Benefits of Data Mesh

Data mesh has been generating a lot of buzz recently in the business intelligence world. This is because businesses are always trying to improve and scale. Due to its scalability and democratization features, data mesh can massively help with data requirements for your business and meet your increasing needs. It’s a relatively new concept that continues to produce optimal outcomes when data is concerned. Although, its true potential has not been reached yet. Continuous modifications are enhancing the data platform architecture to obtain new heights to its power. 

What is a Data Mesh?

In simplistic terms, data mesh is a paradigm that is both architectural and organizational. It’s an innovative way to prove that massive amounts of analytical data don’t need to be centralized or can only be used by a specialized team to gain the necessary value from the information.  

There are four main principles that this paradigm follows:

  • Decentralization of architecture and data ownership that is domain-oriented
  • Focus on data provided as a product that is domain-oriented
  • Supporting self-serve data infrastructure by using a platform that promotes the use of autonomous, domain-oriented data teams. 
  • Ecosystems and interoperability are achieved by federated governance.

Why choose a Data Mesh?

There are many benefits as to why businesses should use a data mesh. If a company is looking to become data-driven, data mesh helps increase customer personalization and improve customer experience. Not only does it drastically increase efficiency by reducing your operational costs and employee time, but it also gives more in-depth business intelligence insights.

If you have a large number of domains, the data process can be highly complicated. For domain-based data ownership products that have been federated, a data mesh helps automate the right strategies to make it as efficient as possible. Thus, a data mesh is an essential step in improving the democratization of crucial data. 

Data Lakes vs. Data Mesh: What’s the difference?

Data lakes are great if you are looking for one centralized system to complete all your data needs. However, data lakes can hold you back in achieving your goals when you scale your business. This is where a data mesh comes into play. A data mesh system gives employees more control over large amounts of data. However, as data is used for various things, having a less centralized system is necessary to complete data transformations in the most efficient way possible. Data lakes are great for smaller organizations. However, for larger companies that need lots of data to be processed, a data mesh is required to speed up their processes through autonomy and a more flexible system. This saves tons of time for data teams, giving those using this system a distinct edge over their competitors. 

What’s a Data Mesh score?

A data mesh score is mainly based on how complicated your processes are. It also applies to how many systems or domains you have, the size of your data team, and the priority of data governance. If you have a high data mesh score, this means that your current processes would best benefit from using a data mesh. 

Observability for Data Mesh 

By measuring the internal states of a system by examining what is produced, businesses can analyze chains with more control and identify crucial elements. Data mesh helps ensure domain ownership when observability is concerned and offers these benefits by using self-serve capabilities:

  • Quality metrics in data product 
  • Encryption for data at rest and in motion
  • Monitoring, alerting, and logging in data product 
  • Data product schema
  • Data production lineage
  • Discovery, catalog registration, and publishing in data product 
  • Data governance and standardization

These core standardizations help give businesses high-end observability when utilized. Furthermore, it provides the ability to scale individual domains throughout the entire observability process. 

Data as a product using Data Mesh

This is achieved through the ownership of data being federated to domain data owners, providing more control and allowing them to hold accountability when supplying data as products. However, during this process, owners are supported by self-serve data platforms to reduce the technical knowledge needed for data mesh to work.

In addition, a new system of federated governance that is automated to ensure interoperability of data products that are domain-oriented is required. All these factors allow data to be decentralized, helping enhance the experience received by data consumers. Businesses that maintain a high pool of domains that require various systems and teams to produce data can benefit from data mesh, along with those with a range of set data-driven access patterns and use cases. 

Challenges of Data Mesh

Although the current data mesh has tons of benefits, there are currently a few challenges that you may face. Many domain experts are not knowledgeable in using the specific domain programming languages which the data mesh may be using. On top of this, many programs in the data mesh are not API compatible. This can sometimes make it difficult for some businesses to complete their required tasks efficiently.

Putting Data Mesh 2.0 into practice

Digital transformation can be a complex process, primarily when data mesh is implemented on large networks. However, with version 2.0 coming soon, many of its advantages will cancel out many of the current challenges of Data Mesh 1.0 while significantly improving network processes. For more information on ensuring a smooth process, contact us today. 

Understanding Data Visualization from a UX Designer’s Perspective

In today’s digital world, the number of connected devices that generate data has increased exponentially. Recent research by IDC revealed that the global data volume would reach 163 trillion gigabytes by 2025. With more businesses relying on this data for decision-making, the design of business dashboards has gained prominence in the past decade. UX designers often need to coherently and visually communicate quantitative data to their teams and users. Thus, it’s paramount for them to follow the best practices in making data representations engaging. With the power of data visualization, UX designers can create effective analogies that can simplify complex datasets for human minds.

At Radiant Digital, we help clients make the most of their business data by leveraging data visualization in UX. In this blog, we focus on how data visualization is a crucial enabler for decision-makers to simplify concepts, identify new patterns, and obtain actionable insights for better decision-making.

What is data visualization?

Data visualization is a representation technique that involves restructuring datasets to obtain actionable insights. It uses graphs, charts, images, and other formats to convey complex ideas and logic better. UX designers must familiarize themselves with visualization to best represent data in their designs.

What does data visualization offer?

  • Simplicity: Data visualization simplifies data analysis by converting data into color bars, lines, circles, and other visual attributes. The human brain can comprehend different datasets by converting them into easy-to-understand and communicating trends, patterns, behaviors, and conclusions.
  • Fidelity: For a given context, a colored chart/graph can enhance the quality of your UX design, including its look and feel. On the other hand, data tables make data hard to understand by individuals who are not experts.

Benefits of data visualization

  • Data simplification: A large volume of unstructured data can seem overwhelming for users. Data visualization makes it easy to comprehend by blending pie charts, graphs, line charts, etc.
  • Expedited decision-making: Data visualization helps gain quick insights into many data patterns. Hence, leaders can make informed decisions based on them with more confidence and without manual intervention.
  • Personalization to meet business needs: When UX designers handle raw datasets, they can use custom designs to personalize and represent them. As a result, they can render different visualization perspectives and deliver value to other user groups using the same data. For example, the same data can represent information differently to marketers, finance managers, and HR teams.

Critical considerations for data visualization

  • Capturing the intended message: When focusing on representing complex datasets, you should emphasize accuracy apart from efficiency and transparency: select chart attributes, colors, and designs to communicate information correctly. The type of chart you’ll use should be dependent on the context itself. So, keep checking on your goals to ensure that the intended message is not getting lost in the visualization project.
  • Designing to populate a large volume of datasets: The data visualization placeholders created initially may not always accommodate the required datasets. This results in a critical communication gap between the actual data and the framework. In such situations, UX designers should build a scalable data framework that flexibly accommodates large datasets.
  • Understanding the tools and technologies: You should know about your handling situation and which visualization tools/components will fit the best in such cases. Implementing an incorrect feature can mislead the audience and may even fail to communicate the underlying information. Instead, your intent should be to convey full info with suitable graphs and colors.
  • Embracing inclusivity: Adding colors to data visualization doesn’t add visual attractiveness. However, a study by Salesforce has revealed that colors are significant factors behind consumer decision-making. In addition, this study has shown how the application of different color palettes can add aesthetic value to other datasets. Here’re some tips that can be useful for you in this regard: Use various labels and icons; try to use colors with high contrasts; use colors and patterns to communicate different types of information.
  • Handling distortion: Taking care of data distortion is a must during a data visualization project. Remember that you’re trying to tell a story here, and distorted data can become a barrier there. Using proper color choices, appropriate data points, befitting data charts is essential in data visualization. In addition, you don’t want the audiences to reach a wrong conclusion from distorted datasets.

Methods to visualize your data via UX design

Bar graphs

If you’re dealing with datasets that can change over the years or are based on specific categorizations, bar charts would be ideal for representation. Here are a few helpful tips.

  • Maintain a chronological order of information represented by bars.
  • Include two axes: one for the timeline and the other for quantities.
  • If you’re trying to visualize datasets with multiple categories and don’t have any time restrictions, you can place the bars in either most to most minor or least to most orders.

Line graphs

Line graphs help visualize specific datasets for a particular period. Combining them with bar charts helps visualize critical business datasets effectively. However, line charts represent deviations more efficiently and organically.

Pie and donut charts

Pie charts and donut charts are critical data visualization attributes. However, these charts are often misused, considering most people fail to understand their purpose and the type of data best suited for them.


Heatmaps contain large areas, often parted into different sections, cells, colors, etc., to represent a content group. For example, in heatmaps, dark shades represent popularity or high frequency, while lighter shades indicate lesser traffic.


Edward R. Tufte once said, “the essential test of design is how well it assists the understanding of the content, not how stylish it is.” Data visualization through UX design can simplify information to a wide range of users.

At Radiant Digital, we apply the best practices in data visualization to make data genuinely valuable for enterprises and their customers. Connect with us to transform how you represent information.