What are some of the tools and platforms that you use or recommend for dashboard creation and analytics?

Tableau Power BI QlikView/Qlik Sense Google Data Studio these are few tools trending in market but as of now Domo Looker Sisense Databox

The key focus should always be on delivering relevant, aligned, simplistic, story-telling representations of metrics and KPIs tailored to your target audience, which can include persona-based and personalised approaches to maximise their impact. For tools, it is advisable to align with your organisation’s existing investments, such as the Microsoft stack with PowerBI, Qlik with QlikSense/QlikView, or Tableau within Salesforce/AWS etc. Keep in mind that no tool is flawless. You can enhance the visualisation capabilities of dashboards by adding a web layer on top of these tools, mitigating their limitations while still leveraging the underlying engine and data model.

Several tools and platforms are widely used for dashboard creation and analytics. The choice depends on factors like user requirements, data sources, and technical expertise. Here are some popular tools: Tableau Microsoft Power BI Google Data Studio QlikView/Qlik Sense Domo Looker Sisense Metabase Redash Periscope Data IBM Cognos Analytics Chartio You can choose a tool based on your specific needs, such as ease of use, integration capabilities, scalability, and cost. Many tools offer free trials, allowing you to explore their features before making a decision.

For dashboard creation and analytics, key tools include: 1. Tableau: Great for complex visualizations. 2. Power BI: Ideal for Microsoft users, supports real-time data. 3. Google Data Studio: Free, perfect for Google suite users. 4. Looker: Offers deep data exploration for enterprises. 5. Domo: Integrates data visualization and collaboration. Select based on your needs: Tableau and Power BI for detailed analysis, Google Data Studio for accessibility, Looker and Domo for enterprise-level management. Each tool caters to different analytics requirements and team expertise, enabling effective data visualization and decision-making.

In my view, a dashboard is simply a way of presenting data that stakeholders are interested in, in a visually appealing and easy-to-understand format. It’s designed to help them grasp key insights quickly and with minimal effort. Most people aren't accustomed to analyzing raw data, so presenting it pictorially allows them to better comprehend the information and make more informed decisions.

There are multiple platforms for dashboards. The most basic platform is excel itself where you can build dashboards which can be populated and will work with data refresh as required from a cloud service provider. As you move to different tools, Tableau, PowerBI and Quicksight are all great choices depending on the organizational investments in cloud and skills. The analysts who is using these dashboards need to learn the basics of KPis management, learn from books like "say it with charts" and understand data quality and refresh issues. The choice of the dashboard tool while important, is not the critical factor in success of the project.

For organizations seeking faster insights without dedicated analysts, user-friendly options like Domo, Zoho Analytics, and ThoughSpot offer easy setup and powerful features. Domo is cost-effective for lighter usage, Zoho Analytics scales well with data and users, and ThoughSpot excels in natural language querying. However, choosing the perfect BI solution goes beyond ease of use. Factors like data volume, team size, technical expertise, and specific business challenges should influence your decision. For instance, Looker, Amazon Quicksight, or Tableau might be better suited for handling large and rapidly growing datasets. Consider your needs carefully to ensure the BI solution aligns with your long-term goals.

In my experience, Power BI and Tableau are still two of the front-runners in terms of market popularity. When reviewing candidate applications for data and analytics postings, Power BI and Tableau are the data visualization platforms I most often see listed. Tableau, often perceived as pricier, tends to be favored by larger organizations. Power BI, benefiting from Microsoft's widespread corporate adoption, enjoys broader usage. For those pursuing careers in data or analytics, proficiency in both Power BI and Tableau is highly advantageous, aligning with current industry preferences and demands.

Choosing the right dashboard and analytics tool isn't a one-size-fits-all solution. For beginners and self-service users, Looker Studio and Power BI Desktop offer user-friendly interfaces and seamless integrations. If you're a SAP whiz, Business Objects excels within that ecosystem. Alteryx tames complex data wrangling. Open-source options like Grafana offer budget-friendly alternatives. Personally, I'm drawn to Mode, which combines Tableau's familiarity with integrated coding for advanced analysis. It empowers self-service and churns out executive-worthy dashboards in a flash. ThoughtSpot is also a favorite, allowing anyone to ask questions and uncover key drivers behind changes – a finance game-changer, especially during month-end chaos.

Start with 10 rows of data which you create using your own hands. Start asking questions to the same 10 rows and find answers. Best 10 rows any one could start with number of people who attended an exam and their result. You can start with questions like ? How many passed ? Then what is the pass % ? And then which professor class has highest pass % ? and the list goes on ... then you move to 100 , then K , then M and Bs .. now the story gets completed when you know how to traverse from Bs to 10s which is of interest in that Bs.

Once can approach dashboard skills by practicing with real-world ‘data compositions.’ Start with small projects—like visualizing a personal budget or a simple dataset—and gradually tackle more complex scenarios. Engage in ‘jam sessions’ by participating in online forums or communities where you can share your dashboards, receive feedback, and refine your skills. This hands-on, iterative approach will help you develop a deeper understanding and create more impactful data visualizations.

There are free resources available on multiple platforms, making it an excellent starting point. Later, I prefer hands-on learning. The communities associated with these platforms are extensive; for example, the Tableau community is notable, featuring 'viz of the day,' 'viz of the week,' and more.

Me gustaría a?adir al debate que a la hora de seleccionar la plataforma de visualización de datos adecuada, es fundamental considerar la ubicación de los datos de origen. Optar por una plataforma que ofrezca conexiones directas con las fuentes de datos facilita significativamente el proceso y garantiza una integración más eficiente. Esta elección estratégica no solo ahorra tiempo, sino que también puede reducir los costos asociados. En situaciones donde la conexión directa no es posible, surge la necesidad de desarrollar conexiones API personalizadas o contratar conectores externos, lo que puede agregar gastos adicionales al proyecto.

If you're learning Tableau, download the free version. It comes with some sample data. Then watch some basic videos and you'll be off! Youtube, EdX and uDemy etc. all have great tutorials. Sites like kaggle.com will let you download sample datasets to practice. Otherwise, just create a basic spreadsheet with some imaginary data like a list of friends, their postcodes and the brand of car they drive (or any such imaginary table).

Free resources on various platforms serve as an excellent starting point for individuals to explore complex visualizations and enhance their storytelling skills. Engaging with communities, such as the Tableau community with features like 'viz of the day' and 'viz of the week,' provides valuable insights and learning opportunities for hands-on experience.

Start with 10 rows of data which you create using your own hands. Start asking questions to the same 10 rows and find answers. Best 10 rows any one could start with number of people who attended an exam and their result. You can start with questions like ? How many passed ? Then what is the pass % ? And then which professor class has highest pass % ? and the list goes on ... then you move to 100 , then K , then M and Bs .. now the story gets completed when you know how to traverse from Bs to 10s which is of interest in that Bs.

Most market leading tools (Power BI, Tableau, etc.) offer a free tier of licensing which affords developers the ability to begin building dashboards and reports. A good place to start would be finding open source data sets of interest (platforms such as Kaggle or data.gov) and begin building a basic reporting layout against your data.

Oracle's commitment to innovation and improvement resonates with the ethos of continuous growth in dashboard skills. Staying updated with Oracle's evolving features and functionalities is a part of my journey in enhancing dashboard creation proficiency.

My recommendation would be to search github and start looking at all the available tooling, which is usually free. Most have some decent instructions, come with sample datasets and there’s typically a community of folks to assist should you bump into roadblocks.

Applying UX design principles to Power BI dashboards elevates them from simple data displays to intuitive, engaging interfaces. Picture a dashboard where crucial metrics stand out at the top, complemented by an aesthetically pleasing layout. interactive elements like clear filters enhance user interaction, while accessibility considerations ensure it's usable by a broader audience. this approach turns a Power BI dashboard into a seamless, user-friendly experience that makes data exploration both effective and enjoyable.

Learning UX designs can help improve the dashboard, and following the community content can also help. Having data visualization essentials knowledge can come in handy.

Oracle Analytics Cloud not only provides a canvas for technical operations but also emphasizes the importance of storytelling. Its features empower users to merge technical expertise with narrative-building skills, facilitating data-driven decisions across industries, much like the other leading tools in the analytics landscape.

There are easy-to-read dashboards that quickly help us gather what is essential. On the other hand, some dashboards (visualizations) are badly created, with basic mistakes that make us apprehend a completely different alternative reality. Sometimes, they are biased on purpose or just by plain ignorance of how to create them. Unfortunately, the latter is dangerously everywhere, especially in the press and social media. When creating dashboards, it's crucial to tell a story highlighting what needs to be highlighted. Most importantly, the dashboards should faithfully represent the real world based on the data. I suggest starting by reading a book on visualizations, and I recommend this one, Storytelling with Data, by?Cole Nussbaumer Knaflic.

Feedback is crucial - the dataset of your dreams may be useless for everyone else in the world. The goal of any dashboard should be to quickly relay the overarching takeaways and then let users intuitively dive deeper. If the dashboard is too simple, you'll be hit up with 100 one-off questions so it'll have been a waste of time for you to create it. If it's too complex, nobody will know how to navigate it and you'll be hit up with 100 one-off questions. Find the balance and take feedback as an opportunity to let your organization make more strategic decisions in the future.