When engineering teams prototype or build new systems, they start capturing simulation or sensor data. The next thing they want to do is dive in the data, making visualisations and analysis. At this moment, the discussion often sparks again: should we buy an external tool, or build one ourselves? In this blog post, we briefly dive into the advantages of both choices.

Why build: tailoring to your exact needs

Experienced engineers have a habit of turning to custom scripting in Python or Matlab. From there, it’s a natural step to expand this into a proper internal tool for the entire team or company. Building your own data analysis tool has three key advantages:

• Flexibility: When developing complex systems like electric engines or airplanes, the way you test and analyse performance for your product is unique. Building your own tool ensures it's perfectly tailored to your specific needs and design choices. For example, your tool can be focused on handling data from a specific file format, database or message broker.‍
• Specific Calculations: Complex systems require specific data calculations. Custom-built tools can make specific computations:
  1. Extracting and processing specific data samples based on complex conditions (e.g. ground effect on lift-off for airplanes, or a tyre model calculation for cars)
  2. Custom data interpolations between timestamps for your specific use case‍
• Independence: Using an external tool means relying on another company, which can lead to issues if you need to comply to specific legal requirements, or they require the data to be stored in a public cloud. Building your own tool provides control, though it comes with its own challenges. For example, you need to handle data security yourself, making sure every engineer can only access the data they have permission to see.

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Building yourself gives you total freedom to have tailored-made solution. But it also comes with the responsibility to manage the team developing the internal tooling, and making sure data access happens securely.

Why buy: how spending money can save you money

Building in-house tooling sounds an appealing side-project, but engineering managers often overlook the advantages of buying external tooling. On the face, this looks like an extra cost, but due to the increased team efficiency ends up saving money.

There are a lot of options to choose from, ranging from local desktop applications for individual use to cloud-based solutions for entire teams.

Here are the benefits of purchasing data analysis software:

1. Time Efficiency: Custom scripting in Python or Matlab can be time-consuming. Engineers frequently need to update scripts for new data requirements, wasting valuable time. Platforms like Marple allow for quick data visualisation and manipulation, saving significant time compared to scripting.
2. Skill Discrepancy: Not all engineers are equally skilled in coding. This leads to reliance on a few skilled individuals, like our homage to "Barbara" an engineer who end up overwhelmed with requests for help with scripts and visualisations. Purchased software often comes with a well design user experience, democratising the access to data inside the team.
3. Maximise Teamwork: Most engineering teams have a tendency to work a lot more isolated than other departments. This is largely due to the result of their work being tied to their own computer. Purchasing a tool can bring the data to a central location, making it easy for people to collaborate. This does not necessarily mean data has to be stored in the cloud, a lot of on-premise data solutions exist as well.
4. Building and Maintenance Overhead: Creating and maintaining a custom tool internally requires significant time and resources. It can take months to develop a tool, and ongoing maintenance often falls to the original creator, who may eventually leave, taking crucial knowledge with them. This results in outdated, unsupported tools, with heavy hidden costs. When buying an external tool, this is all included in the price.

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Buying an external tool can increase efficiency, enhance teamwork and democratise access to data for everyone. It avoids hidden cost of maintaining internal tooling, which are often underestimated.

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Conclusion

Choosing between building a custom data analysis tool and buying an existing solution depends on your specific needs, resources, and long-term goals.

For many organisations, a combined approach can be effective: leveraging a powerful commercial tool for general analysis needs while supplementing with custom scripts or modules for specialised tasks. This strategy can provide the best of both worlds, combining the efficiency and reliability of purchased software with the tailored precision of in-house solutions.

Another option is to go with Marple’s hybrid approach. You buy our pre-made powerful tool, but it can be customised to fit your specific data storage, calculations, or analysis features.

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‍Schedule a meeting with our experts to explore how Marple can meet your specific needs.

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