What metrics should you track to improve your Business Intelligence (BI) skills and competencies?

Ensuring the quality of data is vital for BI projects, covering accuracy, completeness, consistency, timeliness, and relevance. Inadequate data quality can result in misleading outcomes, resource wastage, and credibility damage. Enhance data quality by adopting best practices like validating, cleaning, standardizing, and enriching data sources, accompanied by comprehensive documentation of data definitions and processes.

1.)Create a project plan with clear tasks and assign ownership. 2.)Let team members know what is expected from their work efforts. 3.)Have regularly scheduled team meetings to discuss the project. 4.)Have 1:1 meetings to discuss with the individual team member their work efforts. 5.)Use collaborative tools: Slack, Google Drive, Atlassian, etc

Communication skills: to be able to sell your idea and interpreation of the data to your stakeholders. Business Financial knowledge: to be able to understand the business financial health and how this influence internally and externally. Visualization of data: mastering good tools for visualizing data enable you to evidence and others comprehend the data.

Data quality is it. The rest are provided through proper ai output. Too many go before gathering proper data. Which makes 2-7 horrible

My top 3 if it comes to data quality: 1. Data Accuracy: - Metric: Error Rate - Main Fact: Lower error rates indicate higher data accuracy, contributing to more reliable BI insights. 2. Data Completeness: - Metric: Missing Values - Main Fact: Minimize missing values to ensure the completeness of your analysis. 3. Data Timeliness: - Metric: Data Latency - Main Fact: Track data latency to ensure timely access, preventing outdated information in BI insights.

In my experience, maintaining simplicity in dashboards is crucial, emphasizing a thematic approach. Each dashboard should center around a specific theme and maintain a focused structure. For e.g, if your dashboard includes 3-4 KPIs related to sales, product mix, then your line graphs/bar charts should also contribute to providing additional insights for the KPIs presented on the dashboard. Excessive information can lead to confusion. Therefore, consider strategically dividing your components into 2 or more dashboards based on logical criteria. This approach ensures a clearer presentation and enhances the overall effectiveness of data visualization. Wherever possible, include a concise summary to provide a quick overview.

The main goal of any dashboard is to look simple, & easy to understand. Else, I've seen dashboards not being used because they're too bad at visual appeal. Additionally, every dashboard should tell a story, & should have functionalities to unfold the entire story. A lot of times dashboards are built to show a particular number, but no more depth as to why that number is the way it is. Super crucial for the dashboard to be effective.

O principal objetivo de um dashboard é informar objetivamente os principais indicadores do negócio e fornecer uma rápida informação sobre tendência e classificação dos números. Claro que vão existir alguns formatos de visualização mais recomendados que outros, cores, temas... Mas se posso deixar uma dica sobre visualização de dados é: em um certo momento, solicite que seu cliente adapte a visualização da forma mais útil para ele e deixe que ele defina como os dados serão exibidos.

Keep it simple as much as you can. With a global KPI overview and a more deep-dive oriented focus. Always better to have a short list of smart KPIs than a huge list.

One time at work, we were analyzing flight delays for a major airline. We knew the data was key to optimizing schedules and customer experience, but the traditional bar charts weren't helping. The sheer volume of flights overwhelmed the visuals, making it hard to spot patterns and trends. That's when I decided to experiment with animated heatmaps. By layering origin and destination cities over time, we could instantly see which routes were experiencing the most delays, at what hours, and how they cascaded across the network. That experience taught me the power of data visualization. It's about understanding your audience, choosing the right visual format, and telling a compelling story with the data – that's the true metric of BI success.

In my experience, honing data analysis skills proved pivotal in unraveling insights crucial for effective decision-making. Engaging with diverse tools like SQL, Python, R, Excel, and Power BI enriched my ability to dissect data comprehensively. Employing varied analytical approaches, including descriptive, diagnostic, predictive, and prescriptive analytics, allowed me to glean actionable insights, test hypotheses, and derive substantial value from the data, steering strategies and enhancing outcomes.

Um profissional de BI não pode analisar dados de forma fria, mas sim buscando intensamente apoiar a tomada de decisão. Dessa forma, a habilidade de análise de dados deve estar aliada a um olhar crítico sobre o negócio. Exercitar o "olhar de dono" é uma boa dica para potencializar o valor do BI para o corpo executivo de uma empresa. Ou seja, deve existir a capacidade de racionar se colocando no lugar de quem toma as decisões para o negócio.

Dans mon métier, l'analyse de données a une importance cruciale. En effet, les stratégies digitales les plus performantes sont celles qui se veulent "DATA DRIVEN". L'analyse de données, dans le secteur du marketing digital, permet par exemple de comprendre le comportement des clients sur les canaux à travers lesquels on interagit avec eux. Elle est surtout utile pour identifier les leviers d'acquisition les plus rentables, déterminer les produits les plus performants, ou encore découvrir des segments de clients ou des marchés à développer. Pour exceller dans l'analyse de données en marketing digital, il est essentiel de maîtriser des outils comme Python, R, Excel, Power BI, SQL, mais aussi Google Analytics ou des outils équivalents.

Para uma análise de dados consistente, é importante definir quais são as métricas que devem ser acompanhadas para mensurar o impacto das ações nos resultados. Essas análises dependem de um profundo conhecimento de negócio, alinhadas ao conhecimento técnico que viabilize a entrega de informações de valor através de dados bem tratados, categorizados e íntegros. Na minha experiência, ferramentas como SQL, Python e Power BI. Proporcionam muitas possibilidades de criar parte de um ciclo de vida para os dados, garantindo uma entrega de valor.

You have the Data, then what? Converting raw data into actionable insights is the ultimate way to process the data. There are two major things to answer from analysis; prove the hypothesis and also provide recommendations from the conclusions. Sometime we do not need an advanced analysis process to answer the question, need you to assess carefully. But on the other hand, advanced analysis is mandatory to solve complex situation that consider many things

When tracking metrics to enhance Business Intelligence (BI) skills and competencies, it's important to consider aspects related to data storytelling. Here's an explanation and metrics to track in this context. - Clarity of Message - Engagement - Relevance to Audience - Actionable Insights

Como gerente de planejamento em concessionárias de caminhões, acompanho de perto o impacto das habilidades de storytelling de dados. Integrar dados, visuais e narrativa é crucial para contar histórias envolventes. Aprimorar essa habilidade envolve criar arcos de história claros, usar emoção e empatia, adaptando a narrativa ao público e contexto. Essa métrica reflete diretamente nossa capacidade de conectar, transmitir mensagens e inspirar ações no dinâmico ambiente das concessionárias.

In my experience Story telling is a very underestimated skill that organisations should invest on because : 1- Whether it is for activating usage of a simple preconfigured metric or for understanding of a new published context. None of Data strategic insentives can be deployed without a translation from data to a valuable asset for the company business. 2- Every role in the company should understand how the usage or generation of new Data participates on global company strategy and target throughout attractive Story Telling. Story telling can ease the understanding of what is a data centric organisation and promote the move into a Data Mesh alike organisation which allows faster decision making and innovative business strategies.

It’s important to stay true to the mission of the dashboard you’re creating. Take four or five KPIs and develop visualizations that will support that dashboard’s mission. Within the first 10-15 seconds, your dashboard should answer four analytic questions that will tell a story to the end user.

Make sure that whatever data story you are telling is Accessible to all consumers. Take note of the use of color, iconography, language and layout. The right story is one that is easy to tell, and easy to understand. I recommend chatting with anyone with a UI/UX background to get ideas/feedback or go learn about UI/UX yourself! :)

I find the most challenging aspects of data literacy for people to learn are 1) distinguishing signal from noise and 2) distinguishing causation and correlation - these two concepts, when misunderstood, lead to over or under-reacting to data - such as missing a key insight or causing a fire drill for no good reason.

[1] Track Technical Skills: [BI Tools and Platforms] [2] Business Understanding: [Business Case Studies, Marketing] [3] Continuous Learning for Communication and Storytelling Ensure a strong understanding of how effectively you comprehend and communicate data concepts, particularly to those without a technical background. Track your progress in making data understandable for everyone in your team or company. For instance, after conducting a data training session, assess participants' ability to apply their learnings in their work. Are they now more comfortable interpreting charts and graphs?

Interestingly enough, Data Literacy is not just a Data issue, this also can very well be a process or cultural challenge. This fact highlights the importance of process refinement and user collaboration required to build your data strategy vs focusing only on technology.

One thing I found helpful is to understand the data lineage and their pattern.It allows companies to 1.Track errors in data processes. 2.Perform system migrations with confidence. 3.Combine data discovery with a comprehensive view of metadata, to create a data mapping framework.

To boost data literacy, master key data concepts and tools, and enhance your data interpretation and critical thinking skills. Focus on creating effective data visualizations and communicating insights clearly to diverse audiences. Ensure accurate data collection and governance, and apply data-driven analysis in decision-making. Engage in continuous learning, keeping up with industry trends, and collaborate effectively in data-centric projects. This approach combines technical expertise with analytical thinking and communication, essential for informed decision-making.

Métricas relacionadas à conformidade ética, como auditorias regulares e monitoramento de acesso aos dados sensíveis, são essenciais. Em minha experiência, o estabelecimento de práticas rigorosas de ética de dados não apenas assegurou conformidade com regulamentações, mas também fortaleceu a confiança na integridade e segurança dos dados.

Como gerente de planejamento em concessionárias de caminhões, monitoro atentamente a conscientização das habilidades éticas de dados. Aplicar princípios éticos na coleta, uso e compartilhamento de dados é essencial. Aprimorar essa métrica envolve compreender estruturas legais, padrões de governança de dados e avaliar riscos e benefícios. A ética de dados não apenas protege a privacidade e os direitos dos titulares, mas também evita preconceitos, discriminação e danos, contribuindo para uma abordagem responsável e sustentável no dinâmico ambiente das concessionárias.

La ética de datos es esencial para la confianza, credibilidad y cumplimiento legal de su organización. Una auditoría de ética de datos puede considerar: ✔ Códigos, políticas, procedimientos y normas de ética de datos existentes y aplicados. ✔ Prácticas comerciales éticas, transparentes y responsables con los stakeholders y la prevención y resolución de conflictos de interés. ✔ Revisión del sistema de información, procesos, controles, indicadores y reportes para asegurar la calidad, integridad, seguridad, confidencialidad, disponibilidad y uso correcto de los datos. ✔ Evaluación de los riesgos y beneficios de las acciones y decisiones de datos, y el balance entre los intereses de la organización y los afectados.

A ética de dados é definida como a aplicação de princípios e valores éticos na coleta, uso e compartilhamento de dados. Essa prática é essencial para proteger a privacidade, segurança e direitos dos titulares dos dados, além de evitar preconceitos, discriminação e danos.

Los datos se deben revisar y auditar de forma periódica. Debe existir siempre una área de gobierno de datos maestros, ya que son la base de todo lo que sigue con los diferentes sistemas de gestión. Deben haber políticas muy claras y definidas, que permitan establecer los diferentes procedimientos para la obtención de datos, la procedencia de estos, el uso que se les dará, quienes los usarán, donde se usarán y el almacenamiento de estos. No pueden haber fugas de datos, y tampoco pueden haber datos sin una procedencia sustentada y documentada.

All the aforementioned points carry significant weight in ensuring the success of a BI program. Among them, the prompt availability of information holds particular importance. The BI platform should serve as the primary source for reliable and pertinent information. However, any delay in disseminating this information may lead users to seek alternative means, ultimately diluting the impact of the BI initiative.

Para excelência na análises dos dados entendo ser importante alinhamento detalhado entre a gestão estratégica e tática, com foco no planejamento estratégico de 10/20 anos. Desta forma a arquitetura de dados deve ser desenvolvida, enriquecida e criticada constantemente, assim teremos melhores ferramentas de BI para a gestão dos negócios. Por experiência própria, desenvolver arquitetura para subsidiar decisões de curto prazo, no futuro darão mais trabalho do que montar uma estrutura com horizonte mais avançado, afinal as principais métricas são iguais pra todas empresas, mas os detalhes que farão toda a diferença naquele mercado / segmento específico.

As empresas adotam painéis bonitos cheios de KPIs para acompanhamento. Grande parte deles pra medir desempenho e remunerar os funcionários por meritocracia. Mas a pergunta que fica é: se o indicador estiver bom ou ruim, que ação eu posso tomar para melhorar o negócio? Ou eu consigo impedir o resultado ruim antes que aconteça com este indicador? Se não vou fazer nada com a informação, será que ele deveria estar ali no painel? Porque o gestor precisa de foco e tempo para se dedicar aos assuntos importantes e a empresa não precisa gastar com armazenamento, tráfico de dados, segurança da informação de indicadores que servem apenas pra dizer que os dados existem e estão disponíveis.

Think opposite direction or put position reverse then you’ll have a good strong answer when discuss with your counterpart. More understand the needs, more effective you will do the analysis with your data

I faced challenges visualizing metrics from ChatGPT's suggestions. To address this, I used the same tool to refine my expectations. Here are condensed metrics: User Adoption Rate: (Active Users / Total Users) * 100 Query Response Time: Total Time for Queries / Number of Queries Data Accuracy: (Accurate Data Points / Total Data Points) * 100 Queries and Reports Created: Total Generated Customer Retention Rate: ((Customers at End - New Customers) / Customers at Start) * 100 BI ROI: (Net Profit - BI Investment Cost) / BI Investment Cost * 100 Data Integration Efficiency: (Successful Integrations / Total Attempts) * 100 System Uptime: Total Uptime / Total Time Training Effectiveness : (Competent Users after Training / Total Users) * 100