You might already know that the role of data visualization is to communicate complex datasets in a clear and actionable manner to help audiences understand and get insights, hence, making better data-driven decisions.
It seems that creating a dashboard is so easy that everyone can do it, however, building and designing beautifully interactive data visualizations that do tell the story is a skill you need to be adept at. When your dashboard is well-designed, they facilitate more data-led decisions and cut down on the amount of time needed for data analysis.
In this blog post, we will discuss interactive visualizations as well as bring you an in-depth tutorial using Dataflake data visualization tool on creating story-telling visualizations
Interactive data visualization refers to the process of presenting data in a graphical or visual format that allows users to interact with and explore the data. With interactive visualizations, the user is able to manipulate the data and modify the visual representation in real-time, enabling a deeper understanding of the information being presented.
Specialized software tools that enable the development of intricate and dynamic images, such as graphs, maps, and other visual representations of data, are frequently used to produce data visualizations. With the use of these tools, users can alter the data, zoom in and out, and view various parts of the data in multiple formats.
Data visualizations are widely used in a variety of fields, including data science, business intelligence, journalism, and scientific research. They can be used to identify patterns, trends, and relationships in data, to communicate complex information to a broad audience, and to provide insights into data that may not be apparent through other forms of analysis.
The rise of big data and the increasing availability of software tools have made data visualizations more accessible than ever before, allowing users to explore and understand complex data sets in real-time. For clear elaboration on why creating a dashboard that is interactive is crucial, here are some reasons:
Improving understanding: If your dashboard is stunning, you can make complex data more accessible and easier to understand. By presenting data in a graphical or visual format, users can quickly identify patterns, trends, and relationships in the data, which may be difficult to discern from raw data or text.
Enabling exploration: It enables users to explore data in real time, allowing them to modify the visual representation and manipulate the data to gain insights into the information being presented. This can help users to identify trends, patterns, and relationships that they may not have otherwise seen.
Enhancing communication: Demonstrating Interactive visualizations can be an effective way to communicate complex data to a broad audience. By presenting data in a visually compelling format, users can engage with the data and gain a deeper understanding of the information being presented.
Supporting decision-making: provide decision-makers with insights into complex data, enabling them to make informed decisions based on the information presented.
When you made your decision on choosing the right data visualization tool to start digging deep into creating interactive dashboards, it’s time you execute it!
Basically, there are many data sources that support you in collecting data, there might be databases, spreadsheets, APIs, or other sources of data. With Dataflake, we don’t support spreadsheets because the limitation is the lack of real-time data and hard to transform complex data, then, the outcome might not be exact. So, which data sources do we support
Postgresql
Bigquery
Clickhouse
MySQL
Microsoft SQL Server
Flat File
To connect to a Data source, there are some information requirements you need to fill in to complete connecting to Data sources. To be clear, Dataflake only sends the query directly to your database and no more than that, so the database user just needs the SELECT (Read) permission on tables used in the SQL. We always recommend giving enough permissions to your user to keep your database safer from cyber attacks
Tips: Make sure to execute the data cleaning process that involves identifying and correcting or removing errors, inconsistencies, and other issues. Notice data that are not cleaned properly can lead to inaccurate results and incorrect conclusions
Most BI tools provide no-code solutions to transform data such as visual SQL builder. It is undoubted that SQL visual builders SQL visual builders can still be useful for certain types of data transformation, particularly for less complex tasks or for users who are less familiar with SQL, however, they are often limited in terms of their capabilities and can be less efficient for complex transformations.
In contrast, using SQL to transform data has greater control over data transformations such as writing complex queries that join, filter, and aggregate data in ways that may not be possible with a visual builder.
Without further ado, let’s start writing a query to induce and transform data that meets your requirements
Some samples of SQL commands you'll be able to use:
SELECT: The SELECT command is employed to retrieve data from a database. It allows you to specify the columns you would like to retrieve and therefore the tables you would like to retrieve them from. for instance, the subsequent SQL command retrieves all columns from the "customers" table:
SELECT * FROM customers;
GROUP BY: accustomed group rows of information supported one or more columns in a very table. it's often used with aggregate functions, like SUM, COUNT, AVG, MIN, and MAX, to perform calculations on the grouped data. As an example, this command groups employees by department and counts the quantity of employees in each department:
SELECT department, COUNT(*) as num_employees
FROM employees
GROUP BY department;
JOIN: The JOIN command is employed to mix data from two or more tables in an exceeding database. It allows you to specify the columns you wish to retrieve and also the tables you wish to hitch. for instance, the subsequent SQL command retrieves the customer name and order date for all orders placed by customers within the "New York" region:
SELECT customers.name, orders.order_date
FROM customers
JOIN orders ON customers.id = orders.customer_id
WHERE customers.region = 'New York';
Once you transform your data using SQL, it’s time to add visualizations
Once your dataset is cleaned and prepared for data analysis, it's time to add some visualizations.
To visualize the Sales volume per price category in the time period, we choose the pie chart to demonstrate the difference
This is how we create a pie chart
In the left toolbar of the dashboard editor, click the “chart” icon, then hover to choose pie chart → click to add chart on the canvas
In the right panel, in the “query” tab, choose the existing query (the one that you created before), then choose that query.
Done! You have successfully created your pie chart.
Note: If you want to change the field in your pie chart, switch to the “data” tab, then drag and drop the field to perform on the chart
If you want to present the story of other collected data through other queries, you can choose different genres of visualizations. Depending on the purpose of each kind of chart, you can use the proper one to better depict the data. For example, a line chart shows data as a collection of points connected by straight lines. A line chart is used to display patterns and trends in data over time or between categories. Bar chart employs rectangular bars to depict data values. A bar chart's comparison and illustration of the relative size or distribution of various categories or data points are its main purposes.
Select and demonstrate your charts within the dashboard to ensure the information you want to convey, they might be charts, filters, text, images, etc. Keeping all visualizations on a page doesn’t mean that you successfully completed it. Let’s think about the visualization you want your audience to pay attention to first, or which are the most important ones in your dashboard.
One tip we share with you is that you can follow the “F pattern”
The F-pattern is a design principle used for dashboard layouts. It refers to the natural eye movement pattern when viewing content on a screen, which resembles the shape of the letter "F". Designers should place the most important information at the top left of the screen, use headings and bullet points, use visual elements, avoid clutter, and keep the design simple and easy to read. By following these principles, designers can create an interface that is intuitive and effective at communicating information to viewers.
Some best practices for using the "F pattern" when designing a dashboard include:
The final step is customizing your dashboard. Each visualization in your dashboard, features charts, text, filters, images, arrows, and other design elements, you can easily customize every single of them by its bottom toolbar and more on the right panel. Besides that, let’s consider choosing the color of your dashboard and dashboard designs so that you can seamlessly create interactive visualizations that attract your audiences.
Notes: You can use conditional formatting for table charts, line reference, or zoom analysis to turn your dashboard more interactive
That’s all. Creating your first interactive dashboards with Dataflake is so easy and effective. Dataflake is here to help you unlimitedly tell your story without constraint.
More importantly, now, it’s free. After May, just a snippet of the fee, then, your data visualization workflow is at ease!
Try Dataflake from today!
