Top 8 Data Visualization
TableauInformatica PowerCenterQlik SenseSAS Visual AnalyticsDundas BIOracle Analytics CloudDomoActian Ingres
I consider Tableau to be the best analytical tool available. It's really handy to use and can be used by non-technical people.
The feature that is currently most valuable is the import feature where I can link to an Excel data source. I'm not using it with any other data source, such as SQL Server. I directly link it to an Excel sheet, and if I change anything in that Excel sheet, the changed data immediately gets reflected in the virtualization. This is something that is very convenient for me as of now.
The most valuable features of Informatica PowerCenter are the ease of use, and development, and is simple to find resources.
Has a good visual tool for data mapping.
Qlik Sense is a very powerful and useful tool.
What I found most valuable in Qlik Sense is its flexibility. It's a very flexible tool, and it has a lot of capabilities compared to Tableau and Power BI. In Qlik Sense, you can take data from any of the systems, and you can do data transformation, prepare the data, then present your KPIs. Data transformation isn't present in Tableau, Power BI, and other tools. In Qlik Sense, you're able to get the data and directly present that data. The presentation layer is also very good in Qlik Sense. My company is very happy with this tool.
I use Visual Analytics for enterprise reporting.
It provided the capability to visualize a bunch of data in an organized way.
It's great for consolidation and creating one source of truth.
Mobility is the most valuable feature for us. All employees can access it from anywhere. It is a big advantage for us.
The dashboarding itself was pretty easy. So both the front and the back end were positive in this case.
The best feature of Domo is that it's completely on the cloud. I also like that you can handle data end-to-end without having to depend on multiple tools. Another specific feature I like the most about Domo is Magic ETL because, through it, you can do all your expression, transformation, and loading activities very smoothly. The tool also follows the lineage concept, so you can understand what kind of transformations took place on a particular data set. You can find end-to-end data from the source until it has become the final output or the final data set. Whatever happened to a particular data set, you can understand it through the Domo lineage, and that isn't possible in most of the tools available in the market, but in Domo, that's available. The tool is also solid and because it's on the cloud, it uses multiple data engineering in the backend and multiple algorithms in the back, behind the scenes, r
The deployment of our solution across a number of servers using Ingres .NET has meant that we can protect the database server behind a highly secure firewall and deploy the front end solutions on a normal web server.
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Data Visualization Topics
What are 4 characteristics of data visualization?What are the 3 main goals of data visualization?What are the common types of data visualization?What are 3 pros and cons of data visualization?What are data visualization techniques?What are data visualization tools?Data Visualization Solutions Benefits
What are 4 characteristics of data visualization?
Effective data visualization requires a balance between strong visuals and clear data communication. This is achieved via these four characteristics:
- Information: The data visualization process starts with correct data. Data must be reviewed to ensure that the information is accurate.
- Story: Next, it is important to define a clear and compelling concept for visualization.
- Goal: Decide what the specific objective of the visualization is. What information is being communicated?
- Visualization: Choose the correct visual display that will effectively reflect your information and concepts to help you achieve your goals.
What are the 3 main goals of data visualization?
Data visualization aims to easily communicate insights like relationships, trends, and patterns to stakeholders, regardless of their data analysis expertise, allowing them to make informed decisions that will positively affect their organization. This is reflected in the three main goals of data visualization:
- Understanding your data: Visualization helps users comprehend vast amounts of data at a glance and allows users to understand the data better to measure its impact.
- Educate your audience: Easy-to-understand visualization strengthens the impact of data delivery for audiences and presents the data analysis results in a convincing manner.
- Help with decision-making: Having a clear picture of the visual data and insights assists audiences in clear decision-making.
What are the common types of data visualization?
Some of the popular types of data visualization are:
- Tables: Data is displayed in rows and columns to compare variables.
- Pie charts: These graphs are divided into sections representing parts of a whole. This is a simple way to organize data and compare the sizes of each section.
- Line graphs show change in one or more quantities by plotting a series of data points over time.
- Histograms plot a distribution of numbers using a bar chart, representing the quantity of data that falls within a particular range.
- Tree maps display hierarchical data as a set of nested shapes. Treemaps are helpful when comparing proportions between categories according to their area size.
What are 3 pros and cons of data visualization?
The top three pros of using data visualization are:
- Accurate analysis: With the help of data visualization, it becomes easier to understand the trends and get a better understanding of the data behind the visualization.
- Data modification: Data on the basis of which the information is presented can be easily modified.
- Understanding opportunities and trends: Using data visualization, companies are able to find the patterns in the behavior of their customers, enabling them to explore trends and opportunities to improve growth and customer satisfaction.
Some of the cons of data visualization are:
- Bias: Different audiences may focus on different parts of a visual and come to inconsistent conclusions.
- Improper design risk: If the design is not properly done, it can lead to confusion in communication.
- Unfocused audiences: The audience may not focus on the intended message of the visualization and can miss the point of the data visualization.
What are data visualization techniques?
Here are some essential techniques that will help you with your data visualization:
- Choose the right chart type and colors: To effectively present your data, select the right chart for your specific project, audience, and purpose. Choose colors that are easy to decipher so audiences see a clear picture.
- Manage your data: Break your data down into the most focused, logical, and digestible way possible.
Include comparisons: By presenting two or more graphs simultaneously, each showing contrasting versions of the same information over a particular time frame, you provide a clear picture of the impact of your data.
- Consider the user’s end device: Bear in mind that audiences will be viewing your visualizations on a multitude of end devices. Your visualizations have to be designed to display properly on mobile screens, laptops, desktops, and other devices.
What are data visualization tools?
Data visualization tools render information into easily understandable visual formats, such as graphs and charts, for data analysis purposes. These tools make it simple to work with and understand large amounts of data.
Data Visualization Solutions Benefits
Some of the benefits of using data visualization tools include:
- Better business insights: Data visualization tools can help businesses boost their ability to access the information they need anytime, anywhere.
- Improved reports: Data visualization tools identify and display insights that may otherwise be missed in standard reporting methods. Implementing data visualization tools in addition to traditional reports can go a long way in presenting the bigger picture.
- Boost revenue: Most visual data discovery tools can provide users with real-time information, which can be used to predict analytics for sales figures. Decision-makers will be able to determine why certain products are more popular to target customers while others are not.