Toggle Menu

Insights > Artificial Intelligence (AI) > Out-of-the-Box Data Science Platform: Buy vs. Build

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…

By

May 21, 2019

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.

You Might Also Like

Excella

Keeping Your Organization Future Focused

Twenty years ago, Excella started with the belief that organizations could realize their futures through...

Excellian Spotlights

Excella Innovators: Emily Gardner

Meet Emily Gardner: Engagement Manager at Excella. Emily talks about what made her switch to...

Excellian Spotlights

Excella Innovators: James Greene

Meet James Greene: Capabilities Lead and Project Management Lead Consultant. James talks about his consulting...