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What Is Data Visualization: Typical Errors and Dangers

by Jeniffer
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To put it simply, data visualization is the process of representing data graphically using charts and graphs.

Data visualization is used in the marketing domain to swiftly and clearly transform complicated data sets into usable insights, saving marketing professionals from having to sift through reports and spreadsheets in search of pertinent information.

Important insights and trends are made evident by graphically emphasizing vital data, which facilitates quicker and more efficient marketing decision-making.

Data visualization’s advantages for marketing teams

For marketing teams, data visualization can offer a number of benefits, such as:

1. Quicker decision-making
Making decisions quickly can give you a big competitive advantage. Data visualization is excellent at helping marketers make decisions more quickly by enabling them to quickly assimilate findings.

2. Recognizing trends
Trends and patterns in data can be more easily seen through data visualization than they would be in reports that mostly rely on tables. Marketing teams find it easier to spot possibilities and problems that need to be fixed because of this visibility.

3. Enhanced participation
Data tables and spreadsheets can frequently be dull and difficult to understand, which causes disengagement. By attracting users’ attention and encouraging more engagement, data visualization helps make sure that insights aren’t missed.

4. Usability and accessibility of data
Not every member of a marketing team will be technically inclined or able to handle large amounts of sophisticated data. By making crucial company data more understandable and useful to all team members, visualizing marketing data contributes to the democratization of access to insights.

5. Improved comprehension

Marketing teams may make better educated, data-driven decisions by using data visualization to assist simplify and make difficult information easier to understand.

How crucial it is to properly prepare your data for visualization

It should go without saying that the accuracy and quality of the data that forms its foundation determines how effective any given data visualization is.

Even the most interesting and eye-catching data display won’t provide insightful information without reliable data. In the worst situation, it might even result in incorrect marketing optimizations.

Making sure that all of the data from your various marketing sources is standardized and combined before being entered into your data visualization tool is among the most crucial things to think about.

This makes it easier for you to compare performance across several channels in your visualizations with ease and accuracy. To do this, a top-tier ETL or data integration platform can assist you.

If you want the assurance that your company is making informed decisions based on your visualizations, it is equally essential to make sure your data is accurate and error-free.

To help assure accuracy and data quality, it’s worthwhile to take into consideration a data integration platform with integrated data transformation and data governance features.

Allowing your marketing staff to make timely and pertinent judgments instead of optimizing based on last week’s events is another crucial component. It’s important to take into account a data integration solution that can offer unified cross-channel data that is routinely fetched while getting your data ready for presentation.

Typical errors that companies make while using data visualization

When done well, data visualization may give marketing teams a simple way to comprehend and analyze data, which facilitates quicker and more efficient decision-making.

To assist you get the most out of the process, we’ve compiled an outline of the four golden laws of data visualization in addition to stressing the need of properly preparing your data.

But in the rest of this piece, we’ll examine the typical difficulties and dangers in data visualization to be aware of when building your data dashboards.

1. Excessively intricate graphics

It can be tempting to incorporate as much information as possible in data visualizations in order to maximize their value.

On the other hand, including an excessive number of metrics or visualizations on a single dashboard may have the opposite impact of what was intended, confounding the user and making it more challenging to interpret and evaluate the data.

Consider comparing performance, for instance, inside a single chart, across various dimensions such as impressions, interactions, clicks, conversions, and revenue. Finding significant insights would be difficult because you wouldn’t be able to concentrate on examining trends for important data.

Simplifying visualizations to just contain charts and images that are suited to the most important KPIs for the main business goals is a better strategy.

Decision-makers benefit from this method’s clarity, which also makes it simpler to find areas for optimization and pinpoint problems that need to be fixed.

2. Misusing different chart kinds

It’s typical to want to experiment with the many charts and graphics available to you when you first log into your data visualization platform.

But, it’s crucial to use caution and pick your visuals carefully because the incorrect chart style might cause confusion and incorrect data interpretation.

For instance, a line chart that shows the trends and patterns of traffic from each channel would be more useful for comparing the total amount of website visitors from various marketing sources over time than a pie chart.

Thus, choose your visuals carefully and select the appropriate kind of chart for your particular data.

3. Ignoring the intended audience

It is imperative to customize your data visualizations to the specific needs and skills of your audience while avoiding excessive complexity or oversimplification.

Simple line charts or column charts are the finest visual aids for conveying important performance trends, such as those that a CMO may need to view quickly. For this demographic, creating more intricate charts will probably simply serve to divert attention from the dashboard’s intended purpose.

However, if a data analyst wants to look at the link between a set of variables or analyze data in a hierarchical structure, they could find it helpful to use more intricate graphics like radar charts or tree maps.

4. Misrepresentation of data

It makes sense that you would want to give your marketing graphics the best possible impression if you are in charge of creating them and they would be viewed by the larger company.

However, one typical error made when presenting data is to manipulate graphs and charts, such as by utilizing a shortened axis to exaggerate small performance gains.

Inaccurate data representation in your visualizations can lead to mistrust and disengagement with your marketing data dashboards, in addition to painting an inaccurate picture of marketing effectiveness.

5. Complicated color usage

Effective color choice is essential to the ease of understanding and analysis of data visualizations.

However, misusing color is another typical error that frequently appears in data visualizations, slowing down analysis and impeding prompt and efficient decision-making.

Think about a pie chart that illustrates how sales are distributed throughout different marketing channels, for instance. It can be unduly difficult to grasp the relative performance of each channel if every segment of the chart is colored in roughly comparable tones of blue.

6. Using contradictory or erroneous data

Your ability to make decisions using data visualizations is strongly influenced by the caliber, consistency, and correctness of the data that powers them.

Making data visualizations from inadequate, erroneous, or inconsistent data sets can result in poor marketing optimization choices.

Users’ capacity to derive major insights from your images can be severely impacted by even seemingly insignificant problems with the data.

Consider the challenge of making meaningful comparisons, for instance, between two channels where one displays CPA determined solely from post-click conversions and the other includes both post-view and post-click conversions. This graphic might be more confusing than helpful if it didn’t provide a consistent metric for people to compare.

Before uploading your data to your data visualization platform, it’s critical that you take the necessary precautions to make sure it is correct, consistent, and prepared for analysis. To provide total piece of mind, use solutions like data integration platforms.

Advertising: The engine behind powerful data visualization

Searching for a way to power your data visualizations using data integration?

One of the best platforms for integrating data that is especially designed for marketers is Adverity.

Adverity can connect to all of your marketing sources, aggregate and standardize data, and prepare it for your data visualizations using a library of over 600 data connectors.

Adverity can guarantee the accuracy and superior quality of the data driving your visualizations with its integrated data governance tools. Marketing teams may use the most recent data to inform optimization decisions thanks to a market-leading data retrieve rate of up to once every 15 minutes.

Contact Adverity to schedule a demo and start using your marketing data to its fullest.

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