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 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.

Pros:

• Faster results.
• Less need for sophisticated programming and workflow knowledge.

Cons:

• Designed for specific – and often very narrow – business problems.
• Requires data to be properly formatted and cleaned.

Custom-built solutions are tailored for an exact problem and leverage open source tools, such as Python and R (programming languages with extensive data science support).

Pros:

• Tailored solution that truly fits the needs.
• High quality results.

Cons:

• 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.

Interested in taking the next step? Click here to learn more about Excella’s Data and Analytics offerings.