登录查看更多内容

What are the most common challenges when analyzing data on a smartphone?

由人工智能和领英社区提供技术支持

Smartphones have become powerful tools for data analysis and visualization, allowing you to access, explore, and share insights from various sources anytime and anywhere. However, analyzing data on a smartphone also poses some unique challenges that you need to overcome to get the most out of your mobile device. In this article, we will discuss some of the most common challenges when analyzing data on a smartphone and how you can address them.

此文章中的业界达人

由社区从 4 条内容中精选。了解更多

Antonio Jiménez Gil

Corporate Legal Manager | NTT Data

1 Screen size

One of the obvious limitations of a smartphone is its small screen size, which can make it difficult to display and interact with complex data sets and charts. You may have to scroll, zoom, or rotate your screen to see the details, or switch between different views to compare data. To overcome this challenge, you need to design your data visualizations with mobile in mind, using simple and clear layouts, colors, and fonts, and avoiding clutter and unnecessary elements. You also need to optimize your data for mobile consumption, by filtering, aggregating, or highlighting the most relevant information, and providing interactive features that allow users to drill down or slice and dice the data as needed.

添加您的观点

Antonio Jiménez Gil

Corporate Legal Manager | NTT Data
举报内容
En mi opinión la imagen en pantalla en un dispositivo móvil va a pasar a un segundo plano. La tendencia es que los dispositivos cada vez sean más portables y las pantallas pierdan su función en favor de las proyecciones de imágenes en distintas superficies.

已翻译

赞

2 Data quality

Another challenge when analyzing data on a smartphone is ensuring the quality and accuracy of the data you use. You may encounter issues such as missing, incomplete, or outdated data, or data that is not formatted or structured properly for analysis. You may also face data security and privacy risks, especially if you use public or unsecured networks or devices to access or store your data. To overcome this challenge, you need to verify the source and reliability of the data you use, and check for any errors or inconsistencies before you analyze it. You also need to follow best practices for data protection and compliance, such as encrypting, anonymizing, or deleting sensitive data, and using trusted and secure applications and platforms for data analysis.

添加您的观点

Antonio Jiménez Gil

Corporate Legal Manager | NTT Data
举报内容
A priori el procesado de la gran cantidad de información que puede contener un dispositivo móvil va a exigir el desarrollo de un Análisis de Impacto de Protección de Datos en aras de: 1) salvaguardar los derechos e intereses de las personas y 2) cumplir con la normativa de protección de datos.

已翻译

赞

3 Data integration

A third challenge when analyzing data on a smartphone is integrating data from different sources and formats. You may want to combine data from various applications, databases, web services, or sensors to get a holistic view of your problem or opportunity. However, integrating data on a smartphone can be challenging, as you may have to deal with incompatible or inconsistent data formats, standards, or schemas, or limited or slow connectivity. To overcome this challenge, you need to use tools and techniques that can help you integrate and harmonize your data on the fly, such as APIs, cloud services, or data transformation functions. You also need to ensure that your data integration process is scalable, flexible, and efficient, and that it does not compromise the quality or security of your data.

添加您的观点

4 Data literacy

A fourth challenge when analyzing data on a smartphone is developing and enhancing your data literacy skills. Data literacy is the ability to understand, interpret, and communicate data effectively. It is essential for making informed and evidence-based decisions based on data. However, data literacy is not something that you can learn overnight, or by simply using a smartphone app. It requires ongoing practice, feedback, and learning from various sources and contexts. To overcome this challenge, you need to invest in your data literacy development, by taking courses, reading books or blogs, watching videos, or joining communities of practice that can help you learn and improve your data skills. You also need to apply your data literacy skills in real-world scenarios, by asking relevant and critical questions, exploring and testing hypotheses, and sharing and discussing your findings and recommendations with others.

添加您的观点

5 Data ethics

A fifth challenge when analyzing data on a smartphone is adhering to data ethics principles and standards. Data ethics is the branch of ethics that deals with the responsible and ethical use of data, especially in relation to its collection, analysis, and dissemination. It is important for ensuring that your data analysis does not harm or exploit others, or violate their rights or values. However, data ethics can be challenging, as you may face dilemmas or trade-offs between different ethical principles or stakeholders, or encounter situations where the ethical implications of your data analysis are not clear or agreed upon. To overcome this challenge, you need to be aware of and follow the data ethics guidelines and codes of conduct that apply to your field, organization, or project. You also need to be transparent and accountable for your data analysis process and outcomes, and respect the consent, privacy, and dignity of the data subjects and users.

添加您的观点

Antonio Jiménez Gil

Corporate Legal Manager | NTT Data
举报内容
El smartphones es el dispositivo más íntimo que puede existir hoy día. Ello hace que la sensibilidad, cantidad y valor de la información es muy alta, y en consecuencia las medidas de información y control del usuario sobre su información es piedra angular. Los proveedores deberán trabajar en la transparencia y seguridad de los procesamientos de datos en pro de los derechos de protección de datos e intimidad de los usuarios

已翻译

赞

6 Data innovation

A sixth challenge when analyzing data on a smartphone is fostering data innovation and creativity. Data innovation and creativity are the abilities to generate new and valuable insights, ideas, or solutions from data. They are essential for finding novel and effective ways to address your challenges or opportunities, or to create new value or impact from data. However, data innovation and creativity can be challenging, as you may face barriers such as cognitive biases, mental models, or assumptions that limit your data exploration and discovery, or lack of resources, support, or incentives that hinder your data experimentation and implementation. To overcome this challenge, you need to cultivate a data innovation and creativity mindset, by being curious, open-minded, and willing to learn from data. You also need to foster a data innovation and creativity culture, by collaborating with others, seeking feedback, and celebrating failures and successes from data.

添加您的观点

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Ginny Katz, MPH

Founder, CEO @ HazAdapt, Inc. Empowering Disaster Resilience in Communities with Humanity-Friendly Technology
举报内容
In high-pressure or high-stress situations like emergencies, people using smartphones for data analysis need quick, clear insights. UI designers must minimize external and cognitive load. Complex, intricate visuals hinder rather than help. Simple, high-contrast, and focused data presentation is crucial for those with limited time and attention. Avoid overloading with peripheral details to ensure rapid decision-making. Also, factor in the user's physical environment. Designs ideal in controlled settings might falter under real-world conditions. What seems playful in theory could become problematic in practical, non-ideal scenarios. Adapt design for real-world use.

已翻译

赞

Mobile Technology

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

What are the most common challenges when analyzing data on a smartphone?

1

2

3

4

5

6

7

1 Screen size

2 Data quality

3 Data integration

4 Data literacy

5 Data ethics

6 Data innovation

7 Here’s what else to consider

Mobile Technology

给文章评分

感谢您的反馈

更多Mobile Technology相关文章

更多相关阅读内容

What are the most common challenges when analyzing data on a smartphone?

1

2

3

4

5

6

7

1 Screen size

2 Data quality

3 Data integration

4 Data literacy

5 Data ethics

6 Data innovation

7 Here’s what else to consider

Mobile Technology

给文章评分

感谢您的反馈

查看其他技能