What are the most important tools for a Business Intelligence (BI) professional to visualize data?

1. Ease of Use and User Interface: - Intuitiveness: The tool should be user-friendly for both beginners and advanced users. - Customization: Ability to customize visualizations easily according to specific needs. 2. Visualization Capabilities: - Chart Variety: Wide range of charts for different types of data - Dashboard Creation: Create comprehensive dashboards combining multiple visualizations 3. Data Connectivity and Integration: - Compatibility: Supports various data sources commonly used in your organization. - Real-time Data Handling: Capability to handle real-time or streaming data 4. Performance and Scalability: - Speed: Fast rendering and response times - Scalability: Handle increasing data volumes

Selecting the optimal data visualization tools can be guided by the following questions: 1. Does the tool offer sufficient secured data sources? 2. Is it adaptable for users across various skill levels? 3. Is it cost-effective, and free from hidden charges? 4. Is data transformation a straightforward and effective process? 5. Can data governance and security measures be easily implemented? 6. Does the tool boast a user-friendly interface for seamless navigation? 7. Is there strong community support to address upcoming issues? Ask these inquiries to any tool, and if you receive maximum positive responses (Yes), consider choosing it as your organization's data visualization tool.

Select a data visualization tool with following factors: •Integration & Compatibility - with data sources, data types, & integration with existing tools. •Ease of Use- for quicker adoption and streamlined onboarding, catering to all skill levels. •Flexibility- adaptability, allowing configuration, customizations and innovative. •Performance & Scalability- for peak data volumes and its ability to handle real-time visualizations. •Security- alignment with industry standards, compliance with regulations. •Data Governance- access controls, audit, collaboration, & cataloguing. •Citations & Comparisons- from comparable implementations. •Cost Considerations- capital & maintenance costs. •Availability of Support & Community. •Time to Market.

Most people and companies ignore the fact that there must be a reason for analytics and any visualization of the results is meaningless without initial direction. We love the activity of analysis but chose to forge ahead with no plan in mind. We also buy technology and software with no plan in mind. Analysis must start from a fundamental understanding of where you are going and the reasons why.

Para seleccionar una herramienta de visualización de datos hay 2 aspectos importantes a tener en cuenta: La facilidad de uso: que tenga una interfaz amigable y que sea sencillo crear y personalizar las visualizaciones La capacidad de personalización: que puedas personalizar con facilidad, que sea flexible y tenga suficientes características para lograr con facilidad las visualizaciones que se necesitan Que

Why Customers Choose Tableau: - Analytics for Everyone: Analytics goes beyond mere number reporting; it involves discovering insights and posing new questions to enhance business analytical outcomes. Tableau’s user experience empowers all users to utilise data for gaining valuable insights. - Flexible Architecture: Tableau is designed to fit, rather than dictate your data infrastructure, data ecosystem and business workflows so that you can leverage your existing technology investments and expertise. - Community Ecosystem: Tableau’s community is well-known and recognised within the industry. It inspires people to both learn and share knowledge, highlighting our community and impact of analytics.

A lot of are using Power BI, thinking of easy access and usability. However, if you are having an SAP backend do not forget about SAP Analytics Cloud (SAC). Within SAC, the IBCS standard for visualizations and tables is already implemented, which makes data visualization more efficient and faster.

For how long will we be having a debate over one BI tool vs other. All BI tools offer similar capabilities - helping analysts make beautiful dashboards and charts. But the real question is - 1) when will we rise above the tools and look for solving the problems of actionable insights? 2) When will business user be able to access data freely without having to worry about the chart he uses but how the chart is able to convey insights. I believe still there lot of work on the table left to be done ny data analyst and business users. We need to evaluate tools on this capability now? What you think, am I right?

Microsoft Power BI. Salesforce Tableau. Google data studio. Atoti Library on python 3 is used by writing some simple lines of code.

A mi siempre me ha encantado Qlikview por su facilidad de uso y su interfaz de usuario. He trabajado también con Alterian que es mucho más potente cuando tienes un volumen muy alto de datos pero es mucho más intuitivo Qlikview.

Actionability! Go beyond 'nice to know' and 'good to see it this way'. Data exists to improve decision-making. The visualization should be such that it does not leave any room for debate on the right strategy to pursue - even to the layman. Higher management, e.g. board members, don't understand the data, it's sources and intricacies, as the visualizer. Simplify the data for them, so that it gives them confidence to make the right decision.

Data visualization tools, the perfect instruments for any business looking to artfully hide the less flattering aspects of their operations. With these tools at hand, business users can cherry-pick data that backs up their narrative, conveniently leaving out anything that doesn't. Plus, they're great for accelerating poor decision-making processes by restricting in-depth analysis and exploration of data - who needs thorough understanding when you can make snap judgments, right? And let's not forget the crowning glory: these tools create entirely new job roles like data analysts, engineers, and architects, ensuring that business users are kept at a safe distance from the raw data. How wonderfully efficient! What you think, am I right?

1. The most important advantage of the data visualization tool is to get insights, in a way which can give analysis of past and current data which can be used for future decisions and insights. 2. The insights can be in any form we want based on users needs. For e.g. analyzing every data point corresponding to various fields like year wise, country wise sales, and checking where sales were at low or at peak can be easily demonstrated by simply plotting charts and showing trends at various dimensions. 3. This can be used to detect areas needing attention and taking major business decisions preventing failures and risks 4. BI tools save a lot of time in data analysis and are a lot secure as they have various security implementations.

Data is the new oil, and nowadays, every company is generating a huge amount of it. However, simply generating and storing data is like owning a car without knowing how to drive. It's the tools that unlock its true potential. Data visualization tools like Power BI, Tableau, and Qlik are the game changers in this era. They help companies transform their data into presentable and insightful visuals, enabling them to not only utilize their data effectively but also make informed decisions with greater confidence.

Having the right data is good, analysts, data scientists and engineers understanding data is better, but ultimately what actually matters and is likely to make a difference is when an entire organization can see, understand, and act on the data. The prettiest dashboard in the world is worthless if nobody can get to it or understand what it means. The right data delivered in a timely manner to the right people can change an organization from one that is reactive and behind the curve to one that is lean, mean, and a few steps ahead of their peers.

Data representation: is an art which even an experienced analyst will be lacking. Analysts with a combination of experience, domain knowledge, best practices on chart selection, managing real-estate on the visualization, understanding the users requirements etc are very hard to find or very expensive. Data size: how do we show hidden patterns and insights from millions and billions of data points in a database with the quickest response time. Data security: managing security in a complex tool with many users especially in the cloud. Dependency on data engineering team to provide quality data. Tool usage: what may seem simple and obvious in a functionality of the tool may be complex for a non technical user in a self service bi tool.

Uno de los grandes desafíos es tener un excelente equilibrio entre la facilidad de uso y la gran cantidad de funcionalidad que necesitamos para personalizar nuestras visualizaciones.

Many BI tools allow for self service, however this can pose challenges from a security standpoint. Row level security is always an option, but it is up to the user to apply these rules and share with the appropriate people. BI tools can easily become the “Wild West” with many data transformations and visualizations made on the fly. It can be a challenge to keep the instance clean with only data and visuals being used. Data integrity can pose a potential challenge as well. When you have a self service approach there is no longer the old school structure in place for QAing results and certifying the data before pushed to production. This can lead to users consuming incorrect information to make business decisions.

In data visualization there are often trade offs. The right visualization to show data insights is not always obvious or straightforward. And what is clear to a trained and technical analyst and what is clear to a manager or end user are not always the same. Data processing is a fine line between cleaning up junk data to improve signal to noise ratio and introducing variables that can show a bias. Disclosure of source and methodology is critical if the data being used to drive decisions or policy. Finally, the presentation and audience are everything! A very pretty dashboard that provides nothing actionable is worthless, but so is a dashboard that is dense and/or filled with jargon that people can't grasp easily.

Taking about Microsoft Power BI challenges: I will start with data modeling challenges. For example, the most appropriate and suitable data model works and fits the architecture of Microsoft Power BI is the storage schema and the simple to medium data models. It's obvious that Power BI has issues in dealing with complex data models. Second, the data analysis expressions DAX language that is used in data manipulation inside power query in power bi it could be difficult for someone with SQL background or python and it takes some good time to be expert in it.

Always consider the end user needs in mind - what exact problems are we trying to solve. - how will this help in identifying the underlying factors. - how can it help them to measure the deviations and alert them. - how can it help to make the next decision and action. - does it start with a high level picture and is it easy to drill down to the details. - last but not least, does it create a culture where organization can trust the insights and empowered to do analysis on their own.

Here are 7 tips in choosing the best fitting BI tool for your projects: 1. Know Your Needs: Clearly define your data visualization requirements and goals. 2. User-Friendly Interface: Choose a tool with an easy-to-use interface. 3. Scalability: Ensure the tool can grow with your organization's expanding data needs. 4. Connectivity: Pick a tool that seamlessly connects to your data sources. 5. Collaboration is Crucial: Look for features that enhance teamwork, like real-time collaboration and sharing options. 6. Total Cost: Evaluate the overall cost, including licensing fees and training expenses. 7. Support and Community: Opt for a tool with strong support resources and an active user community for assistance when needed.

1.Craft a Narrative: Arrange data in a logical sequence, guide attention, and incorporate annotations to tell a compelling story that conveys the data's significance. 2.Clarity First: Prioritize clarity by using simple designs, clear labels, and a user-friendly layout. 3.Audience Awareness: Tailor visualizations to your audience's understanding and needs to ensure effective communication. 4.Chart Selection: Choose the right chart type based on the data; use bar charts for comparisons, line charts for trends, and pie charts for proportions. 5.Color Purposefully: Implement a cohesive color scheme to emphasize key points, avoiding overwhelming visuals with excessive colors.

A few tips from my end: 1. Keep it short and simple. Limit the visuals on a page to reduce loading time. 2. Keep calculations out of the reporting layer, at the backend layer as much as possible to optimize loading time 3. Keep a track of loading time of visuals using various tools like performance analyzer in Power BI 4. Restrict the data load into the report as much as possible to optimize data and report 5. You can create a template based on your need to reduce creation time of reports with similar templates. 6. Try using out of box and certified visuals only as other visuals might increase load time 7. The report should be accessibility compliant. For eg. Contrast, title etc should be compliant

Here are a few tips for using data visualization tools effectively: Start with a clear goal in mind. What do you want your visualizations to achieve? Choose the right tool for the job. Not all data visualization tools are created equal. Make sure to choose a tool that is well-suited to your data and your needs. Keep it simple. Avoid using too many colors, fonts, or data points in your visualizations. Label your axes and charts clearly. Use color wisely. Color can be a powerful tool for highlighting important data points, but it can also be used to mislead viewers. Tell a story with your data. Use your visualizations to tell a story about your data and what it means.

Business Analytics é a parte de Business Intelligence que engloba o fornecimento de análises estatísticas, previsões, modelos, e simulações para auxiliar executivos a aumentar receitas e reduzir custos. É uma evolução do Business Intelligence ou um Business Intelligence mais abrangente, sendo usado para tecnologias, métodos e aplicações que analisam dados para orientar a tomada de decisões. Nessa lógica, Business Intelligence trata de dados do passado; Business Analytics, usando ferramentas preditivas de Data Science, ajuda na tomada de decisão para a construção do futuro possível, com base na análise do provável.

Not sure if my feedback is appropriate here as I see that this conversation is more focused on data visualization tools and data preparation aspects. But one thing that is critical in BI projects is, understanding of business outcome that the organization wants to drive. While the steps of data visualization are important to effectively present the story the data is bringing out, they must directly relate to the business model. For this, understanding the business operating model is the first step in the game. Then, understand and optimize the relevant data to create effective visualization on the presentation layer. Going further, there must be feedback loop from actions taken based on analytics, that the newer data should reflect.

BI professionals are the most valuable tools, it is their skills and knowledge that generate the most value from whatever digital tooling is in use to produce visuals or intelligence. A professional well versed in visual selection criteria, requirements gathering and UX will deliver great insights

The true effectiveness of BI visualisation lies in the design mastery of the data model it feeds from. The more comprehensive and smart a data model is, the more coverage and depth can be obtained to generate Business Intelligence. Another key aspect is the User Interaction Experience. BI visualisation should be considered like any other software product that needs to look visually appealing and offer a friendly experience to generate information. I personally recommend using the policy of ‘click of a button’ to generate the desired output supported by visuals showing trends and time series analysis that enables a user to make decisions or understand the situation with minimum effort.

If your company is already heavily invested in all things Microsoft, I’d highly recommend adding Power BI (PBI) as your BI visualization tool as the Microsoft ecosystem is much smoother to integrate with other MS applications. Example, being able to use PBI with Teams. The learning curve on PBI - versus a Tableau - is much lower where organizations can consider creating a semantic layer and expose the data to LOB users who can then become self sufficient. Democratizing data is a major trend as so many coworkers in non-technical roles have become much more tech savvy and analytical.