Analistas de dados e equipes de negócios muitas vezes se chocam em BI. Como evitar esses conflitos?

To avoid conflicts between data analysts and business teams in Business Intelligence (BI), fostering effective communication and collaboration is essential. This involves ensuring clear understanding of each other's objectives, priorities, and constraints. Regular meetings and brainstorming sessions can facilitate alignment of goals and expectations. Additionally, involving stakeholders from both sides in the decision-making process and encouraging feedback loops can help bridge any gaps and build mutual trust. Establishing standardized processes and documentation methods can also promote transparency and minimize misunderstandings.

In BI different people might have different expectations. For example, data analysts focus on finding insights from data, while business teams care more about how those insights can help make decisions and improve outcomes. These different expectations can sometimes lead to conflicts because they're looking at BI from different angles. To avoid conflicts, it's important for everyone involved to communicate openly, understand each other's needs, and work together towards common goals. This means data analysts explaining their findings in a way that makes sense to the business teams, and business teams providing clear guidance on what they need from the data. When everyone's on the same page, BI projects can be more successful.

To avoid conflicts between data analysts and business teams in business intelligence (BI), focus on clear communication and understanding. Establish a shared understanding of goals and priorities, ensuring that data analysis efforts align with business objectives. Encourage collaboration and involve stakeholders from both teams early in the process to gather requirements and set expectations. Provide training and support to help business teams interpret data effectively, fostering a data-driven culture throughout the organization. Regularly review processes and feedback to address any issues promptly and improve collaboration over time.

To mitigate conflicts between data analysts and business teams in BI, it's crucial to establish a common understanding of what constitutes success. Regular communication is key to aligning on goals and definitions. Emphasize the importance of comprehensive documentation that captures agreed-upon standards, processes, and outcomes. This not only facilitates smoother collaboration but also serves as a reference point to resolve ambiguities and ensure that both teams are working towards a unified objective.

Based on my experience a conflict of interest arises only when the requirements are not clear or there is denial of responsibility or ownership or the communications between the data analyst and businesses teams are not properly documented with sign offs. To kind of tackle these issues usage of project management tools like jira, aha and confluence can be used for capturing of requirements and communication and relevant sign offs. Always involve product owner in these discussion who would be acting as the bridge between these two parties. By adhering these basic principles most of the conflicts can be avoided.

🗣️Los analistas de datos hablan en términos de modelos estadísticos y análisis predictivo, mientras que los equipos de negocios usan un lenguaje centrado en el mercado y en el rendimiento organizacional. Esta "barrera del lenguaje" puede llevar a malentendidos. 💡Solución: Fomentar la educación cruzada. Organizar sesiones de formación donde los analistas de datos aprendan sobre los fundamentos del negocio, y los equipos de negocios sobre los principios básicos del análisis de datos. Esto ayuda a crear un lenguaje común.

Bridging the communication gap between data analysts and business teams is crucial for successful BI outcomes. Adapting to business jargon and conveying complex data concepts in understandable terms is essential. Creating a data dictionary or glossary can help, but the real value lies in analysts' ability to translate data insights into actionable business strategies. By speaking the language of business stakeholders, data analysts can demonstrate how data-driven decisions can lead to tangible business benefits, fostering a culture of trust and collaboration.

In this scenario the data analyst always needs to get a clear picture on the requirements from someone who would act as a bridge I.e the business analyst to understand the business terminologies and act upon them. The same way the data analyst once creates any kpis and visuals needs to present to business and analyst and make him/her understand how these kpis would help business and why those particular kpis are being used. Initial demos on data insights and kpi selection would help both parties to come together to a common conclusion and business analyst can bridge the gap if any.

While it's common that both data analysts and business teams use different languages, it can create confusion and miscommunication that you need to avoid. To bridge this gap, it's essential to establish a common language that both parties can understand. This involves translating technical items into business-friendly language that both parties can understand. This will enable the stakeholders to resonate with their findings and clearly articulate the value proposition of data-driven insights. By providing training and resources to business teams on basic data literacy and visualization techniques can also empower them to engage more effectively with data and collaborate more seamlessly with data analysts.

When business stakeholders question or interpret data differently, it's an opportunity, not a hindrance. Embrace these moments to refine your data storytelling, ensuring it resonates with diverse viewpoints. Utilizing visualizations and dashboards can democratize data interpretation, making insights more accessible and engaging. Ultimately, your goal is to present data in a way that encourages constructive dialogue, leading to informed decisions. This approach fosters a collaborative environment where varied interpretations enhance, rather than detract from, the depth and value of the BI insights shared.

🔄Mientras que los analistas de datos pueden enfocarse en lo que los datos dicen, los equipos de negocios a menudo están más interesados en lo que los datos significan para la organización. Esta diferencia puede llevar a conclusiones divergentes a partir de la misma información. 💡Solución: Implementar sesiones regulares de revisión de datos donde ambos equipos discutan sus hallazgos y perspectivas. Esto no solo mejora la comprensión mutua sino que también puede llevar a insights más ricos y accionables.

When stakeholders address questions and concerns, it offers an opportunity to refine your data storytelling based on a business perspective. To avoid this conflict, it's essential to recognize and appreciate the strengths and limitations of each perspective. Encouraging cross-functional collaboration and leveraging the complementary expertise of both parties can lead to more holistic and well-informed decisions. Good storytelling will enable you to present and share your BI insights compellingly. As a Data Analyst, you tend to approach problems from a quantitative and analytical perspective but business teams may incorporate qualitative factors, market trends, and customer feedback, into their decision-making process.

É importante garantir que os clientes de uma visão de dados saibam utilizar as ferramentas onde os projetos são publicados, para que consigam extrair o máximo de insights e sem possíveis erros de análise por não saber interagir corretamente com um filtro por exemplo, o que pode prejudicar uma decisão de negócio.

🛠️Los analistas de datos son expertos en manipulación de datos y análisis cuantitativo, mientras que los equipos de negocios tienen un profundo conocimiento del mercado y la estrategia. La falta de apreciación por las habilidades del otro puede crear conflictos. 💡Solución: Promover proyectos colaborativos que requieran tanto análisis de datos como conocimiento del negocio. Esto ayuda a cada equipo a apreciar las habilidades y contribuciones del otro, promoviendo un respeto mutuo.

Fostering a culture of continuous learning and collaboration between data analysts and business teams is essential to bridge the skill gap in data and BI. Regular sync-up sessions or "knowledge shares" can be invaluable for mutual education, helping each team understand the other's methodologies and challenges. By engaging in such interactions, teams can gradually align their skills and vocabularies, reducing misunderstandings and enhancing their collective ability to leverage data insights effectively. Emphasizing communication and shared learning experiences reinforces a team-oriented approach, where diverse skills and backgrounds contribute to a richer, more nuanced understanding of data and its business implications.

Both Data analysts and business teams have different skills and competencies in data and BI. Data analysts use advanced data skills such as data modeling but business teams use basic skills such as communication skills that can incorporate confusion. To mitigate conflicts, it's essential to leverage the strengths of each team member and foster a culture of collaboration and knowledge sharing. By providing training and professional development opportunities to business teams, one can enhance analytical and data literacy skills. Similarly, by providing an understanding of business processes and objectives, enabling them to tailor their analyses to address specific business needs more effectively.

Data Analyst should be responsible for ensuring the structural integrtiy and the semantic integrity of the Data. Additionally, the Data Analysts must insure the Data is an accurate representation of the business. ie. Data Analysis Business Analysts are consumers of the data that provides for accurate and timely decision making based upon the high quality data the Data Analyst has curated. ie. Business Analysis I have seen confusion and conflict and poor decision making when an unskilled Business Analyst performs the role of the Data Analyst.

Cultivating a unified culture in data and BI, where the inquisitive nature of data analysts and the results-driven focus of business teams complement each other, is crucial for organizational success. Promoting a culture where every team member feels valued and heard can bridge the gap between these diverse mindsets. Implementing frameworks like data ethics and stewardship not only establishes guidelines but also encourages a shared responsibility towards data integrity and utilization. Every individual's contribution to this culture is vital, as it collectively shapes a conducive work environment where trust and collaboration are the bedrock for innovation and pragmatic decision-making.

🌍En algunas organizaciones, puede haber una cultura de "silos" donde los departamentos trabajan de manera aislada. Esta separación puede fomentar una mentalidad de "nosotros contra ellos" entre los analistas de datos y los equipos de negocios. 💡Solución: Crear oportunidades para la interacción social y profesional fuera de los proyectos. Las actividades de team building y los espacios compartidos de trabajo pueden ayudar a romper barreras y fomentar una cultura de colaboración.

I believe that a good team culture can be attributed to eliminating differences in organizational culture and norms. As data analysts have a culture of curiosity and innovation, business teams have a culture of efficiency and achieving results. To address these cultural differences, it's essential to foster a culture of innovation, collaboration, and continuous learning. You can do this by promoting the value of data-driven insights across the organization and encouraging cross-functional collaboration between data analysts and business teams. Additionally, by creating a supportive and inclusive work environment where diverse perspectives are valued and respected, organizations can minimize conflicts.

The best way I've found to avoid the clash is to align the goals of both teams around what really matters for the business. Many times, the goals of the BI team are to create X number of analyses or improve efficiency. These goals only end up with too many reports/dashboards that nobody uses. If you reframe the BI team as consultants striving to achieve the same goals and objectives as the business teams are, you gain alignment and trust between the teams.

In my experience, to avoid clash between data and business teams, communicating goals of the BI project is beneficial. Furthermore, I try to understand what is laying behind the business and their procesess and even learn them from the stakeholders or participate in related courses about their business. It is helpful in both designing insightful dashboards and comprehending their requirements.

Determining metrics: business teams know the key business drivers, but analysts bring expertise on feasible, methodologically sound KPIs. Blend business needs with analytical rigor.

When there are different teams, the clashes and conflicts in priorities are eminent. The following approaches will help in aligning the goals. - If the teams have to remain separate, create a scrum of scrums teams for better alignment and sort out dependencies. - If possible, Create one team which will work on data and BI together. The skills are not that vastly different. This will help one thinking end to end rather than one portion of the final solution. - Identify a stakeholder who can steer the coversation and set the priorities for the team(s). This stakeholder will determine the overall priorities for the Data and BI teams. This will clear the air on many fronts.