The future of the enterprise is on the cloud. The numbers back up this claim: the global public cloud services market alone touched $312 billion in 2020, according to IDC, driven primarily by demand for shared infrastructure, data, and application resources. And yet, despite the high acclaim and well-publicized momentum to shift to the cloud, research by HIS Markit found that 74% of companies have moved a cloud-based solution back on-premises after failing to achieve the anticipated benefits.
Clearly, cloud migration is tedious and time-consuming. It is challenged by extreme complexity that often tanks well-intentioned efforts toward finding the perfect fit.
However, high-performing enterprises continue moving their data platforms to streaming data and cloud-native models, accelerating the demand for sophisticated data capabilities. Cloud data platforms help improve data consumption and achieve balance and scalability across three critical areas: DataOps, FinOps, and SecOps. These areas are foundational to the notion of XOps, a framework that integrates and harmonizes the ingestion, design, deployment, collaboration on, and understanding of data across an organization’s data infrastructure.
Why Leveraging XOps Makes Sense for Cloud Data Platforms
Without question, a robust XOps strategy integrates and streamlines the ingestion, design, deployment, collaboration on, and understanding of data across the entire pipeline. In that process, XOps addresses four critical aspects of enterprise data needs:
- Ensures agility and speed: Modern businesses are prioritizing agile data monetization to stay ahead of the competition. By leveraging XOps, they can upgrade to enterprise data pipelines, accelerate the data monetization process, leverage data insights to increase efficiency and revenues, and drive growth.
- Builds accessibility and trust: The accessibility of data inevitably depends on the organization’s ability to find and use the right data across its business units. And while it may sound easy, Forrester estimates that only 29% of enterprises are good at “connecting analytics to action.” With an XOps framework, businesses can minimize dependency on specialized teams and drive enhanced data governance, quality, and analytics.
- Improves scalability: The XOps framework can help unify the growing data landscape of an organization. It also offers the ability to integrate and scale multiple data sources, including new sources, use cases, data consumers, and more. Data operations can be streamlined and made more effective and productive.
- Drives robust governance: With a well-defined XOps framework and cloud data platform, enterprises can integrate authentication and authorization across the entire data-to-decisions journey. It also helps centralize enterprise metadata data management and scheduling and logging mechanisms. Last but not least, XOps enables the infrastructure needed for more detailed auditing of control checks and reports.
Data analytics platforms: To build or not to build
Traditionally, enterprises often opted to build data platforms and applications from scratch. This approach was based on the premise that a plug-and-play solution would not address their unique needs. However, organizations invariably lost time, money, and effort to keep pace with the evolving business and technology landscape. More often than not, a custom-built data platform will require constant maintenance as the data volume, sources, and types expand over time. Then there is also the question of keeping pace with new data types that weren’t accounted for in the original system design.
Modernizing the system to stay abreast with technology and business needs and ensure optimal data flow becomes increasingly tricky with build-it-yourself solutions. This is primarily because data sources, silos, supporting applications, and evolving technology can expand at sometimes unsustainable rates. Besides, the need for skilled resources must be considered. To maintain an internal system, businesses need ensure they have appropriately skilled resources as long as the system is in place—which can become more difficult at the system ages and its original caretakers move on. Without having the right teams doing the job, enterprises will find it difficult to address specific areas of fit-for-purpose data processing engines, new approaches to data stores, and domain-specific best practices in data governance.
The total cost of ownership of a homegrown system will be high and may involve significant overruns. Hidden costs, such as hiring or training costs for highly specialized talent to maintain the system and runaway cloud data usage expenses will add up. Then there is the question of usability. Issues related to system integrations, speed and performance, and accessibility, for example, can make it difficult for internal customers to get value from the data efficiently. Moreover, shaky platform designs can put enterprises far behind the state-of-the-art practices that characterize industry-standard data platforms.
Why it makes sense to opt for a ready-built solution
Given the many pitfalls of a build-it-yourself data platform, organizations worldwide are switching to ready-built solutions that enable more optimized data usage and offer better value from the data to users. Besides, implementing an enterprise-class DataOps platform is cheaper and requires fewer resources to deploy and maintain.
A ready-built DataOps solution includes two key elements—the end-to-end data cloud and an automated tool set for data pipelines and data products. This powerful combination offers a flexible no-code/low-code means for democratizing the data-to-decisions process. There are three key areas where the benefits of an enterprise-class DataOps platform have maximum impact:
- It makes DataOps faster, better, and more scalable. Ready-built solutions are more agile and can speed up business outcomes at scale by centralizing core aspects of big data, including governance and security, extensibility and integration, and operationalization and version control. At the same time, minimizing the need for rebuilding and retooling also brings down the technical debt that is inevitable as the data space grows and changes.
- It enhances the cost-efficiency of FinOps. Measuring the true cost of inhouse-built data platforms is often challenging as they are more costly to build and adapt to changing technologies and business dynamics. A ready-built DataOps platform, on the other hand, offers better cost visibility, requires significantly fewer people resources, and provides granular visibility and control to lower the total cost of operations.
- It offers comprehensive, reliable SecOps. Because an enterprise-class DataOps platform runs on the vendor’s virtual private cloud, it shifts responsibility for security largely to the vendor. It also controls access to data across many users and domains through data governance mechanisms, safeguards data through encryption, and offers the flexibility to adapt to regional regulations (e.g. GDPR, CCPA, etc) even when they apply to only certain data subsets.
Clearly, the combination of an end-to-end data platform with automated, enterprise-ready AI/ML toolsets make it easy for business and data science experts to collaborate and maximize value. But having the right insights and decision power is possible only when an organization has the right people, processes, and technology in place. The new class of enterprise-class DataOps platforms fits this bill.
For deeper insights on what an enterprise-class DataOps platform looks like in practice and what it takes to ensure speed and scalability, download our whitepaper Achieve Fast and Scalable Enterprise Data Operations.
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