Out-of-the-Box Data Science Platform: Buy vs. Build
Data is one of the most valuable assets to an organization. A strong data science strategy leads to novel solutions for long-standing challenges or competitive advantages that evolve an organization. What is the best approach to take full advantage of this asset? A recent emergence of out-of-the-box platforms and custom build options places executives at […]
Data is one of the most valuable assets to an organization. A strong data science strategy leads to novel solutions for long-standing challenges or competitive advantages that evolve an organization.
What is the best approach to take full advantage of this asset? A recent emergence of out-of-the-box platforms and custom build options places executives at an intersection where the best route is not defined.
Defining Out-of-the-Box Platforms vs. Custom Builds
Out-of-the-box platforms include purchased solutions ranging from drag-and-drop interfaces to managed development environments that support a data science workflow.
- Faster results.
- Less need for sophisticated programming and workflow knowledge.
- Designed for specific – and often very narrow – business problems.
- Requires data to be properly formatted and cleaned.
- Tailored solution that truly fits the needs.
- High quality results.
- Requires custom work (more time and more expense).
Major Strategy Tradeoffs
Data science platforms solve a niche business problem or opportunity. Additionally, these platforms tend to reduce solution flexibility, have limited scope and require proper data integration. Many platforms are not plug-and-play and require highly structured data. Most platforms have limited scope and may not fully solve the targeted business problem or force the use of a non-optimal approach due to current platform support.
In contrast, custom build data science solutions are more flexible and not confined to the capabilities of a pre-built platform. They are optimized for a specific business problem or opportunity. Custom solutions often require advanced expertise and a cross-functional team, plus development cycles can take longer.
Organizations may prefer a hybrid approach combing the best of “out-of-the-box” and custom solutions. In this approach, a data science team builds custom solutions augmented with pay-per-use tools. For example, a speech-to-text translation service could be purchased to reduce development time of a custom solution that requires the use of spoken audio data to produce a result.
Choosing the Best Strategy
The best strategy for an organization is often problem specific. Typically, starting with free, open source products works well. Platforms can later be considered to solve a problem, once encountered. Data science strategies, tools and platforms enable- not replace- analysts and data scientists.
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