What are the differences between proprietary and open-source statistical software?

Proprietary and open-source statistical software differ significantly in terms of cost, regulatory implications, and hidden costs. Proprietary software typically requires purchasing licenses, which can be costly, especially for large teams or enterprises. Conversely, open-source software is generally free to use, reducing upfront costs. However, open-source may incur hidden costs such as the need for specialized staff for installation, customization, and maintenance. Regulatory implications also vary; proprietary software often comes with vendor support for compliance with industry standards, which is less common with open-source solutions. Users of open-source software may need to independently ensure compliance, adding to the hidden costs

Empresas que optam por usar software estatístico de código aberto geralmente têm mais flexibilidade para personalizar e modificar o software de acordo com suas necessidades específicas, o que pode resultar em economias de custos a longo prazo.

Cost implications between proprietary and open-source statistical software can vary significantly. Proprietary software often requires upfront licensing fees, which can be substantial initial cost. Proprietary software also often involves ongoing subscription or maintenance fees to access updates, support, and additional features. Proprietary software may offer more extensive customization options and integration with other systems, but these often come at an additional cost as well. Open-source software provides flexibility for customization and integration, typically without extra fees.

Proprietary Software: Typically involves upfront licensing fees and ongoing subscription costs. The pricing structure may vary based on factors like the number of users or features. Pay upfront or subscribe for access. Open-Source Software: Generally free to download and use. There are often no direct costs associated with acquiring the software. However, there may be optional support or services available for a fee. It's like getting a free coffee refill.

Proprietary software is typically developed and owned by a specific company or organization, which retains exclusive rights to the software and its source code. Users must purchase licenses to use the software and are subject to the terms and conditions set by the software provider. Open-source software, on the other hand, is released under a license that allows users to access, modify, and distribute the source code freely. Open-source licenses often have fewer restrictions, providing users with more flexibility.

A escolha entre software estatístico de código aberto e proprietário depende das necessidades e recursos específicos de cada usuário. Para aqueles que valorizam flexibilidade, customiza??o e custo-benefício, o software de código aberto pode ser a melhor op??o. Já para usuários que priorizam facilidade de uso, suporte técnico e prontid?o para uso imediato, o software proprietário pode ser mais adequado.

Proprietary Software: Users typically have limited access to the source code, if any at all. Control over customisation and modifications is often restricted to the software provider. You get what you're given. Open-Source Software: Users have access to the source code, allowing for greater transparency and flexibility. They can modify the software to suit their specific needs and contribute improvements back to the community. It's like owning the recipe book.

In my experience, open-source analytical software lives in a cloud or other managed environment, so your access problems are more on the environment, less on the analytical software. Here, I think "control" means "customization". If your software is object-oriented you should have no problems extending existing classes or building new libraries, but then your skillset among analysts may get stretched to include analytical software development. A large number of analysts are bright people who use functional development to solve a business problem; asking them to extend a class to make for an easier solve is outside their wheelhouse but accessible given time. And I've seen organizations solve a customization problem by putting it in Excel.

?Cuáles son las capacidades del equipo y cuál es el objetivo del proyecto de datos? Estas dos simples preguntas pueden proporcionar una pista sobre si se necesita flexibilidad en el código o si el software propietario es suficiente para alcanzar la meta. A menudo, los softwares de código abierto como R o Python cuentan con una amplia comunidad que puede servir como guía de control, mientras que softwares como SPSS o Stata disponen de grandes volúmenes de documentación o sistemas de soporte que pueden orientar el proyecto.

Here the software is developed and tested through open collaboration. This software is managed by an open-source community of developers. It provides community support, as well as commercial support, which is available for maintenance. We can get it for free of cost. This software also sometimes comes with a license and sometimes does not. This license provides some rights to users. The software can be used for any purpose Allows to study how the software works Freedom to modify and improve the program

This is a measurable part of the decision-making process. How much traffic does the tool get on stackoverflow or the project's own forums? A thinly-used package doesn't mean you shouldn't try it, but rather define carefully your use cases and test them out before committing to building your project on that code. In one role we were building ETLs between common drive folders and Azure Gen2 containers, and we vetted 4 different python libraries, with our final solution being a combination of two plus custom-written functions.

Proprietary Software: Support is usually provided by the vendor through customer service channels. The extent and quality of support may vary depending on the vendor's resources. Help desk on speed dial. Open-Source Software: Benefits from a large community of developers and users who contribute to forums, discussion groups, and online resources. Support is often available from multiple sources, including community forums, user groups, and professional services. Crowd-sourced genius at your fingertips, like asking the whole neighbourhood for advice.

La comunidad desempe?a un papel fundamental al embarcarse en un proyecto de datos. En este punto, creo que tanto el software de código cerrado como el de código abierto cuentan con comunidades sólidas. El software de código cerrado tiende a tener un mayor volumen de documentación ordenada y probada, mientras que el de código abierto cuenta con una comunidad colaborativa más activa.

O software de código aberto permite aos usuários acessarem o código-fonte e modificá-lo de acordo com suas necessidades, dando mais liberdade e flexibilidade para personaliza??o. Isso pode ser especialmente útil para empresas que precisam de solu??es específicas e personalizadas. Por outro lado, o software proprietário geralmente é mais seguro e tem uma equipe dedicada para corrigir bugs e fornecer atualiza??es de seguran?a regularmente. Isso pode ser crucial para empresas que lidam com dados sensíveis e precisam garantir a prote??o e integridade de suas informa??es.

Proprietary Software: Updates are typically controlled and released by the vendor. Users may need to wait for scheduled releases and pay for upgrades to access new features or bug fixes. Wait for the vendor's mercy. Open-Source Software: Updates can be more frequent and responsive to user feedback since the source code is accessible to a wide community. Users can also contribute to fixing bugs and adding new features through collaborative development. Updates as fast as pizza delivery, and you can add your own toppings.

The analogue here is to define your requirements.txt (or the equivalent) in terms of fixed release versions, not just getting the latest. Even in Python, from time to time a package released on pypi will break something in your systems, so it's best to test all updates before pushing your production.

Para algunos, la estabilidad y el soporte técnico son fundamentales, mientras que para otros, la necesidad de actualizaciones inmediatas en nuevas fórmulas u otras funciones es prioritaria. Según mi experiencia, las empresas valoran enormemente el funcionamiento óptimo, por lo que es crucial conocer las funcionalidades del software a utilizar.

Para mitigar esses problemas, é importante que as empresas de software de código aberto adotem práticas de seguran?a robustas, como realizar auditorias de código, implementar padr?es de codifica??o seguros, realizar testes de penetra??o regulares e colaborar com comunidades de seguran?a. Além disso, é fundamental que os utilizadores também desempenhem um papel ativo ao reportar vulnerabilidades e adotar boas práticas de seguran?a, como manter o software atualizado e utilizar senhas fortes.

I think that a combination of transparency and a large robust community is more conducive to software cybersecurity than mere proprietorship. The former creates the condition for a large sensitivity surface to bugs or security lapses, and leads invariably to higher levels of security for open source software. In other words, the more people are paying attention to the source code , the more sensitive hidden bugs are susceptible to being unearthed. This is in contradistinction to when the source code is locked away even if a few persons are then paid to monitor and secure it. Notably the most secure general appplication software are not necessarily proprietary, the Linux kernel being a very good example.

Proprietary Software: Security vulnerabilities and privacy concerns may exist, and users rely on the vendor to address them through updates and patches. Trust the locked vault. Open-Source Software: Transparency in the source code can enhance security as potential vulnerabilities can be identified and addressed by the community. However, the responsibility for security ultimately falls on the users or organizations utilizing the software. Like having a community watch, everyone's eyes are on guard.

Proprietary Software: User interfaces and features are often designed with a specific target audience in mind and may undergo extensive user testing and refinement. Sleek, tailored suit. Open-Source Software: User experience may vary depending on the project and community contributions. While some open-source projects prioritize usability, others may require a steeper learning curve or lack certain user-friendly features found in proprietary alternatives. DIY fashion show, sometimes chic, sometimes quirky.

I'd take this a step up the chain, ask about the entirety of the users' (plural) experience with developing, maintaining, using, showing, sharing the system. This is one reason why PowerBI is so favored, it's just very good at showing information, sharing dashboards and integrating with existing products and platforms. It cannot do everything, and I think some visuals are more compelling through packages like Seaborn, but you can't beat the ease with which you can get pretty far in PowerBI and especially to give something to an executive who only wants to know what's needed to make a decision.

A experiência do usuário é diferenciada para cada tipo de usuário. Os economistas preferem determinados tipos de SW de prateleira, como o STATA e EVIEWS. Usuários ligados a TI podem preferir SW livres ou usar as bibliotecas Python. Ent?o vai depender muito do tipo de necessidade e da profundidade do conhecimento do usuário.

Integration with existing systems: How well does the software integrate with other tools and platforms used within your organization? Long-term viability: Consider the stability and longevity of the software, including the vendor's track record or the community's activity and support. Licensing and usage restrictions: Understand the terms of use and any licensing restrictions associated with proprietary software, while open-source software typically offers more freedom in this regard. For example, SAS and SPSS are like the luxury cars of proprietary statistical software, while R and Python with libraries like pandas and scikit-learn are the adventurous backpackers of the open-source world, always exploring new frontiers.

Al elegir entre software propietario y de código abierto, es importante considerar las necesidades específicas del proyecto, el presupuesto disponible, la comunidad de usuarios y desarrolladores, así como la seguridad y la experiencia de usuario. No hay una respuesta única para todos, ya que cada opción tiene sus propias ventajas y desafíos. Lo crucial es encontrar la herramienta que mejor se adapte a tus objetivos y circunstancias únicas. Desde mi punto de vista, software como R ofrece una amplia gama de soluciones y capacidades, mientras que software como SPSS tiene una capacidad "híbrida" donde existe una interfaz de usuario amigable y también una capacidad de personalización a través de la sintaxis de SPSS

Eu percebo que há grande diferen?a está na qualidade. Existe diversos SW de prateleira que possui as mesmas capacidades de análise e cálculo, porem a qualidade de layout e de funcionalidades, s?o diferentes, independente se o SW é de código aberto ou n?o. Para analises econométricas, posso citar: STATA, EWIEWS, SPSS, MatLab e outros, como SW de prateleira, enquanto que o GEODA , Quantum, Qgis e etc, como SW livre. Em se tratando de suporte, os SW possuem suas empresas que versionam o produto, enquanto que os SW livres possuem suas comunidades. Mas a sensa??o do momento s?o as bibliotecas python como o pandas, scipy, numpy, StatsModels, matplolib, etc. Cada SW tem seu valor e serve para cada situa??o, gosto e recursos financeiros do usuário.