Data visualization is crucial for making educated decisions as the business sector relies more and more on data. Data’s rising volume and pace make it impossible to comprehend without abstraction or visual depiction. Furthermore, data that are non-statistical, such as organization processes or customer journeys, are difficult to interpret and repair without visualization.

Data Visualisation has therefore become crucial for businesses to make data more accessible, understandable, and usable in decision-making. Data visualization is the source of business intelligence.

Is data Visualization so important?

Data visualization is a powerful tool that uses statistical visuals, information graphics, charts, and other approaches to clearly and effectively show complex data. Facts visualization facilitates user comprehension and reasoning about facts and evidence by encoding numerical data with dots, lines, or bars. Tables are used to search specific measurements, while charts display data patterns or correlations for more Variables.

Thanks to the Internet and increasing modern technologies, transforming data into understandable images is now possible for everyone. One con is the inclination to prioritize convenience above quality. Transforming the spreadsheet cells into charts can provide merely passable or useless charts since it fails to convey the fundamental notion. As a result, before clicking and viewing data, it is critical to evaluate your aim and objectives.

Creating an Insightful and Profitable Visualization Strategy

To make effective charts, it takes more than just understanding visual grammar rules. It is crucial to understand when to use and how to handle the key and colours; relying solely on rules can result in a lack of strategy in the chart-making process, similar to planning a marketing campaign without a plan. Instead, effective chart-making demands acknowledging a sequence of tasks requiring varying degrees of plotting, resources, & expertise.

Analyzing the purpose of data or information is critical before generating a visualization. Is it conceptual? Is the visualization meant to make a statement or discovery? By answering these questions, you may identify the sources and gears required to build a successful visualization that meets your objectives. This method allows you to choose the most effective visualization style for conveying your message to your audience. As a result, good chart creation begins with careful planning and a clear knowledge of your visualization objectives.

Also Read:  Domo for Dummies: A Guide to Creating Powerful Data Visualizations with Domo

Tips and Tricks!

Here are some surprising yet effective Data Visualization techniques that experts have emphasized and accepted:

Source: merkle.com (Image for representation only)

Art of Omission

The skill of omission should be treasured. You should emphasize what is vital and exclude what isn’t. This will assist in avoiding clutter and allow your audience to focus on the important issues.

Colors should be chosen with Caution.

Colors may be used to highlight the information, while incorrect use can conceal it. Choose easy colors on the eyes and provide a clear contrast between different data points.

Eliminating  Gauges

Although speedometers and gauges have been widely used in dashboards, newer visualization techniques that take up less space are now available. It’s recommended to consider using an easier visualization method instead of gauges.

Begin at zero

To prevent misinterpretation and ensure correct understanding of the scale, it is recommended to always start the horizontal axis of a bar chart at zero.

Display the distinction

You may highlight the differences if you wish to compare the two series. This will assist your readers in comprehending the significant areas of comparison and emphasize the value of the facts.

Pies

Pie charts may be colorful and visually appealing but are not always the best choice for displaying data. It is important to evaluate the relevance of a pie chart to the data being presented and use it only when appropriate.

Highlight what is relevantly essential

Maintain a neutral dashboard and highlight just what is relevant, such as the present location or a critical metric. This will allow the audience to concentrate on the essential points and comprehend the value of the material.

Graphs from a different perspective

Consider using a horizontal bar graph when dealing with labels or hierarchy in your data. It is recommended to explore various types of charts and graphs to effectively highlight your information.

Here are some tools that can be used to implement the mentioned Data Visualization techniques:

Art of Omission:

a. Tableau – Allows users to selectively show or hide elements of a visualization.

b. Power BI – Offers various filters and slicers to customize and refine visualizations.

Colors should be chosen with Caution:

a. ColorBrewer – Provides color schemes that are colorblind-safe and printer-friendly.

b. Adobe Color – Allows users to create, save, and export color schemes.

Eliminating Gauges:

a. D3.js – A JavaScript library that can create custom visualizations and eliminate gauges.

b. Plotly – Offers various visualization types that can replace gauges, such as bullet charts.

Begin at zero:

a. Microsoft Excel – Allows users to manually set axis limits and customize the display of data.

b. ggplot2 – A popular R package that includes the ability to set axis limits and control the display of data.

Display the distinction:

a. QlikView – Offers various charts and tables to highlight the difference between data points.

b. Highcharts – Provides a wide range of customizable chart types to display distinctions.

Pies:

a. Google Charts – Provides a variety of pie chart customization options.

b. Chart.js – A JavaScript library that can create customizable pie charts.

Highlight what is relevantly essential:

a. Plotly – Provides a range of charts and tables that can be customized to highlight essential data points.

b. SAP Analytics Cloud – Offers features to highlight the important aspects of a visualization, such as conditional formatting and alerts.

Graphs from a different perspective:

a. Matplotlib – A popular Python library that provides a wide range of visualization types, including 3D graphs.

b. Vega-Lite – A declarative language for creating interactive visualizations, including custom perspectives.

Excel:

Excel is a widely used spreadsheet program that also offers basic data visualization capabilities. It can be used to create charts, graphs, and other visualizations, and can be a good option for simple visualizations or data exploration.

Wrapping Up

Being able to visualise data in the data-driven world of today is essential for making successful decisions. The ability to master data visualisation is a skill that is attainable through the use of a variety of tricks and tips, and it can significantly improve one’s capacity to comprehend and analyse complex data. Anyone can become a better analyst and improve their data visualisation skills by adhering to best practices like selecting the proper visualisations, structuring data in a meaningful way, and using colour and design effectively. With these strategies in mind, people and organisations can use data visualisation to generate insight, wise choices, and significant outcomes.

The post Mastering Data Visualization: Tips and Tricks to Effectively Analyze Information appeared first on Indium.

With the use of interactive dashboards, personalised graphics, and intuitive data visualisations, Domo lets you create your own stories. Furthermore, it provides data governance and role-based access controls to guarantee that users can only access the information that they are authorised to see. To further safeguard your data visualisations, you can quickly and easily set up single sign-on and multi-factor authentication.

This guide is intended to assist beginners in learning the fundamentals of Domo and creating powerful data visualizations that drive business results.

Getting Started with Domo

To get started with Domo, you must first create an account. To do so, visit the Domo Website and register for a free trial. After you’ve created your account, log in to access the Domo dashboard. Domo’s dashboard is the primary interface for interacting with data in the software. It displays a summary of all your data connections, reports, and dashboards. To make a new report or dashboard, go to the dashboard and click the “Create” button.

Given the volume of the client’s data, the current procedure made it necessary for the SAS Data Admin to manually intervene and made it necessary for key decision makers to wait three days before viewing useful processed data.

Workflow for Domo Data Visualization

The workflow for data visualisation in Domo typically includes the following steps:

1. Data Connection

Domo offers numerous data connection options, including databases, spreadsheets, cloud-based applications, and APIs. To connect to a data source, go to the dashboard’s “Data” tab and then click on “Connections.”

Once you’ve selected your data source, follow the on-screen instructions to connect to it. You can also use Domo’s data connectors to bring in data from popular sources such as Salesforce, Google Analytics, and more.

2. Data Transformation

After connecting to your data source, you can begin transforming the data. To do so, go to the “Data” tab and then click “Transform.”

Domo offers a simple drag-and-drop interface for data transformation. Data transformation options include filtering, merging, and pivoting. You can also use the “Calculated Fields” feature to create custom calculations based on your data.

3. Data Visualization

Once your data has been transformed, you can start visualizing it. To do so, go to the dashboard’s “Visualize” tab and then click “Create.”

Domo provides a wide range of visualisation options, such as bar charts, line charts, pie charts, and more. You can also create custom visualisations based on your data by using the “Custom Visualizations” feature. Simply drag and drop the data elements you want to visualise into the visualisation builder to create a visualisation. To further customise your visualisation, you can add filters, calculated fields, and other data elements.

4. Creating Dashboards

Dashboards are an effective way to communicate insights and share data with others. To create a dashboard, go to the dashboard’s “Visualize” tab and click on “Dashboards.”

After you’ve created your dashboard, you can add visualizations, text, and images to create a comprehensive and interactive presentation of your data. You can also use the “Widgets” feature to add interactive elements to your dashboard such as charts, graphs, and maps.

5. Sharing and Collaborating

One of Domo’s most important features is its ability to share and collaborate on data. To share a report or dashboard, simply click the “Share” button.

You can share your report or dashboard with others by emailing them a link or embedding it in a web page. You can also limit who has access to the report or dashboard and what actions they can perform on it, such as viewing, editing, or commenting on it. This facilitates your teams to deliver data-driven collaboration and decision-making.

Business intelligence and data analytics depend heavily on data visualisation because this is how customers will see the outputs and outcomes they need. Check out this blog to learn more.

Domo vs. Tableau vs. Power BI

When comparing top BI tools, many factors must be considered. Keeping track of all business affairs is becoming increasingly difficult for any company, as large volumes of data pose a challenge to companies of all sizes. However, each of these three tools is capable of solving them in its own way.

Domo: How Can It Benefit Your Business?

1. Better Decision Making: By providing a centralized platform for data analysis, visualization, and collaboration, Domo enables businesses to make data-driven decisions, leading to improved outcomes and increased efficiency.

2. Improved Insights: With its powerful visualization options and ability to integrate with a wide range of data sources, Domo helps businesses gain new insights into their data, enabling them to identify trends, patterns, and opportunities.

3. Increased Productivity: By automating manual data collection and cleaning processes, Domo saves businesses time and resources, freeing up employees to focus on more strategic tasks.

4. Better Collaboration: With its sharing and collaboration features, Domo makes it easy for teams to work together on data, improving communication and collaboration among team members.

5. Improved Data Management: With its centralized platform for data management and analysis, Domo makes it easier for businesses to keep track of their data, reducing the risk of data loss or corruption.

6. Increased Flexibility: Domo’s cloud-based platform and ability to integrate with a wide range of data sources makes it easy for businesses to adapt to changing needs, increasing their ability to respond quickly to new opportunities.

7. Better Data Security: With its robust security measures, Domo helps businesses protect their data and ensure the privacy of their information.

Overall, Domo helps businesses turn data into insights and drive results, enabling them to make informed decisions, improve outcomes, and stay ahead of the competition.

Conclusion

When comparing Domo vs. Tableau vs. Microsoft Power BI, consider who will be using these tools.

Power BI is designed for the joint stakeholder, not the data analyst. As a result, the interface is more reliant on drag and drop and automatic features.

Tableau is equally powerful, but the interface isn’t entirely intuitive, making it more difficult to use and learn. Domo, on the other hand, is ideal for businesses looking for an all-in-one cloud-based data and analytics solution. It is used by many customers to supplement existing solutions. Given its flexibility and breadth, domo is a good choice for any organization looking to get more value from its data.

Domo is a powerful data management, analysis, and visualisation tool. Domo’s user-friendly interface, extensive data connections, and powerful visualisation tools make it simple for businesses to turn data into insights and drive results. Domo is a valuable tool for any organisation looking to harness the power of data, whether you are a beginner or an experienced data analyst.

We’ll get to the next interesting topic soon.

I hope this was useful.

Happy Reading…!!!!!

The post Domo for Dummies: A Guide to Creating Powerful Data Visualizations with Domo appeared first on Indium.

Many businesses are finding their existing infrastructure that includes servers and racks to be limiting their ability to meet their need for increased storage space and compute power. In the traditional architecture, often businesses went for proprietary end-to-end solutions right from centralized data collection to storage, and analysis, to optimize resources and minimize costs but lost control of their own data.

As technology grows by leaps and bounds, businesses are also facing the problem of the tools being unable to up with the changing times and storage being insufficient for their growing needs. The cost of expanding infrastructure is also formidable, needing viable alternate solutions.

According to a McKinsey report, businesses are trying to simplify their current architectural approaches and accelerate deliveries across data activities such as acquisition, storage, processing, analysis, and exposure, to create a modern infrastructure that can support their future analytics and BI efforts. For this, six foundational shifts are being effected on data-architecture blueprints, leaving the core technology stack untouched. This can result in an increase in RoI due to lower IT costs, improved capabilities productivity, and lower regulatory and operational risk.

The Six Features of a Modern Data Ecosystem

As per the report, the six foundational shifts that facilitate the creation of a modern data ecosystem to support future analytics and BI efforts include:

1. Shifting to Cloud-based Data Platforms: Cloud-based solutions from providers such as Amazon, Azure Google, are disrupting the data architecture approach for sourcing, deployment, and running data infrastructure, platforms, and applications at scale. Two key components of this revolution are serverless data and containerized data solutions.

a. Serverless platforms such as Amazon S3 and Google BigQuery eliminate the need for installing and configuring solutions or managing workloads while enabling businesses to build and manage data-centric applications at scale at almost no operational overhead.

b. With containerized data solutions using Kubernetes, businesses can decouple compute power and data storage while automating the deployment of additional systems.

2. Real-time Data Processing: Real-time data streaming is a cost-effective solution that allows data consumers to receive a constant feed of the data they need by subscribing to relevant categories from a common data lake that is the source and retains all granular transactions. It can be of three types: messaging platforms such as Apache Kafka; streaming processing and analytics solutions such as Apache Spark Streaming, Apache Kafka Streaming, Apache Storm, and Apache Flume; alerting platforms such as Graphite or Splunk

3. Modular Platforms: A modular data architecture that leverages open source and best-of-breed components provides businesses with the flexibility to discard old technologies and embrace new ones with the least disruptions using data pipelines and API-based interfaces and analytics workbenches. They also facilitate the integration of disparate tools and platforms to connect with several underlying databases and services.

4. Decoupled Data Access: For effective reuse of data by different teams, businesses can provide limited and secure views and modify data access by exposing data through APIs. It also enables quick access to up-to-date and common data sets. As a result, analytics teams can collaborate seamlessly and accelerate the development of AI solutions. The two key components that facilitate this are an API management platform or API gateway and a data platform.

5. Domain-based Architecture: Instead of a central enterprise data lake, domain-driven data architecture designs help businesses customize and accelerate time-to-market new data products and services. Data sets can also be organized in a more easily consumable manner for domain users and downstream data consumers. Some of the enabling features of this architecture are its data-infrastructure-as-a-platform model integrated with data virtualization techniques, and data cataloging tools.

6. Flexible, Extensible Data Schemas: The traditional, predefined, and proprietary data models built into highly normalized schemas tend to be rigid, limiting the addition of new data elements or data sources due to the risk to data integrity. Therefore, with schema-light approaches with denormalized data models and fewer physical tables, data can be organized for optimizing performance, agility, and flexibility. This is facilitated by components and concepts such as Data-point modeling, graph databases, dynamic table structures, and JavaScript Object Notation (JSON).

This piece of content might be of your interest: Modern Data Analytics And Modern Data Architecture

Indium Approach

A McKinsey Digital report shows that only 14 percent of companies launching digital transformations are able to see sustained and substantial improvements in performance. This is because, though there are several digital engineering solutions and analytics service options available to draw insights through business intelligence and analytics, , organizations are unable to suit the right architecture for their business. What is popular or obvious may not be the right fit for them.

Indium Software is a data engineering specialist offering cross-domain and cross-functional expertise and experience to understand the unique needs of each business. We use commercial and open source tools based on the cost and business requirements to meet your unique data engineering and analytics needs.

To accelerate your data modernization journey to make your business future-ready, we help you with large-scale data transformation and Cloud Data & Analytics. To ensure the efficiency of your data modernization process, we help you with aligning your data management strategy with your business plan.

Our solution encompasses:

ETL Modernization: The three-step process of extract, transform, and load is designed to help you overcome your business challenges.

Data Governance: User- and role-based data access ensures data security, privacy, and compliance while enabling informed decision-making.

Data Visualization: Our experts use cutting-edge business intelligence solutions to enable data visualizations for actionable insights

Data Management: Data abnormalities are identified as they occur, reducing time and money in rectifying mistakes.

The post Support Your Analytics and BI Efforts for the Next 10 Years with a Modern Data Ecosystem appeared first on Indium.