In today’s complex data landscapes, where data flows ceaselessly from various sources, the ability to harness this data and turn it into actionable insights is a defining factor for many organization’s success. With companies generating over 50 times more data than they were just five years ago, adapting to this data deluge has become a strategic imperative. Enter DataOps, a transformative approach that empowers businesses to achieve operational excellence and stay competitive in an era of unprecedented data growth.

The Data Challenge

The digital era has ushered in an era of unprecedented data creation. The proliferation of technologies like the Internet of Things (IoT), cloud computing, and the ubiquity of Big Data has created a data explosion. Furthermore, the COVID-19 pandemic accelerated the digital transformation of many organizations, amplifying the complexity of data management. A recent survey indicates that more than 80 per cent of enterprises have adopted hybrid cloud or multi-cloud strategies, compounding the data complexity for IT teams.

As data volumes soar, the demand for efficiency in data handling becomes paramount. The need for businesses to adapt swiftly, deploy scalable solutions, and deliver superior customer experiences has never been more pressing. However, this necessitates the rapid aggregation, integration, and analysis of data from diverse sources – a challenge many organizations have grappled with, even when data was centralized within on-premises data centres. Today, data is distributed across multiple clouds and extends to the edge with IoT devices, mobile technology, and sensors. Moreover, this data needs to be accessible quickly and securely to support large, dispersed workforces.

Enter DataOps

To navigate this increasingly complex data landscape, progressive organizations are turning to DataOps teams. Borrowing from the agile methodology that has revolutionized software development, DataOps teams continuously aggregate, transform, enrich, and deliver reliable data. They often employ automated processes to enable businesses to make data-driven decisions faster. DataOps is a critical solution to the challenges of data acquisition, storage, and governance, and it offers cost-effective ways to manage vast, dynamic, and varied datasets efficiently.

“Enterprise intelligence is not measured by how much data is available, how many analytical assets are deployed, or even how much machine learning is being used. Excellence in enterprise intelligence is achieved through continuous learning, synthesis of information, and delivery of insights at scale, ”

Stewart Bond, Director of Data Integration and Intelligence research at IDC

The Evolution from DevOps to DataOps

While the concept of DevOps has been around for more than a decade, DataOps is a relatively recent addition to the technology landscape. Initially, DevOps faced resistance from IT and developers due to its revolutionary approach to software development. However, with time, DevOps has gained widespread acceptance and success, particularly catalyzed by the COVID-19 pandemic.

Yet, even with DevOps’ triumphs, it does not fully address the data-related issues that come with applications or the need to evaluate whether applications are optimally serving business objectives. The advent of multi-cloud environments, SaaS applications, data lakes, Kubernetes, and microservices architectures has complicated data pipelines. This complexity, spanning edge, on-premises, and cloud platforms, underscores the need for a DataOps approach.

“DataOps is not DevOps for data, but it is the foundation for achieving enterprise intelligence through technologies and methods that focus on quality, for consistent and continuous delivery of data value in the form of better data and decision making. DataOps success requires a technical backbone of data intelligence, control, and engineering software implemented within the context of a strong data culture in the organization.”

Stewart Bond, Director of Data Integration and Intelligence research at IDC

Like DevOps, DataOps is not a product but a cultural shift supported by existing products. While DevOps focuses on the application lifecycle, DataOps centres on the data pipeline – from data ingestion to enrichment, and through to its utilization in applications or analytics platforms. The common thread is the approach used by developers for collaboration, code storage, and management, which largely overlaps between the two approaches.

However, DataOps brings orchestration and data analytics into this technology mix, focusing on different IT and business team members, including Data Integration Specialists, Database Administrators, Data Engineers, Data Scientists, and Business Analysts. These professionals are tasked with continually enhancing the value that businesses extract from their data. While Developers and IT Operations teams are integral to DataOps, the primary emphasis is on data – its modelling, cataloguing, and analytics assets generated within the data pipeline. Similar to DevOps, DataOps seeks to achieve agility, transparency, resiliency, collaboration, and continuous improvements but with a spotlight on data.

Rethinking Data Management

Many enterprises have invested significantly in data infrastructure, including data warehouses, Hadoop-based data lakes, and data marts. However, these platforms often come with predefined and rigid data models and limited accessibility. Data warehouses primarily serve long-term storage and historical operational analysis, while data lakes are used for exploring new ways to analyze data, such as applying AI to social media or clickstreams. In both cases, data within these platforms remains largely inaccessible to anyone outside of the IT department. The same limitations extend to data ingestion tools and data pipelines, with arguably a bigger impact as pipelines may change relatively frequently without updating documentation. The net result? Large volumes of complex and poorly understood data pipelines create risk and reduce agility.

For DataOps to truly transform enterprises, this scenario must change. Data warehouses and data lakes need to become more accessible and integrated with a common set of tools, an enterprise-wide data model, automated data lineage, and catalogues of all meaningful data and analytics assets. This accessibility should extend beyond IT to cross-functional teams in business units.

Conclusion

In summary, while DevOps may have paved the way for DataOps, they are distinct concepts. DataOps is a cultural shift and process enhancement underpinned by technology. Its primary mission is to amplify the value businesses derive from data. Although both share some technological underpinnings, DataOps focuses on orchestrating data and analytics for cross-functional IT and business teams. It fosters a closed feedback loop with applications, enhancing data utilization, and, ultimately, business outcomes.

The ongoing technological revolution provides unprecedented opportunities for organizations to leverage data for strategic advantage. By analyzing and acting upon data, businesses can excel and compete effectively in an increasingly competitive landscape. DataOps creates a culture and process supported by the right technology to make this vision a reality. It empowers organizations to unlock the full potential of their data, turning it into actionable insights that drive operational excellence.

In an age where data reigns supreme, embracing DataOps is not just an option – it’s a necessity for businesses that want to thrive in the data-driven future.