You’re building a data analytics team. How do you ensure they’re up to the task?

由人工智能和领英社区提供技术支持

Data analytics is a crucial skill for any organization that wants to leverage data to make better decisions, optimize processes, and create value. But building a data analytics team is not a simple task. You need to consider the goals, skills, roles, tools, and culture of your team to ensure they can deliver high-quality results. In this article, we will share some tips on how to build a data analytics team that is up to the task.

此文章中的业界达人

由社区从 6 条内容中精选。了解更多

Rashmi Narula

Manager Data Analytics | BI Consultant |Tableau Expert | Visualization Consultant | Technologist | Strategist & Executor
Tiago Custódio de Oliveira

AWS | ETL | DAX | Tableau | Power BI | SharePoint | SQL | Python | IA | VBA
Florent Lefebvre

Social Data Analyst Freelance | Via la data, j'analyse les réseaux sociaux pour répondre à vos questions marketing…

1 Define your goals

Before you start hiring or assigning people to your data analytics team, you need to have a clear vision of what you want to achieve with data. What are the business problems or opportunities that you want to address with data? What are the key questions or hypotheses that you want to test with data? What are the metrics or indicators that you want to track with data? Having a clear and specific goal will help you align your data analytics team with the needs and expectations of your stakeholders, as well as prioritize and scope your data projects.

添加您的观点

Rashmi Narula

Manager Data Analytics | BI Consultant |Tableau Expert | Visualization Consultant | Technologist | Strategist & Executor
举报内容
Provide tailored training programs for skill development. Encourage continuous learning through workshops and courses. Conduct regular skill assessments and offer support for improvement. Facilitate mentorship opportunities for guidance and advice. Foster a collaborative environment for knowledge-sharing. Establish a feedback loop for performance evaluation. Ensure access to necessary tools and resources. Set clear goals aligned with organizational objectives. Recognize and reward achievements to motivate the team.

已翻译

赞
Tiago Custódio de Oliveira

AWS | ETL | DAX | Tableau | Power BI | SharePoint | SQL | Python | IA | VBA
举报内容
To ensure your data analysis team is up to the task, focus on hiring individuals with relevant skills and experience, provide ongoing training and professional development opportunities, foster a collaborative and supportive work environment, set clear expectations and goals, encourage open communication and feedback, and regularly assess performance and address any gaps or challenges that arise.

已翻译

赞
Parth Gandhi

Senior Data Analyst @ TCS | QlikSense | SQL | Microsoft Power BI | ETL | Python | Masters in Data Science | Decoding Complex Data for Actionable Insights
举报内容
Recruit diverse talent with relevant skills in data analysis, statistics, and programming. Provide comprehensive training on tools, techniques, and industry best practices. Foster a collaborative environment for knowledge sharing and skill development. Establish clear goals, expectations, and performance metrics. Encourage continuous learning through workshops, courses, and certifications. Support experimentation and innovation to explore new approaches and technologies. Provide access to resources, data, and tools necessary for success. Foster a culture of continuous improvement. Regularly review team performance and provide constructive feedback. Encourage cross-functional collaboration with other departments for holistic insights.

已翻译

赞
Florent Lefebvre

Social Data Analyst Freelance | Via la data, j'analyse les réseaux sociaux pour répondre à vos questions marketing, com de crise, ou stratégie de croissance | Cartographies les réseaux sociaux |
举报内容
Une analyse commence toujours par une question. La donnée et la méthodologie doivent arriver après. La question est la clef de tout, et c'est via elle que l'on doit définir la méthodologie la plus simple possible, avec le minimum de donnée possible, pour pouvoir y répondre efficacement. Ce n'est pas parce que l'on a déjà une méthode établie, avec des outils/données sur l'étagère, que l'on doit les réutiliser pour toutes nouvelles questions. Le recyclage est un bonus, pas une nécessité.

已翻译

赞

2 Assess your skills

Once you have defined your goals, you need to assess the skills and competencies that your data analytics team will need to achieve them. Data analytics is a broad and diverse field that requires a combination of technical, analytical, domain, and communication skills. Depending on the complexity and scope of your data projects, you may need different levels of expertise in data engineering, data science, data visualization, data governance, and data literacy. You also need to consider the existing skills and gaps of your current staff, as well as the availability and cost of external talent. Based on this assessment, you can decide how to recruit, train, or outsource your data analytics team members.

添加您的观点

Florent Lefebvre

Social Data Analyst Freelance | Via la data, j'analyse les réseaux sociaux pour répondre à vos questions marketing, com de crise, ou stratégie de croissance | Cartographies les réseaux sociaux |
举报内容
Une fois que l'on a défini une question purement "métier", vient la création de la méthode pour y répondre. Cette méthode a besoin de deux niveaux de compétences pour être établie : - Connaitre assez son sujet, avec ces possibilités, son état de l'art, pour connaitre les indicateurs qui y répondront. - Plus que de connaitre leurs existences, savoir installer et utiliser les outils de la méthodo. Le premier niveau peut être rapidement comblé si besoin. Cela ne nécessite "que" de connaitre le secteur. Le deuxième est plus complexe, avec une compétence technique précise et du savoir faire. Peut nécessiter formation, embauche, ou bien de revoir une méthode moins optimisée mais plus approprié aux compétences présentes.

已翻译

赞

3 Assign your roles

After assessing your skills, you need to assign the roles and responsibilities of your data analytics team members. Depending on the size and structure of your organization, there are various models for organizing your data analytics team, such as a centralized model with a dedicated team serving the whole organization, a decentralized model with data analytics teams embedded in different business units or functions, and a hybrid model combining centralized and decentralized teams. It's important to consider the pros and cons of each model - such as the level of coordination, collaboration, autonomy, standardization, and innovation - and choose the one that best suits your goals, culture, and resources. There is no one-size-fits-all solution for organizing your data analytics team.

添加您的观点

4 Choose your tools

Another important aspect of building a data analytics team is choosing the tools and platforms that your team will use to collect, store, process, analyze, and visualize data. There are many options available in the market, ranging from open-source to proprietary, from cloud-based to on-premise, from low-code to high-code, and from general-purpose to specialized. You need to evaluate the features, benefits, costs, and compatibility of each tool and platform, and select the ones that meet your requirements, budget, and preferences. You also need to ensure that your team has the necessary skills and training to use the tools and platforms effectively and efficiently.

添加您的观点

Florent Lefebvre

Social Data Analyst Freelance | Via la data, j'analyse les réseaux sociaux pour répondre à vos questions marketing, com de crise, ou stratégie de croissance | Cartographies les réseaux sociaux |
举报内容
Je n'apprécie pas la manière de présenter le "choix des outils" par LinkedIn. Le choix des outils ne DOIT PAS être une sélection comptable sur catalogue. Pour être efficace, l'analyse doit répondre à une question via une méthodologie, et les outils sont des parties intégrantes de cette méthodologie. Bien s?r, "les fonctionnalités, les avantages, les co?ts..." doivent être à considérer comme des contraintes. Mais qui sont secondaires sur la question "Quel est le meilleur outil pour répondre à ma question ?". Et si une des contraintes est bloquante, on passe au 2nd meilleur... Mais on ne fait pas son marché pour prendre le "meilleur" outil (qui n'existe pas). On sélectionne la brique la plus appropriée pour la méthode d'analyse choisie.

已翻译

赞

5 Cultivate your culture

Finally, building a data analytics team is not only about the hard skills and tools, but also about the soft skills and culture. You need to cultivate a culture of data-driven decision making, where your team and your organization value data as a strategic asset, use data to support and challenge ideas, and learn from data to improve and innovate. You also need to foster a culture of collaboration, where your team and your organization share data, insights, and feedback, and work together to solve problems and create value. And you also need to nurture a culture of ethics, where your team and your organization respect data privacy, security, and quality, and use data responsibly and transparently.

Building a data analytics team is a challenging but rewarding endeavor. By following these tips, you can ensure that your team is up to the task of delivering data-driven solutions and value for your organization.

添加您的观点

6 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

Data Analytics

+ 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

You’re building a data analytics team. How do you ensure they’re up to the task?

1

2

3

4

5

6

1 Define your goals

2 Assess your skills

3 Assign your roles

4 Choose your tools

5 Cultivate your culture

6 Here’s what else to consider

Data Analytics

给文章评分

感谢您的反馈

更多Data Analytics相关文章

更多相关阅读内容