Introduction

The opening paragraph sets the tone for the article and introduces the topic, which is the future of dashboard design. It also emphasizes the importance of keeping up with the latest trends in dashboard design to stay ahead of the curve.

1. Minimalism and simplicity

In the past, dashboard design was all about displaying as much information as possible in a single interface. However, the trend has shifted towards minimalism and simplicity. In 2023 and beyond, users will expect clean, easy-to-navigate interfaces that prioritize the most important information.

2. Personalization

Personalization is another trend that’s likely to shape the future of dashboard design. Users are no longer content with generic interfaces; they want dashboards that reflect their individual preferences and needs. As a result, designers will need to create interfaces that allow users to customize their dashboards according to their preferences.

3. Data visualization

Data visualization has always been a crucial component of dashboard design, but in 2023 and beyond, it will become even more critical. As businesses become more data-driven, users will expect dashboard interfaces that allow them to interact with data in new and exciting ways. Dashboards will need to provide meaningful insights and actionable information through data visualization.

4. Voice-activated interfaces

As voice-activated assistants become more common, we can expect to see voice-activated interfaces in dashboard design. These interfaces will allow users to interact with their dashboards using voice commands, making it easier to access information without having to navigate through multiple screens.

5. Augmented reality

Augmented reality is another trend that’s likely to shape the future of dashboard design. With AR, users will be able to interact with their dashboards in real-time, overlaying data and information onto the physical world. This will allow users to access information without having to switch between different applications.

6. AI-powered analytics

Artificial intelligence (AI) is already being used in many aspects of dashboard design, but in 2023 and beyond, we can expect to see more AI-powered analytics. This will enable dashboards to provide more meaningful insights, making it easier for users to make data-driven decisions.

7. Mobile-first design

Mobile devices are now the primary way that many people access the internet, and dashboard design is no exception. In 2023 and beyond, we can expect to see more dashboard interfaces designed specifically for mobile devices, prioritizing ease of use and accessibility on smaller screens.

8. Gamification

It is the use of game elements in non-game contexts, and it’s becoming increasingly popular in dashboard design. Gamification can be used to incentivize users to engage with their dashboards, making it easier to access important information and providing a more engaging user experience.

9. Real-time updates

Real-time updates are another trend that’s likely to shape the future of dashboard design. As businesses become more data-driven, users will expect dashboards that provide real-time updates on important metrics and KPIs. This will enable users to make data-driven decisions in real-time, rather than relying on outdated information.

10. Accessibility

Accessibility is becoming an increasingly important consideration in dashboard design. As more people use dashboards toaccess information, it’s essential to ensure that everyone can access the information they need. Designers will need to consider accessibility standards and ensure that their interfaces are usable by people with disabilities.

11. Multi-platform integration

In 2023 and beyond, users will expect their dashboards to be accessible across multiple platforms, including mobile devices, desktops, and smart devices. This will require designers to create interfaces that can adapt to different screen sizes and input methods.

12. Security and privacy

As data becomes more valuable, it’s essential to ensure that dashboard interfaces are secure and protect users’ privacy. In 2023 and beyond, designers will need to prioritize security and privacy in their interfaces, providing robust authentication methods and encryption.

13. User testing and feedback

User testing and feedback will continue to play a crucial role in dashboard design in 2023 and beyond. Designers will need to work closely with users to understand their needs and preferences, incorporating their feedback into the design process.

Conclusion

In conclusion, dashboard design is continually evolving, and designers must keep up with the latest trends to create interfaces that meet users’ needs. In 2023 and beyond, we can expect to see minimalism and simplicity, personalization, data visualization, voice-activated interfaces, augmented reality, AI-powered analytics, mobile-first design, gamification, real-time updates, accessibility, multi-platform integration, security and privacy, and user testing and feedback shaping the future of dashboard design.

We are providing more services ReactJS Development Services | Angular Development services | Web Development Services.

FAQs

The post The Future of Dashboard Design: Trends to Watch in 2023 and Beyond appeared first on Welcome to Ahex Technologies.

What is D3.js?

D3.js (Data-Driven Documents) is a JavaScript library used for creating dynamic, interactive, and customizable data visualizations on the web. It was developed by Mike Bostock, a software engineer at The New York Times, in 2011, and has since become one of the most popular data visualization tools available.

D3.js allows developers to bind data to HTML, SVG, and CSS elements, creating dynamic visualizations that can be easily updated and customized. D3.js is open-source and free to use, making it an accessible tool for businesses and developers.

Why choose D3.js?

There are several advantages to using D3.js for data visualization. Firstly, D3.js is highly customizable, allowing developers to create unique and interactive visualizations that meet their specific needs. Secondly, D3.js is compatible with a wide range of data formats and frameworks, making it a versatile tool for different industries.

Another advantage of D3.js is its flexibility in customization and interactivity. D3.js offers a wide range of interactive features, such as tooltips, zooming, and panning, that allow users to explore and understand data in greater detail. This flexibility makes D3.js ideal for creating visualizations that are both informative and engaging.

Examples of D3.js in Action

D3.js has been used by many industry leaders to create powerful and insightful data visualizations. For example, The New York Times used D3.js to create an interactive map of US census data, allowing readers to explore population density and demographics across the country. Airbnb also used D3.js to create a dynamic pricing tool, allowing hosts to set competitive prices based on market trends and demand.

How to Get Started with D3.js

Learning D3.js can be challenging, but there are many resources available for developers and businesses looking to get started. The D3.js website offers extensive documentation and tutorials, as well as a supportive community of developers and users. There are also online courses and books available that provide step-by-step guidance on using D3.js for data visualization.

To effectively implement D3.js, it’s important to have a clear understanding of the data being visualized and the goals of the visualization. It’s also important to have a solid foundation in HTML, CSS, and JavaScript, as D3.js relies heavily on these languages.

When using D3.js, it’s important to avoid common mistakes such as overcomplicating the visualization or using too much animation. These mistakes can result in a slow and confusing user experience, making the visualization less effective.

Challenges of using D3.js

While D3.js offers many benefits for data visualization, there are also challenges to using the tool. Learning and mastering D3.js can be time-consuming and challenging, especially for developers who are new to the tool. Additionally, D3.js can have compatibility issues with some browsers and devices, making it important to test and optimize visualizations for different platforms.

Finally, developing effective visualizations with D3.js requires significant time and resources, including expertise in design, data analysis, and development. Businesses and organizations must weigh the costs and benefits of using D3.js for their specific needs.

Conclusion

D3.js is a powerful tool for data visualization, offering unique benefits such as flexibility, customization, and interactivity. While it may have a steep learning curve and require significant resources, it has been successfully used by industry leaders in various fields to create impactful and insightful visualizations. For businesses and organizations looking to make the most of their data, D3.js is certainly worth considering as a tool for powerful data visualizations.

FAQS

The post Why Industry Leaders Choose D3.js for Powerful Data Visualizations appeared first on Welcome to Ahex Technologies.

Introduction to D3.js

D3.js, also known as Data-Driven Documents, is a JavaScript library that allows developers to create dynamic and interactive visualizations for the web. D3.js is open-source, meaning it can be used and modified by anyone. It was created by Mike Bostock, a web developer at The New York Times, and has since become one of the most popular data visualization libraries on the web.

How D3.js works

D3.js works by using scalable vector graphics (SVG) and manipulating them with JavaScript. This allows developers to create complex visualizations that can be interacted with in real-time. D3.js also has a variety of built-in functions that make it easy to create different types of charts and graphs, such as bar charts, line charts, and scatterplots.

Benefits of using D3.js

D3.js has several benefits that make it a powerful tool for data visualization:

1. Customizability

One of the biggest advantages of D3.js is its customizability. With D3.js, developers have full control over every aspect of the visualization, from the data to the colors and layout. This allows for unique and creative visualizations that can effectively communicate complex data.

2. Interactivity

D3.js enables developers to create interactive visualizations that respond to user input. This means users can manipulate the data and explore different aspects of the visualization, increasing engagement and understanding.

3. Scalability

D3.js is designed to be scalable, meaning it can handle large datasets and complex visualizations without sacrificing performance. This makes it a great tool for businesses that need to analyze and visualize large amounts of data.

Examples of D3.js in action

D3.js has been used to create a wide range of visualizations, from simple charts to complex maps and diagrams. Here are a few examples:

1. Gapminder

Gapminder is a website that uses D3.js to visualize global trends in health and economics. The site features interactive charts and graphs that allow users to explore data from around the world.

2. The New York Times

The New York Times has used D3.js to create a variety of interactive visualizations, from election results to climate change data. These visualizations are often embedded directly in articles, making them an effective way to communicate complex data to readers.

3. Uber

Uber has used D3.js to revolutionizing data visualization that help drivers and riders understand traffic patterns and other data related to the service. These visualizations are an important tool for improving the user experience and making data-driven decisions.

Conclusion

D3.js is revolutionizing data visualization for the web by enabling developers to create dynamic and interactive visualizations that can effectively communicate complex data. Its customizability, interactivity, and scalability make it a powerful tool for businesses and organizations that need to analyze and communicate large amounts of data. With D3.js, businesses can gain new insights into their data and make informed decisions that drive success.

FAQs

The post How D3.js is Revolutionizing Data Visualization for the Web appeared first on Welcome to Ahex Technologies.

Data visualization is like a bridge between the data and its audience, creating a clear path for interpretation and understanding. Through eye-catching charts, striking graphs, and even interactive maps, it transforms data from a jumble of numbers to a work of art, painting a vivid picture of trends, patterns, and insights that would have otherwise gone unnoticed.

It’s not just a technique; it’s an art form that can benefit all business areas. With data visualization, marketing teams can identify their target audience’s preferences, product teams can spot areas of improvement, and finance teams can identify areas of risk and opportunity.

Understanding the Two Main Types of Data Visualization: Exploration vs. Explanation

Exploration:

Exploratory data visualization is an initial step in data analysis that enables individuals to identify trends, patterns, and notable outliers within the data. When faced with a new dataset, one of the first things individuals should do is conduct exploratory data analysis. This is where the dataset is investigated, and some of its main features are identified, laying the foundation for a more thorough analysis. At this stage, visualizations can make it easier to understand what’s in the dataset and spot any noteworthy trends or anomalies. Ultimately, exploratory data visualization helps to develop an understanding of the dataset and lays the groundwork for more extensive analysis.

Explanatory data visualization comes toward the end of the data analysis process, where the focus shifts from exploration to communication. Once individuals have conducted their analysis and figured out what the data tells them, they will want to share these insights with others. Explanatory data visualizations help tell this story, and it’s up to the individuals to determine which visualizations will help them most effectively. Choosing the appropriate type of visualization to support data communication is a critical aspect of the data analysis process.

Why is Data Visualization Important? 

Explanation:

The significance of data visualization lies in the broader context of data analytics, which plays an increasingly vital role in today’s data-driven world. According to recent estimates, the digital universe reached a staggering 44 zettabytes at the start of 2020. It is projected that roughly 463 exabytes of data will be generated globally every day by 2025. This enormous volume of data can be harnessed by data analytics to uncover insights that can inform decision-making across various domains.

For businesses, data analytics can aid in identifying patterns and trends from historical data and anticipate future outcomes. In healthcare, it can improve patient outcomes by enabling data-driven decision-making, while in finance and insurance, it can help to manage risks and prevent fraudulent activities. The role of data analytics in facilitating informed decision-making cannot be overstated, and data visualization is an essential aspect of this process.

Data visualization enables us to comprehend the insights obtained through data analytics by presenting them in an accessible manner to diverse audiences, not just data professionals. It bridges the gap between data analysts and other stakeholders who can leverage the insights derived from data analytics. Thus, data visualization plays a crucial role in unlocking the potential of data analytics to inform decision-making and drive progress across various fields.

What an Effective Data Visualization Can Bring 

Efficient data visualization offers a range of benefits that can aid in comprehending complex data sets. Making visible trends, patterns, and outliers enables a preliminary understanding of the data. Furthermore, it facilitates the comprehension of vast volumes of data efficiently and rapidly. Most importantly, data visualization allows for effective communication of insights and findings to non-data experts, enhancing the accessibility and usability of the data.

Through storytelling, data visualization can convey meaningful and impactful messages by emphasizing only the most pertinent information for a given context. Having gained an understanding of the significance of data visualization, it is imperative to examine the situations that necessitate its application.

Use Cases of Data Visualization 

Conveying Changes Over Time:

One of the most common uses of data visualization is to convey changes over time. This can be achieved using line graphs showing how a specific variable has changed over a certain period. For example, a line graph could be used to show how the value of Bitcoin has fluctuated over the past year. Seeing these changes in a visual format makes it easier to identify trends and patterns, such as sudden spikes or dips in value.

Determining Frequency of Events:

Another way to use data visualization is to determine the frequency of events. This can be achieved using histograms, which show the distribution of a specific event over a certain period. For example, a histogram could show the number of internet users per year from 2009 to 2023. By seeing this information in a visual format, it becomes easier to identify any trends or patterns that may be present.

Highlighting Relationships or Correlations Between Variables:

Data visualization can also highlight interesting relationships or correlations between variables. This can be achieved using scatter plots, which show how two variables are related. For example, a scatter plot could show the relationship between marketing spend and revenue. By seeing this information in a visual format, it becomes easier to identify any correlations between the two variables, such as whether an increase in marketing spend leads to an increase in revenue.

Conclusion 

It is the art of presenting complex data simply and intuitively. It helps us understand patterns, relationships, and trends that might otherwise be hidden in rows of numbers and statistics. The importance of data visualization cannot be overstated, as it helps businesses and organizations to make informed decisions based on data insights.

At Ahex Technologies, we understand the value of data visualization and offer cutting-edge solutions to help businesses unlock the power of their data. Our team of experts uses the latest tools and techniques to create visually stunning and interactive dashboards that make it easy to understand complex data sets. With our data visualization services, businesses can gain valuable insights and make data-driven decisions that improve efficiency, productivity, and profitability.

The post What is Data Visualization and Why is It Important? A Complete Introduction appeared first on Welcome to Ahex Technologies.

Revamp Your Data Infrastructure for Enhanced Efficiency

Be honest with yourself and consider seeking professional data analytics consulting. As companies strive to cut down on data budgets, it may be time to put certain initiatives on hold. If you can easily generate reports from a replicant database, why invest in a data warehouse and hire a full-time employee to manage it?

Likewise, if more affordable data visualization options can provide the same functionality as premium tools, consider taking advantage of them. This is also an ideal opportunity to declutter your infrastructure and part ways with underutilized vendors and contracts. While the total cost of ownership is still a crucial factor to consider, this is a chance to streamline your data infrastructure and maximize efficiency. Don’t let the lure of fancy diagrams and complex solutions lead you astray. Before diving headfirst into a complex data infrastructure, take a step back and ask yourself what you really need. Do you require only basic reporting capabilities, or are you looking to build a sophisticated data stack supporting advanced technologies like AI and machine learning? Don’t miss out on it!

Optimize Your Data Strategy: Only Pull What You Need

It’s no secret that many companies are data hoarders, storing vast amounts of information they’ll never use. According to industry estimates, between 60% and 73% of stored data goes unused. This costly habit can be particularly problematic when utilizing solutions that process and charge based on the number of rows you handle. While such practices may have been tolerated in an era of cheap money, they’re no longer sustainable.

In the past, such behavior was standard practice in a pre-cloud era when local servers were the norm. Space was at a premium back then, and data management teams were vigilant about every byte. However, in today’s world, with gigabytes and terabytes readily available, it’s time to rethink this approach.

Consider adopting a “less is more” mentality to optimize your data strategy. Collect and store what you truly need, and avoid unnecessary data pulls. By taking a more disciplined approach and taking data analytics consulting,  you can cut costs, streamline your data management practices, and reduce the risk of errors and data breaches.

Maximize Your Data ROI: Mind Your Workflows

Snowflake and other cloud data platforms have democratized access to powerful data analytics tools, making it more affordable than ever before. But as companies build increasingly complex workflows, it’s crucial to remain mindful of costs, especially when tables are misconfigured or dashboards rely on pre-aggregated data.

The days of the DBA obsessing over byte counts are long gone. With terabytes and petabytes of data being processed, inflated cloud costs can quickly become a reality. However, cutting costs and maximizing your ROI is possible by optimizing your underlying data structure.

So don’t let poorly structured data workflows drain your budget. Take a closer look at your processes, make minor adjustments where necessary, and watch your ROI soar.

Evaluate TCO

Calculating the Total Cost of Ownership (TCO) is critical when making technology decisions. Getting caught up in the latest trend or tool hype is easy, but taking a step back and assessing the long-term impact on your bottom line is essential.

In the past, it may have been acceptable to invest in a tool that required more specialized expertise, but in today’s economic climate, it’s important to question whether it’s the right choice for your business. For example, if a slightly more expensive solution can reduce the need for an extra team member to manage it, it could ultimately save you money in the long run.

It’s all about math. Take the time to carefully evaluate the TCO of your technology choices to ensure they align with your business goals and budget.

Wrapping Up 

Reducing data analytics costs in 2023 is essential; companies must remain competitive and profitable. By simplifying data infrastructure, pulling in only what is needed, being mindful of data workflows, understanding TCO, and seeking expert advice, companies can achieve significant savings without compromising on the quality of their analytics. If your company struggles to reduce data analytics costs, Ahex Technologies can help. With a team of experienced professionals and a track record of helping companies cut their data analytics spend, Ahex Technologies is well-equipped to guide you through optimizing your data infrastructure and workflows.

At Ahex Technologies, we offer various services, including data architecture, data engineering, data analytics consulting services, cloud services, and more. Our team can help you identify cost-saving opportunities, streamline workflows, and implement best practices to optimize your data analytics spend.

Check other service : Web development | AngularJS development | ReactJS development | Mobile Application development. 

The post Reducing Data Analytics Costs in 2023 – Doing More With Less! appeared first on Welcome to Ahex Technologies.

In this blog post, we will explore D3.js and its capabilities in creating stunning visualizations.

Getting started with D3.js:

D3.js is a JavaScript library that uses HTML, CSS, and SVG (Scalable Vector Graphics) to create interactive visualizations. The library can be downloaded from its official website or installed using a package manager like NPM.

To get started with D3.js, you need to have a basic understanding of HTML, CSS, and JavaScript. You can start by creating a basic HTML file and including the D3.js library in it. Once you have done that, you can start using D3.js to create visualizations.

Creating visualizations with D3.js:

D3.js provides a wide range of options to create visualizations. Here are some of the commonly used visualizations that you can create with D3.js:

1. Bar charts: Bar charts are used to compare different values using rectangular bars. D3.js provides a simple and easy way to create bar charts. You can start by defining the data and the size of the SVG container. Once you have done that, you can use D3.js to create the bars and add labels and axes to the chart.
2. Line charts:Line charts are used to show trends over time. D3.js provides a simple and easy way to create line charts. You can start by defining the data and the size of the SVG container. Once you have done that, you can use D3.js to create the lines and add labels and axes to the chart.
3. Scatter plots: Scatter plots are used to show the relationship between two variables. D3.js provides a simple and easy way to create scatter plots. You can start by defining the data and the size of the SVG container. Once you have done that, you can use D3.js to create the points and add labels and axes to the chart.
4. Choropleth maps:Choropleth maps are used to show data values on a geographic map. D3.js provides a simple and easy way to create choropleth maps. You can start by defining the data and the size of the SVG container. Once you have done that, you can use D3.js to create the map and add labels and legends to the chart.

Customizing visualizations with D3.js:

D3.js provides a wide range of customization options to create unique and stunning visualizations. Here are some of the commonly used customization options:

1. Scaling: Scaling is used to map data values to visual values. D3.js provides a wide range of scaling options like linear, logarithmic, and power scaling.
2. Interactivity:Interactivity is used to make visualizations more engaging and informative. D3.js provides a wide range of interactive options like tooltips, hover effects, and zooming.
3. Animations: Animations are used to make visualizations more dynamic and engaging. D3.js provides a wide range of animation options like transitions, delays, and easing functions.

Conclusion

D3.js is a powerful and flexible JavaScript library used for creating interactive and dynamic visualizations. It provides a wide range of customization options and can be used to create various types of visualizations like bar charts, line charts, scatter plots, and choropleth maps. With its wide range of capabilities and customization options, D3.js is a must-learn tool for anyone interested in data visualization.

Check other service : Web development | AngularJS development | ReactJS development | Mobile Application development.

The post Interactive Data Visualization with d3js appeared first on Welcome to Ahex Technologies.

1. Making Complex Data Easy to Understand

Data visualization makes complex data easy to understand by presenting it in a clear and concise manner. Data visualization tools can turn raw data into interactive charts, graphs, and dashboards that are visually appealing and easy to interpret. By providing a visual representation of the data, businesses can identify trends and patterns that may be difficult to discern from a spreadsheet or database.

2. Spotting Trends and Patterns

Data visualization can help businesses spot trends and patterns in their data quickly. For example, a line chart can show changes in sales revenue over time, while a heat map can reveal patterns in customer behavior. These insights can help businesses make informed decisions, such as adjusting marketing strategies, optimizing operations, or identifying new opportunities.

3. Improving Communication and Collaboration

Data visualization can also improve communication and collaboration across teams and departments. Visualizations are easier to understand and communicate than spreadsheets or raw data, making it easier to share insights with others. By using interactive dashboards and reports, teams can collaborate and make data-driven decisions together.

4. Enabling Real-Time Monitoring

Data visualization tools can enable real-time monitoring of key metrics, such as website traffic, social media engagement, or supply chain performance. By providing up-to-the-minute insights, businesses can identify and respond to issues quickly. Real-time monitoring also enables businesses to track progress towards goals and adjust their strategies accordingly.

5.  Enhancing Data Governance and Compliance

Data visualization can enhance data governance and compliance by providing transparency and accountability. Visualizations can help businesses identify anomalies and outliers in their data, which may indicate data quality issues or potential compliance risks. By identifying these issues early, businesses can take corrective action to ensure data accuracy and integrity.

Conclusion

Data visualization is a powerful tool that enables businesses to make sense of complex data. By presenting data in a visual and intuitive format, businesses can quickly identify trends and patterns, communicate insights, and make data-driven decisions. With the right data visualization tools and techniques, businesses can unlock the full potential of their data and gain a competitive edge in their industry.

Check more Service Web development | AngularJS development | ReactJS development | Mobile Application development | .Net development.

The post The Power of Data Visualization: Making Sense of Complex Data appeared first on Welcome to Ahex Technologies.

In this modern age, Big Data investigation is powering all that we do on the web—in each industry. 

Take the music streaming application Spotify for instance. The organization has almost 96 million clients that produce a gigantic measure of information consistently. Through this data, the cloud-based stage consequently creates proposed melodies—through a shrewd suggestion motor—in light of preferences, shares, search history, and that’s just the beginning. What empowers this are the strategies, apparatuses, and structures that are an aftereffect of Big Data examination. 

Assuming you are a Spotify client, you probably run over the top suggestion segment, which depends on your preferences, previous history, and different things. Using a proposal motor that uses information separating apparatuses that gather information and afterward channel it utilizing calculations works. This is the thing that Spotify does. 

Associations can utilize big data examination frameworks and programming to settle on information-driven choices that can further develop business-related results. The advantages might incorporate more successful advertising, new income openings, and client personalization, and work on functional proficiency. With a viable system, these advantages can give upper hands over rivals.

Big data analyses assist associations with outfitting their information and use it to recognize new options. That, thus, prompts more intelligent business moves, more effective activities, higher benefits, and more joyful clients. In his report, Big Data in Big Companies, IIA Director of Research Tom Davenport met in excess of 50 organizations to see how they utilized huge information. He observed they got esteem in the accompanying ways: 

Reduction in cost: Enormous information innovations, for example, Hadoop and cloud-based investigation bring huge expense benefits with regards to putting away a lot of information – in addition to they can distinguish more proficient methods of working together. 

Quicker and efficient decision-making: With the speed of Hadoop and in-memory investigation, joined with the capacity to break down new wellsprings of information, organizations can examine data promptly – and settle on choices dependent on what they’ve realized. 

Latest product and services: With the capacity to measure client needs and fulfillment through investigation comes the ability to give clients what they need. Davenport draws up that with massive information and investigation, more organizations are creating new items to label clients’ issues.
Check our services for Mean Stack Development | Mern Stack development

Benefits & Advantages of Big Data Analytics

Management of risk 

Big Data analytics helps an organization to identify fraudulent activities and discrepancies. The firm leverages it to come down with a list of suspects or root causes of issues.

Product Development and Innovations

Big Data analytics is used to analyze how efficient the engine designs are and if there is any need for improvements. It is a great assistant in automobiles or any such industry. It is not wrong to say that big data analytics system has diverse uses. 

Quicker and Better Decision Making Within Organizations

Big Data analytics assists in making strategic decisions. For instance, the organization weights it to decide if a specific location would be suitable for a new outlet or not. They will analyze many different features, such as population, demographics, accessibility of the location, and many more.

Improve Customer Experience

Big Data analysis is useful in improving consumer experiences. They examine tweets to find out their clients’ experiences regarding their journeys, delays, and so on. It identifies negative comments and does what’s needed to remedy the situation. By publicly addressing these issues and offering solutions, it helps the company to build good consumer relations.

The post What is the purpose of big Data Analytics? appeared first on Welcome to Ahex Technologies.

It helps in absorbing big data groups in less time. It makes the information more appealing to the audience and helps to retain their full attention during the presentation. Almost every industry has used pie charts and bar graphs to convey data for a long time. The recent years have witnessed some changes in the trends of data visualization.

Types of data visualization:

Data visualization can be categorized into five main categories:

• Temporal – It includes all the data visualizations which are linear and one-dimensional. Some examples are as follows, line graphs, scatter plots, and timelines.
• Network – These are the visualizations that show their relation with data graphs in the same network. Examples are matrix charts and alluvial diagrams.
• Hierarchical – This includes the visualizations that represent data sets within data sets. In simpler words, they convey groups of data in a visual format usually with a single point of origin. Some examples are tree diagrams and ring charts.
• Geospatial – This refers to the visualization of data over a physical location in the form of data graphs over maps. One can use them to demonstrate the distribution of a material over a certain location or to exhibit a specific target set by a company to ensure its productivity. Some examples are as follows, flow maps, cartograms, and heat maps.
• Multidimensional – It includes all the data visualizations which are multi-dimensional. This comprises all the data with two or more variables present. Some examples of multidimensional data visualizations are pie charts and histograms.     

Uses of data visualization:

These techniques are so effective that almost every industry uses them. The variety of options available to represent your data makes it a viable option. Some spheres where it is used are as follows:

• Marketing and research: The use of data visualization is most commonly seen in the sales and marketing fields. Companies use pie charts and flow maps to represent the market acquired by them in a certain geographic location or to show the clients’ location over the map.
• Political campaigns: Politicians use the geospatial form of data visualization to represent their vote bank in a certain area. This helps them to plan out the campaigning in an organized manner.
• Educational institutions: Institutions like schools and colleges use data visualization tools to keep the data of different students class-wise. This enables them to maintain a record of the academics, attendance, and extra credits of the students.
• Healthcare and well being: Hospitals and government agencies employ data visualization techniques to maintain a record of the patient’s activity and distribution of a disease over a certain locality.
• Financial analysis: It is used by market analysts to determine the trends and patterns in the stock market. Visual representation of the data like closing price and opening price helps in a  better understanding of the market situation.

Importance of data visualization:

It is the representation of tabular data in a visual format. Data Visualization is important as it helps in conveying the data more efficiently and in a lesser time. It is used to determine the following:

1. Change in data over time – Data visualization helps to adequately represent the changes or trends in data over a particular period. The visual display of data makes it easier to determine a pattern and make out the changes and reasons behind it.
2. Frequency – Data visualization provides the best aid to keep a track of the frequency of data, like measuring the number of products a company supplied within a period. 
3. Market research – The visual representation of financial data like the price of stocks, ensure accurate assessment of the trends prevailing in the market. It is less prone to mistakes and provides a better result in relatively less time.
4. Correlations – Data visualization determines the relation between two or more independent variables. Doing the same with just tabular data will be time-taking and less precise.
5. Determining the reward per risk – Measuring the value of risk taken for a certain reward is difficult without visual representation as it involves large spreadsheets and tons of data. Placing the same data in a visual format ensures that the information is conveyed at a more efficient rate. 
6. Accurate analysis – Data visualization ensures the accurate reading of the information and data conveyed through it. Tabular data can be misleading sometimes as it does not have much impact on the brain. On the other hand, visual data is easier to grasp and retain, which helps in deriving the analysis accurately.
7. Business Insights – Visually represented data enables you to gain more information from a network in less time. It’s useful while managing the insights of your business over a particular period.
8. Scrutinizing errors – Finding and rectifying errors within a data set is much easier if the data is represented visually. Owing to the exhaustive amount of data present, people can overlook some mistakes in a tabular or spreadsheet form. This is where data visualization proves significant. 

CONCLUSION

Data visualization should be adopted as a prominent way to convey information. Visual formats like bar graphs and pie charts help to assess the patterns and changes in data with just a look at the graphs. The readings are far more accurate and obtained in relatively less time. Several industries use it to represent different types of data and information, making it the most crucial step of data analysis.

The post What is data visualization and how is it important? appeared first on Welcome to Ahex Technologies.