登录查看更多内容

You need to make sense of complex data sets. What tools can help you see the bigger picture?

由人工智能和领英社区提供技术支持

As a marketing researcher, you often have to deal with large amounts of data from various sources, such as surveys, web analytics, social media, and customer feedback. How can you analyze, visualize, and communicate this data effectively and efficiently? In this article, we will introduce you to some of the tools that can help you see the bigger picture and make better decisions based on data.

此文章中的业界达人

由社区从 15 条内容中精选。了解更多

Fernando Weydt

Head de Negócios | Dados, Marketing, CRM e Tecnologia
Ankesh Saxena

Co-Founder and CBO - Acutus AI Insights | Podcaster @ The Collaborative Canvas | TRP Magazine | Learn and Grow | Market…
Beata Biernacka

Research Manager | Project Manager | Insights Manager | Data Analyst | ex-HBO | ex-Nielsen | ex-Discovery

1 Data analysis tools

Data analysis tools are software applications that allow you to manipulate, transform, and explore data in different ways. They can help you perform tasks such as cleaning, filtering, sorting, aggregating, calculating, and modeling data. Some of the common data analysis tools are Excel, SPSS, R, Python, and SQL. Depending on your level of expertise and the complexity of your data, you can choose the tool that suits your needs and preferences. For example, Excel is a widely used tool that offers basic functions and features, while R and Python are more advanced tools that require coding skills and offer more flexibility and power.

添加您的观点

Fernando Weydt

Head de Negócios | Dados, Marketing, CRM e Tecnologia
举报内容
When we're talking about data, people, process and tech MUST HAVE the same weight. You can be a star coding, but without a great process and a team that you can count on, you have nothing. Going straight to the point: to analyse your data, you need to have different points of view - a heteregon team will be necessary. A process that guarantees the full vision of business. And obviously: enough tools to allow you to do your job.

已翻译

赞
Ankesh Saxena

Co-Founder and CBO - Acutus AI Insights | Podcaster @ The Collaborative Canvas | TRP Magazine | Learn and Grow | Market Research | Personal Branding | Consumer Insights | Sales | ESOMAR member | MRSI Member |
举报内容
To comprehend complex datasets, tools like Tableau or Microsoft Power BI offer dynamic visualizations for a holistic view. Data analysis platforms such as Python with libraries like Pandas and NumPy enable in-depth exploration and interpretation. Statistical tools like R or IBM SPSS help uncover patterns and trends. Additionally, machine learning tools like TensorFlow or scikit-learn can uncover insights in large datasets. These tools collectively aid in gaining a comprehensive understanding of complex data sets.

已翻译

赞
Beata Biernacka

Research Manager | Project Manager | Insights Manager | Data Analyst | ex-HBO | ex-Nielsen | ex-Discovery
举报内容
In regards to data analysis tools I like using R and SQL. I especially like R as the structure of the language is clear and simply, and it has a substantial library of packages well prepared for data cleaning, manipulation, analysis and visualization.

已翻译

赞
Juan Carlos Esguerra

CEO @ SUMMA CAR P DIEM BONAZENS RESORT | Sales Manager for EMEA & LATAM
举报内容
To understand complex data, various tools can help. Spreadsheet software like Excel or Google Sheet is good for basic tasks. Statistical software (R, Python) allows for deeper analysis. Business Intelligence. tools (Tableau, Power BI) are user-friendly for visuals. SQL helps with database queries, while data mining tools (RapidMiner) find data patterns. Big Data platforms (Hadoop) handle large data sets. The right tool depends on your data size, analysis complexity, and skill level. Ultimately, the quality, truthfulness of data, and the analysis's context matter most.

已翻译

赞
Alexandra Silva
举报内容
The tools depends on what you want to achieve and the level of complexity. For basic and quick analysis, excel is more than enough. For data visualization Power BI, tableau, Qlik. For statistical analysis tools for Data Science such as R, python.

已翻译

赞

加载更多内容

2 Data visualization tools

Data visualization tools are software applications that allow you to create charts, graphs, maps, dashboards, and other visual representations of data. They can help you discover patterns, trends, outliers, and relationships in your data, as well as communicate your findings and insights to your audience. Some of the popular data visualization tools are Tableau, Power BI, Qlik, Google Data Studio, and Infogram. These tools vary in their ease of use, functionality, customization, and integration with other platforms. For example, Tableau is a user-friendly tool that offers a wide range of options and interactivity, while Google Data Studio is a free tool that works well with Google products and services.

添加您的观点

Beata Biernacka

Research Manager | Project Manager | Insights Manager | Data Analyst | ex-HBO | ex-Nielsen | ex-Discovery
举报内容
I like visualization done in R. I know it is not that easy and fast to do that. It needs time and deliberation to make a really nice visualization. But there are multiple options to make the visualization clean and clear and I believe it is worth the effort.

已翻译

赞
Cesar Yanapa Zenteno

Gerente General / Finanzas / Marketing & Gestión Comercial
举报内容
Durante mucho tiempo, construí bases de datos llenas de información, muy útil, pero que al no ser entendible para muchas personas me llevo a la conclusión que puedes tener toda la información del mundo pero si esta no habla sola (no transmite conocimiento a quien la necesita) no sirve, se vuelve inservible. Power BI ha sido la más eficiente en construir y aportarme una visualización de datos que ayuden a la toma de decisiones con base y una sólida convicción de seguridad.

已翻译

赞
Alexandra Silva
举报内容
For me Power BI. It's very easy to use and learn, for those without any previous knowledge in data modelling. It has also free license. For starters it's very good.

已翻译

赞
Rodrigo De Lacerda Pimentel

Business Partner | Business Analytics | Statistical Data Analysis | Data Science
举报内容
For analysis R (ggplot2) for sure, you can you to explore or for presentations. For reports Tableau or Shiny then you can automate and have dynamic report.

已翻译

赞

3 Data storytelling tools

Data storytelling tools are software applications that allow you to combine data, visuals, and narratives into engaging and compelling stories. They can help you convey the meaning, context, and implications of your data, as well as persuade and influence your audience to take action. Some of the examples of data storytelling tools are Storyteller, Flourish, Datawrapper, and Piktochart. These tools enable you to create interactive and dynamic stories that can be shared online or offline. For example, Storyteller is a tool that guides you through the process of crafting a data story, while Flourish is a tool that lets you animate your data and visuals.

添加您的观点

Alexandra Silva
举报内容
Bookmarks of Power BI. You can also create reports and dashboards accordingly with datastorytelling best practices. you can also use smart narratives functions.

已翻译

赞
Rodrigo De Lacerda Pimentel

Business Partner | Business Analytics | Statistical Data Analysis | Data Science
举报内容
I believe that a good storytelling comes with a good storyteller, in that way presentations still the best. Build presentation is so important as make the analysis. Remember influence is very important for a data professional.

已翻译

赞

4 Data management tools

Data management tools are software applications that allow you to store, organize, access, and secure your data. They can help you ensure the quality, consistency, and availability of your data, as well as protect your data from unauthorized use or loss. Some of the types of data management tools are databases, cloud services, data warehouses, and data governance tools. These tools differ in their scope, scale, and cost. For example, databases are systems that store and retrieve data in a structured way, while cloud services are platforms that offer data storage and processing over the internet.

添加您的观点

5 Data integration tools

Data integration tools are software applications that allow you to combine data from different sources, formats, and systems into a unified view. They can help you eliminate data silos, reduce data errors, and enhance data analysis and visualization. Some of the common data integration tools are ETL tools, API tools, and data connectors. These tools have different capabilities and requirements. For example, ETL tools are tools that extract, transform, and load data from various sources to a destination, while API tools are tools that use application programming interfaces to exchange data between applications.

添加您的观点

6 Data science tools

Data science tools are software applications that allow you to apply scientific methods, algorithms, and techniques to data. They can help you generate insights, predictions, and recommendations from data, as well as automate and optimize data-driven processes. Some of the examples of data science tools are machine learning tools, artificial intelligence tools, and natural language processing tools. These tools are often complex and sophisticated, and require specialized skills and knowledge. For example, machine learning tools are tools that use statistical models and algorithms to learn from data and make decisions, while artificial intelligence tools are tools that use machine learning and other methods to simulate human intelligence and behavior.

添加您的观点

Rodrigo De Lacerda Pimentel

Business Partner | Business Analytics | Statistical Data Analysis | Data Science
举报内容
For data science the most complete tool is Python (regarding integration after modeling). As statistician I do prefer work with R.

已翻译

赞

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

Rodrigo De Lacerda Pimentel

Business Partner | Business Analytics | Statistical Data Analysis | Data Science
(已编辑)
举报内容
We have many different tools for many different needs. The tool decision is very personal, try to find the best (or combination) that works for you.

已翻译

赞

Marketing Research

+ 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

You need to make sense of complex data sets. What tools can help you see the bigger picture?

1

2

3

4

5

6

7

1 Data analysis tools

2 Data visualization tools

3 Data storytelling tools

4 Data management tools

5 Data integration tools

6 Data science tools

7 Here’s what else to consider

Marketing Research

给文章评分

感谢您的反馈

更多Marketing Research相关文章

更多相关阅读内容