“Build vs Buy” is an important decision every technology strategist has to make. With the rise of open source and the wealth of freely available software, organizations have the flexibility to build custom solutions when off-the-shelf solutions don’t directly address their needs. In the domain of enterprise data platforms, many organizations have leveraged the open-source ecosystem to build tailored solutions, expending a lot of resources in the process. With the Cloudera Data Platform (CDP), powered by open-source, I argue that this “build vs buy” dilemma for organizations is an easy one to solve.
First, CDP’s capabilities, such as hybrid/multi-cloud compatibility, end-to-end data lifecycle management, multi-function analytics, and consistent security and governance, address the whole gamut of enterprise-grade needs. Second, it allows organizations to invest limited resources more strategically into building business-specific analytics applications on top of the platform to drive the right business outcomes. Third, through CDP, organizations get access to Cloudera’s committer-level expertise, ensuring not only enterprise-grade support but also lower costs when compared with building and maintaining an in-house solution. In the rest of the post below, I make a case for CDP using a simple “build vs buy” framework.
A Build vs Buy Analysis for Cloudera Data Platform over In-House Self Support
Over the last decade, open-source development has continued to gain momentum in the technology sector. The collective effort of and collaboration among thousands of developers has ensured rapid innovation. This has, in addition, also unlocked multiple benefits for organizations looking to adopt open source – lower dependency on proprietary tools and faster time to value. Benefit realization, though, lies in the execution and ability to address the following challenges:
- How to translate open-source projects into effective and performant enterprise-grade solutions?
- How to keep the solution up to date, incorporating the latest project version releases while managing interdependencies between various open-source projects?
Many organizations that have built in-house solution development capabilities tend to rely on the open-source community support for troubleshooting. Despite the in-house skills, there are significant execution challenges as evident from the Cloudera Community statistics below:
- 37% of the technical questions raised in the community typically remain unanswered.
- 78% of the answered posts indicate “no acceptable answer” as the resolution.
- Average time to first reply is about 20 hours.
- Average time to resolution is about 385 hours.
For every technology strategist, these statistics should trigger a “build vs buy” analysis, taking three key dimensions into consideration: Criticality, Skills, and Total Cost.
- What is the strategic importance of the solution in driving positive business outcomes?
- Does the solution involve deep sector-specific knowledge?
- Is the solution a source of competitive advantage?
- Are the skills required to build the solution available within and outside the organization?
- Are these skills available in sufficient capacity?
- How is the quality of skilled resources available?
- What is the total cost – direct and indirect – to build and maintain an in-house solution vis-à-vis buy one?
Across these three dimensions, CDP makes a strong case for “buy” over “build” (self-support):
- Criticality: On one hand, enterprise data platforms are foundational to foster a data-driven decision-making culture, hence they can be classified as a critical capability. On the other hand, one could argue that it’s the analytics applications built on top of the platform that drives positive business outcomes and competitive advantage. That is, an enterprise data platform is a critical enabler for success. So, an optimal strategy could be to “buy” an open, integrated, flexible, secure, and governed data platform such as CDP and “build” in-house capabilities, e.g. a Data Center of Excellence, to drive multiple analytics projects and ensure faster time to value.
- Skills: CDP has been developed on the back of Cloudera’s deep open-source expertise (600+ engineers with 250+ committers). This not only ensures out-of-the-box integration of key open source projects into a single platform but also access to world-class support organization. For example, customers using Cloudera support have experienced a 35% improvement in time to resolution vs self-support. With this level of skill available externally, organizations are better off buying CDP and maintaining a lean, in-house data platform team.
- Cost: For cost assessment, organizations should take into account not only the direct labor cost but also the cost of risk (e.g. loss due to platform downtime) and delays (e.g. project delays due to potential skill gaps). With CDP, organizations can reduce direct costs required to build, run, and maintain a data platform. In addition, access to committer level expertise allows organizations to lower downtime risk and time to issue resolution. Leveraging predictive analytics, the Cloudera support team proactively raises about 11% of tickets on behalf of customers. Further, access to Cloudera Professional Services (PS) can help accelerate the onboarding of use cases and reduce project delays. For one customer, we estimated that Cloudera PS could reduce the duration of a key project by about 50%. For the same customer, overall cost analysis indicated that transitioning from self-support(“build”) to Cloudera subscription (“buy”) could unlock savings 2-4 times the “buy” costs.
In conclusion, Cloudera Data Platform has been built with the ever-evolving enterprise needs in mind – agility, scalability, performance, integration, security & governance, and cost to list a few. Simply put, CDP is a platform that grows and evolves with the organization’s data needs – a compelling value proposition in my opinion.
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