated, and observable processes**. DataOps applies these principles to the world of data while addressing an additional layer of complexity: data is dynamic and continuously changes in structure, meaning, quality, and update frequency. ### The Pillars of DataOps: People, Processes, and Technology DataOps si regge su tre dimensioni che operano e devono evolvere insieme.   1. **People and Culture** DataOps requires data engineers, data scientists, developers, and business teams **to work in an integrated way,** **with clear ownership** of each dataset and shared responsibility for its quality. Data ceases to be a byproduct of IT and **becomes a corporate asset with a lifecycle, an owner, and users.** 2. **Processes** Data ingestion, transformation, validation, and distribution activities are **codified into repeatable, documented, and measurable processes**. No more one-off scripts or procedures that exist only in someone's memory. Every step is traceable, and every anomaly can be detected. 3. **Technology** Tools enable processes; they do not replace them. Orchestrated pipelines, data quality monitoring systems, data catalogs that track the origin and meaning of every dataset, and testing environments separated from production all play a role. The technology behind DataOps **is not a single product** but a stack built around the specific needs of the organization. ## How Data Management Changes with DataOps DataOps introduces a significant break from the past. It is not an incremental improvement but **a complete rethinking of how data flows, is controlled, and reaches those who need it**. To understand the scope of this change, it is worth comparing the two models.  | ### Traditional model Data resides in the systems that generate it, from CRM platforms to Excel files, and is extracted, transformed, and loaded into a centralized data warehouse according to periodic cycles. In stable environments with manageable volumes, this approach has worked effectively over time. Its limitations become evident **as complexity and speed increase**. Data extraction is often manual or semi-automated, transformation logic is not always documented, and update cycles are measured in days or weeks. When an anomaly enters the data flow, it can travel through the entire chain before being detected—sometimes only **after the report has already reached the desk of the decision-maker.** | ### Data Ops The same journey, from data sources to analysis, becomes **an orchestrated and measurable system.** Ingestion pipelines are triggered automatically, data is versioned like code, and transformations pass through automated testing before reaching production. The data platform becomes a shared asset: there is a catalog that explains **where data resides, who is responsible for it, and which datasets are certified for use.** Dashboards display real-time data rather than snapshots of a past situation. | | --- | --- | ## Why Adopt DataOps: From Time-to-Value to Data Democratization Adopting a DataOps methodology represents a true cultural transformation aimed at **bridging the gap between data engineering and business needs.** By implementing principles derived from Agile methodologies and Continuous Integration/Continuous Deployment (CI/CD), organizations can unlock at least five key benefits.  ### Faster time-to-value Through automation, DataOps dramatically reduces data pipeline delivery times, enabling organizations to move from concept to insight **in a fraction of the time previously required.** This translates into unprecedented responsiveness to market fluctuations and greater innovation capacity across the organization.  ### More reliable data Organizations experience a substantial improvement in **data quality and reliability**, thanks to monitoring and automated testing throughout the entire pipeline. This allows anomalies and biases to be detected before they reach production systems. The approach reduces technical debt and strengthens stakeholder confidence in data-driven decision-making processes. ### Data democratization and collaboration DataOps breaks down silos between data scientists, engineers, and analysts, promoting an environment of shared responsibility. Through standardized and reusable processes, knowledge is no longer compartmentalized **but flows freely throughout the organization.** ### Operational efficiency The automation of processes and infrastructure (Infrastructure as Code) enables organizations to manage growing data volumes without a proportional increase in human resources, **optimizing costs and minimizing manual errors.** ### Integrated Governance DataOps provides governance and compliance by design. [**By embedding security controls**](https://newsroom.kireygroup.com/en/news/data-governance-in-the-ai-era-from-traditional-models-to-datagovops?hsLang=en) and lineage tracking from the earliest stages of the pipeline, organizations can ensure strict compliance with regulations such as GDPR without sacrificing agility, transforming compliance from a constraint into a competitive advantage. ## Helping Organizations Build a Data-Driven Culture Becoming a data-driven organization does not simply mean introducing new technology platforms or modernizing data integration pipelines. It means **rethinking how data is managed, governed, and leveraged** throughout the organization while simultaneously addressing **technological, organizational, and cultural** dimensions. At Kirey, [we support organizations throughout this transformation journey,](https://newsroom.kireygroup.com/en/news/data-governance-in-the-ai-era-from-traditional-models-to-datagovops?hsLang=en) helping them build **more modern and business-oriented data management models**. The goal is to make information processes more agile and efficient, improving the organization's ability to respond to market changes and support increasingly effective decision-making. Would you like to understand how to evolve data management within your organization and build a truly data-driven operating model? 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