Power BI Data Analytics Consulting

What does a good data visualization look like?

Data visualization success is in user engagement and actionable insights. A good place to start is by looking at the reports and dashboards that end users are currently using and mapping those to the data visualization services.

General characteristics of good data visualization are that they are:

• Quick Using data visualization should help you share crucial findings faster than examining large tables of data. Many business intelligence systems, like Power BI, use real-time data and send alerts as events occur.
• Clear Data visualization should accentuate patterns, not obscure them. Clarity is typically achieved by emphasizing key trends while hiding or removing complex datasets.
• Engaging Colors, patterns, and spacing help to simplify data processing, identify trends, speed up knowledge transfer, and drive use and development.
• Shareable Data visualization must function for the team as well as their suppliers and customers in a world of dispersed employees and contractors working together in business processes.
• Actionable What do you want users to take away from the visuals? Do the informative graphs make that point? Do they provide enough analytics insights to take confident actions?

What is the data visualization process?

As a provider of data visualization services, we assist you throughout your data journey by implementing an in-depth research and development plan to determine the best way for answering your questions and achieving your vision at the highest level.

Our approach to data visualization services is as follows:

• Collect data The first and most critical stage in data visualization is data collection. We must plan in order to obtain raw data that accurately reflects the true state of the business. Sometimes, data from many data sources must be compiled as well.
• Clean data Data cleaning is an essential step to perform before we visualize data. Inappropriate, empty, or inaccurate values in data sets may result in erroneous reports and dashboards.
• Choose data visualization Choosing a visual chart or graph depends on the business needs. Make decisions based on data insights, comparisons, and linkages. Choosing the best chart generally takes some trial and error.
• Prepare data After the chart is chosen, the data may need to be transformed. Tasks like producing aggregate values for groups and merging variables to generate new columns are part of data preparation.
• Visualize data In the final step, we apply our data visualization services to the prepared data and deliver it to you in the form of charts or graphs with meaningful insights.

What are the data visualization best practices?

The basic idea of data visualization is to make the most important data easier to see. The usage of data visualization tools and big data platforms has revolutionized the procedures. Here are some of the best practices in data visualization:

• Know your audience Good data visualization is subjective. Thus, the target user groups needs have to be your starting point. What informed decisions are they after? What graphical charts will help them understand these numbers more easily?
• Show the facts Users desire numbers. The story isn't enough. Graphical charts of a large dataset can quickly reveal patterns or trends, which is normally desired by business users.
• Reduce clutter Aside from clearly presenting the key facts, it is often necessary to remove unnecessary complex datasets or simplify the presentation to make the relevant patterns stand out more.
• Use visualization tools Data visualization is a young and evolving field. Every year, new data visualization tools and BI tools are released. The capabilities are amazing.