Data Visualization vs. Data Analytics - What's the Difference? - Fingent (2024)

With data now being a critical source of competitive advantage, enterprises are cutting across size and geographies seeking newer methods to identify and analyze the data they generate. Most enterprise decision makers are now familiar with intuitive graphs, pie-charts, and other forms of visualizations that try to make sense of sales, revenue, and other aspects of company operations. However, the usefulness of such data visualizations depends on the effectiveness of the data, or how the data is used to come up with conclusions. A balanced approach in data visualization and analytics is thus pivotal in formulating an effective data strategy.

Many enterprises confuse data analytics with data visualization. Both allow users to make sense of data and obtain the relevant metrics that helps in better decision making. In today’s age of information overload, where data generated is multiplying every 3 years, interpreting them turns out to be the need of the hour. On the other side, we have these forecasts and projections hinting at an exponential growth in revenue for the big data software market in the coming years. The confusion, however, stems from the fact that both data visualization and analytics represent data in visual interfaces.

While there is considerable overlap between the two, data analytics deals with data at a much deeper level, compared to visualization. An end-to-end business intelligence solution consists not just of the front end dashboard, which transforms data into a visual context, but also tools and algorithms at the backend.

Data Visualization vs. Data Analytics - What's the Difference? - Fingent (1)

Related Reading:Find out how enterprises are relying on business intelligence platforms to leverage data for driving innovation and growth.

Difference between Data Visualization and Data Analytics

Data visualization represents data in a visual context by making explicit the trends and patterns inherent in the data. Such pattern and trends may not be explicit in text-based data. Most tools allow the application of filters to manipulate the data as per user requirements. The traditional forms of visualization, in the form of charts, tables, line graphs, column charts, and many other forms, have of late been supplanted by highly insightful 3D visualizations.

Data analytics go a step deeper, identifying or discovering the trends and patterns inherent in the data. Data visualizations, while allowing users to make sense of the data, need not give the complete picture. Visualizations are only as effective as the data used to prepare the visualization in the first place. Feeding visualization engine with incomplete data will render half-baked, obsolete, or erroneous visualization.

Data Visualization vs. Data Analytics - What's the Difference? - Fingent (2)

Moreover, today’s enterprises gather data from multiple sources, and store data in multiple repositories, including many silos. In such a state of affairs, gathering comprehensive data for visualization is a tough ask. While visualization tools mostly deal with raw and unstructured data, end-to-end analytic tools employ data mining algorithms to cleanse the data, evaluates the cleansed data using different evaluation models and software tools, subject it to algorithms, and then decides how to display the results.

Data Integration as the first step of the process

The essential prerequisite of effective analysis is consolidating all data in one central place for effective analytics. While there are analytical engines capable of collecting data from multiple silos, consolidating data in one place enables a “single version of the truth,” preventing duplicating and contradicting data from distorting the visualizations. Until recently, many companies use to aggregate data manually, on an ad-hoc basis, as it was easier this way than invest time and effort in a solution for the same.

However, the sheer increase in the volume of data in recent times makes manual aggregation impossible. A number of software tools and platforms cater to the need, by providing automated solutions. The add-on benefit of such automated solutions is data cleansing, to eliminate misnamed, outdated, and messy data, inevitable in a set-up which involves disparate sources and users.

Data Analysis as the second step of the process

The logical step after aggregating and cleansing data is subjecting the data to analysis or performing calculations on the data. As today’s business environment has grown complex, data analysis also involves complex calculations. The need for speed introduced multi-stage formulas that perform a number of calculations simultaneously. Visualization tools focus on reporting data rather than analyzing it, and as such, most tools are limited, with restrictions in the possible aggregations per formula.

In contrast, truly end-to-end analytical solutions allow users to create complex formulas, working in separate sources. The software undertakes the required pre-calculations automatically, making life easy for the user. Businesses seeking to thrive in today’s fast-paced business environment need analytic tools which update data and facilitate collaboration in real-time. The leading analytics tool in the market today, such as IBM Cognos play into this need, by streamlining available data and leveraging plug-and-play interfaces to derive colorful dashboards.

Companies in the retail sector have already leveraged the power of data analytics to streamline their business processes and thus maximize revenue. Analytics and visualization have aided them discover patterns and actionable insights pertaining to customer behavior helping managers plan and develop initiatives. Find out how retailers are harnessing data analytics to aggregate their customer data for accentuating efficiency and profitability.

Comprehensive business intelligence analytics suites offer predictive modeling and other types of advanced analytics based on complex algorithms compiled using languages such as R and Python. Advanced data visualization, data warehousing and dashboards make up some of the key technologies used by business intelligence platforms currently. The best solutions offer unmatched flexibility to the user, with the ability to combine data any way the user requires or prefers.

Moreover, the latest analytical platforms apply modern tools such as natural language processing (NLP) and chatbots, making it easier for users to perform the required calculation or input their queries with ease. The latest advances, such as location-based intelligence increases the potential of analytics and actionability of the insights in a big way.

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Data Analytics or Visualization: Which comes last?

While the most effective visualization is based on the data subject to analytics, visualization need not always be the end of the process or the culmination of the project. Many situations adopt data analytics and visualization in a cyclical spree.

Consider the case of Zao, who runs a host of machine learning and predictive modeling applications to gauge the success of targeted email campaigns. Data visualization enters early in the process, with the analysts pulling out specific variables into a graph to identify any possible correlation, or to identify metrics such as mean and median averages, data spread and standard deviation metrics, to get a sense of the scope of the data.

Both data visualization and analytics deal with data. Visualization tools generate a beautiful and easy to comprehend report, but only robust backend capability, which handles the messy data and processes the data by applying advanced algorithms, gives an accurate report. Data analytics offers the complete picture, while visualization summarizes the available data in the best possible way. The best solutions co-opt both.

Your data is growing at exponential rates. The insights from data can help the managers and business owners make decisions that can improve turnaround times, efficiency and more.

Related Reading:Get an insight into the hows of using data analytics to scale and grow your business.

Fingent a leading custom software development company, we house a team of skilled business analysts and data visualization experts. With their expertise, we can offer you exceptional data visualization services. Our team can assist you in implementing robust data analytics software that seamlessly integrates data from various sources, providing rich and insightful visual solutions that uncover the true story behind your data. Partner with Fingent today to unlock the power of data visualization and make informed business decisions.

Data Visualization vs. Data Analytics - What's the Difference? - Fingent (2024)


Data Visualization vs. Data Analytics - What's the Difference? - Fingent? ›

Analytics enables businesses to better understand their customers, improve advertising campaigns, personalize content, and improve bottom lines. Data Visualization provides an accessible way to see and understand trends and patterns in data by representing the information in graphical or pictorial formats.

What is the difference between data visualization and data analytics? ›

Data analytics is the part of this process that extracts meaning from this data. Data visualization can then be applied to this meaningful data, helping to effectively convey the insights found in it, in the form of easy-to-understand visual narratives that can be shared internally and with external stakeholders.

What is the difference between information visualization and visual analytics How do you answer? ›

Information visualization typically focuses on abstract data, that is, data without any agreed-upon depiction, such as financial data, text, statistics, databases, and software. Visual analytics emphasizes analytical reasoning about data and combines computational analysis techniques with interactive visualizations.

What is the difference between visual and analytics? ›

Visualization tools focus on reporting data rather than analyzing it, and as such, most tools are limited, with restrictions in the possible aggregations per formula. In contrast, truly end-to-end analytical solutions allow users to create complex formulas, working in separate sources.

What is the difference between data and information in terms of analyzing and visualizing data? ›

Data typically comes in the form of graphs, numbers, figures, or statistics. Information is typically presented through words, language, thoughts, and ideas. Data isn't sufficient for decision-making, but you can make decisions based on information.

Which one is the biggest difference between data visualization and visual analytics? ›

Data visualization is what provides clarity to data-driven insights and it's what enhances understanding throughout an organization. In this diagram, visual analytics is shown to be the foundation for interactive data, thereby demonstrating how the two are connected.

What is visualization and analytics? ›

Visual analytics is the use of sophisticated tools and processes to analyze datasets using visual representations of the data. Visualizing the data in graphs, charts, and maps helps users identify patterns and thereby develop actionable insights. These insights help organizations make better, data-driven decisions.

What is the relationship between data analytics and data visualization? ›

Analyzing raw data benefits from the aid of visualization, making trends and patterns clearer and differences more apparent. In essence, data analysis uncovers insights, while visualizations skillfully convey those insights in a clear and meaningful manner.

What is data visualization in data analytics? ›

Data visualization is the process of using visual elements like charts, graphs, or maps to represent data. It translates complex, high-volume, or numerical data into a visual representation that is easier to process. Data visualization tools improve and automate the visual communication process for accuracy and detail.

What is the difference between data and information visualization? ›

While data visualizations are a representation of raw data, information visualizations represent the data visually in the context of business logic. It's similar to asking what the difference between information and data is. Information is data with meaning attached to it.

Why data analytics and visualization? ›

The importance of Data visualization is – analyzing complex data, identifying patterns, and extracting valuable insights. Simplifying complex information and presenting it visually enables decision-makers to make informed and effective decisions quickly and accurately.

What are the 3 vs data analytics? ›

Dubbed the three Vs; volume, velocity, and variety, these are key to understanding how we can measure big data and just how very different 'big data' is to old fashioned data.

What is the role of data visualization and visual analytics? ›

Data visualization is the practice of translating information into a visual context, such as a map or graph, to make data easier for the human brain to understand and pull insights from. The main goal of data visualization is to make it easier to identify patterns, trends and outliers in large data sets.

What is the difference between data visualization and data storytelling? ›

What are data visualization and data storytelling? Data visualization conveys information through images, while data storytelling creates a narrative with the data.

What is the difference between big data and data visualization? ›

Big data analytics generate insights by crunching huge data sets. These insights may be in the form of a reduced data set or summary. Data visualization in big data analytics is representing these insights in the form of graphical charts or reports for easier interpretation by decision makers.

What are the basics of data visualization? ›

Data visualization is the representation of data through use of common graphics, such as charts, plots, infographics and even animations. These visual displays of information communicate complex data relationships and data-driven insights in a way that is easy to understand.

Is data visualization and visual analytics the same? ›

Data visualization is representing data in a graphical format to communicate information effectively. Visual analytics uses interactive visual interfaces to explore and analyze data in real time, enabling users to uncover hidden patterns and gain insights.

Is data visualization part of analytics? ›

Data analytics and data visualization, while they are different disciplines, have a closely related if not symbiotic relationship. Data visualization supports data analytics in that it clearly communicates the outcomes and patterns in data, and gives end users a new perspective on that information.

Do data analysts do data visualization? ›

Data scientists & analysts use Data Visualization to communicate important findings with others. Learn more about these tools and how they differ by role. Data visualization is the process of transforming data into visual representations to make information easier to understand, analyze and communicate.


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