How can you overcome common challenges when communicating BI query results?

I agree with the above - knowing your audience is certainly the first step in effective communication. In my experience, the best approach to cater to your audience is to set out by determining what their interest will be in what you are presenting. For example, if you are communicating to a room of sales people, they will likely be most interested in leads and revenue so you should focus in on how the BI you are presenting will help them close offers and thereby boost sales and hit their targets. They will be less interested in the technical steps taken (“how” of the analysis) compared to a room of BI practitioners for example who might be equally interested in the “what” as well as how you came to your conclusion.

Knowing your audience is indeed the first step to overcoming common challenges when communicating BI query results. Here are some strategies I use to address specific challenges: 1. Avoid using Technical Jargon :Use clear, concise, and plain English to explain the findings and avoid using Technical Jargon 2. Use charts, graphs, and other visual aids to help the lay person understand the data set analysis. This can help make complex information more accessible and engaging. 3.Focus on the key takeaways: Highlight the most important findings from the data set analysis, and explain what they mean in practical terms.

In my experience, understanding your audience is the compass in the world of Business Intelligence communication. Whether your stakeholders are tech experts or not, tailoring your message is the secret sauce. Clear language and visuals are your allies.

Insights are valuable if they are relatable to a process that an individual or a team owns. This comes with understanding your audience - are they domain experts or are they executives? Stitching a story around their body of work not only makes it more understandable, but also creates a conversation for further enhancements and use cases.

I would think of it like explaining a complex LEGO model to a kid. First, simplify: Break down the results into smaller, easy-to-understand parts, just like you would show each step of building a LEGO set. Use clear and simple language, avoiding technical jargon. Second, visualize: Use pictures or graphs to make your point, similar to showing a picture of the LEGO model. A colorful chart or a simple drawing can help make complex data more understandable. Third, relate to what they know: Connect the information to something familiar. If you're explaining sales data, compare it to something like a lemonade stand, where you talk about how many cups were sold and which days were the busiest.

In my experience story telling is a great and a very effective format in communication …. Having a compelling narrative along with sharing anecdotes makes your content engaging and also relatable to the audience. People may forget the data and the numbers but they will always remember the story.

La documentación de las consultas no solo facilita la comprensión y el mantenimiento, sino que también aumenta la credibilidad y la transparencia de los resultados.

As a BI expert, you may need to retrace your steps. By this I mean you may need to go back and check your queries on how you decided to go about doing your analysis in the first place. Naming your queries or documenting them can save you from a huge headache. Also, when sharing your process with other BI experts, they'll be able to understand your reasoning behind your work, which will save them a headache too.

In my life, especially in business life, real-life examples are the most valuable. Concluding someone else's failures and successes is for me, next to real data, the basis of decision-making.

Its a good practice to document queries with correct syntax, business explanation and query completion date. Its always good practice write comments to explain the purpose of the query, headers with metadata information, data definitions, consistent formatting for readability, document query dependencies, performance considerations for optimization and version control

I believe it's essential that the visuals we use are tailored to our audience. This means choosing graphics and imagery that not only align with their level of understanding and familiarity with the subject but also effectively convey a single, clear idea. Visuals are powerful tools in storytelling; they should not just decorate the information, but rather enhance and clarify it, helping to build and support the narrative. The right visual can simplify complex data, emphasize key points, and make the insight more accessible and memorable. In short, wise use of visuals is about creating a cohesive, engaging, and informative experience for the audience.

You don't want to be creating a pie chart for a dataset that contains a bunch of variables.. Instead, you want to go for a bar chart. Simplicity is key in data visualization.. Often times technical BI experts will get caught up in the analysis itself and forget to ask the big picture questions: "How does this visualization actually affect our bottom line if at all?" "Are my visuals congruent with the data types?".. Effective BI visuals should clearly and simply communicate data insights, tailored to the audience, while avoiding clutter and maintaining a narrative flow.

Devemos considerar que muitas vezes podemos extrair mais informa??es do que a inten??o inicial ao usar BI e n?o devemos desperdi?ar a chance de trazer outros insights ao seu público alvo. Principalmente quando estamos apresentando resultados a alta gerência que espera que quem está analisando os dados possa trazer solu??es que eles nem estavam considerando. N?o desperdice a chance de impressionar a sua audiência.

Regla de 7 o 5 segundos es clave. Y se relaciona con el storytelling y la capacidad de la audiencia target para comprender la abstracción que se le presenta en forma de chart. Pensá en lo siguiente: Hay un universo complejo, con infinita información. Solo una porción realmente chica capturamos y la sintetizamos en 'un dato' inserto en alguna base, por ejemplo. A la misma la relacionamos, la contextualizamos, y volvemos a abstraerla en un gráfico para que pueda ser más fácilmente comprendida.

Utilizar imágenes sabiamente implica elegir priorizándo la didáctica y el público objetivo, representar objetivamente los datos y mantener simplicidad y claridad en el dise?o, usar colores y etiquetas estratégicamente, y proporcionar contexto adecuado para evitar malinterpretaciones. Mantener un dise?o consistente, considerar la interactividad para un análisis más atractivo y realizar pruebas con usuarios finales para garantizar la comprensión. Además, cumplir con las normativas de privacidad y ética de datos. Todo esto con el objetivo de facilitar la comprensión y apoyar la toma de decisiones informadas en el ámbito empresarial.

No hay éxito en la comunicación (ni en considerarcomo 'bueno' a un análisis de datos o solución BI) sin una buena historia. Se vuelve indispensable conocer la narrativa propia de la industria, negocio o situación analizada, dado que la perspectiva nos evoca a que la audiencia target se sienta incluida o comprendida en su conocimiento o saber.

For success, telling a value driven story should be a must BUT how is a story best shaped? Quick tip is to start with a simple anchor & here are some ideas to get you started: 1. Organisational strategy - a lot of companies already have (or are in the process of developing) a corporate strategy & this is a good place to find compelling anchors 2. Known pain point - there are few anchors better than a pain point especially one that is well known & that is broadly impacting 3. Competitor position - most companies are in competition with others & their market position is therefore partly influenced by what their competitors are doing, which makes related anchors particularly compelling Hope these help focus you value driven stories

Telling the story is critical. Business would like to understand what we did in a way that they can digest it. Nice to start with what data was pulled in without going in depth into technical. What is the high-level summary we inferred from the data in Business language and not by table or fields. Visuals do help as long as it is not too complex. Give pointers or actions specific to the report being looked at that Business may have to dive in. Also, any other generic observations you found that will benefit Business. All this will increase the trust, credibility, and relationship you have plus your understanding of the overall Business you support.

Telling a story around BI data, as opposed to just throwing statistics at the audience, may greatly improve comprehension. I created a story that emerged from the data to convey market trends to the leadership team. This method not only improved the findings' memorability but also helped everyone appreciate the strategic ramifications.

Numbers don't matter. The story those numbers tell is what matters. Imagine telling a story 'E-commerce returns are more sensitive to delivery timelines for electronic items compared to fashion products' vs 'Our experiment reduced returns from xx% to yy% for electronic items when we reduced average delivery time from aa days to bb days. For fashion products, the returns reduced from xx% to only zz% when the average delivery time reduced from cc days to dd days' Data should be a support player, story should be the main player.

Feedback-seeking is essential for BI communication to keep becoming better. I put in place a feedback loop after delivering quarterly financial reports to find out how stakeholders perceived the information. In addition to dispelling any misunderstandings, this iterative process made ensuring that future communications better met the expectations of stakeholders.

This should be as easy as possible - every feedback helps you to improve your Reports - find ways how to collect feedback with ease - forms, buttons, internal communities, and leverage their feedback in your Reports.

Feedback is a powerful tool for growth and fuels improvement. There are certain steps/ways in which the feedback can be taken regarding the BI visuals. Firstly, we need to get the input from both audience and BI experts to cover all angles. Next, we need to monitor how effectively our visuals and explanations convey our message. We must be open for constructive criticism so as to address areas for improvement. Finally, we need to adapt our communication style based on feedback and engagement data.

I struggled with this when I first started working with BI. It hindered my ability to effectively communicate and gain insights to improvements within the role. Asking for constructive criticism is critical to be successful in any position, but working with BI means you have additional responsibilities to take. Imagine yourself not taking criticism well, and how that might impact the business. It’s not a small thought. Know that not everyone sees what you see nor knows what you know. Seeing it from this perspective will assist in communication and helps explain complex problems to others. This helped me create many high-quality relationships with people, too. My advice - be simple and accept all the criticism you can handle in this role.

O Feedback é primordial, n?o somente com outros analistas de BI observando o seu trabalho e criticando melhorias, mas também o usuário final, com dicas, sugest?es de vis?es e otimiza??es que possam surgir. Etapa importantíssima.

Mostly the focus is on the issues covered, but one important aspect not to be missed out is the data quality that is being used. It’s not just about the accuracy of the figures but also the uniformity in definitions, time periods and the scope under coverage. In large organisations with operations in multiple geographies this is a very important issue to be addressed at the beginning, else more time is lost in discussions on data quality rather than on the results when the visuals are presented. The best way to avoid this is to involve and get the buy in from the relevant stakeholders and spend time to create the design document and get it approved.

Consolidating your insights to 3-5 points that the users can take away makes the entire journey of Business Intelligence fruitful. Accessibility and ease of reading the file to draw conclusions is the most important aspect of any visualization. Its always better to get feedback as mentioned in the previous point to generate improvements, but over fitting will lead to breaking an effective story telling data template.

Desde mi experiencia, la #gestión #proactiva de #expectativas se destaca como una consideración clave en la comunicación de resultados de consultas de BI. Entender a tu cliente, establecer claridad sobre alcances y limitaciones previene malentendidos. La flexibilidad en el enfoque, combinando gráficos visuales con explicaciones detalladas, se revela como crucial para adaptarse a diversos niveles de familiaridad con la terminología técnica. La introducción de elementos interactivos, como demos y sesiones de preguntas, mejora la comprensión y el compromiso. La adaptación continua basada en el feedback refuerza la importancia de la agilidad en un entorno dinámico de BI, asegurando comunicaciones efectivas y relevantes.

Em uma recente análise de dados, deparei-me com um desafio intrigante. Ao documentar e visualizar um conjunto complexo de dados, percebi que a escolha inadequada de elementos visuais poderia obscurecer a mensagem. Optei por um gráfico de dispers?o para destacar correla??es, mas a falta de clareza nas cores obscurecia nuances importantes. Com feedback construtivo, ajustei a paleta, adicionei rótulos significativos e aprimorei a narrativa. O resultado? Uma comunica??o de BI mais impactante e acessível, ressaltando a importancia da adapta??o constante para garantir que nossas histórias de dados brilhem com clareza, mantendo seu significado essencial.

Bi is not only about making solid data presentations but should be a tool for the user to get answers to future questions. We should help with our BI reporting to give the user a tool that can help direct him to the future. Ultimately, he should use the tool using our significant data set to keep up with the challenges we don’t know yet. Bi should replace all current reporting and spreadsheets.