What is Data Visualization: Definition, Examples, Principles, Tools

65% of people are visual learners, making data visualization an effective way to communicate information.

When Excel spreadsheets aren’t enough to connect the dots between your data and there’s no possibility to involve data or digital analyst to get the report quickly, data visualization software tools and tools is what you need to become data-savvy.

In this article, which was last updated in January 2024, we’ll show you what data visualization techniques are available, how to visualize data correctly, which tools can be used for engaging and interactive visualizations without any help from developers or data professionals, and how to choose a tool that suits your specific needs.

The definition of data visualization is the visual representation of your data. With the help of charts, maps, and other graphical elements these tools provide a simple and comprehensible way to clearly see and easily discover insights and patterns in your data.

Data visualization is the graphical representation of data using visual elements such as charts, graphs, and maps.

It is a way to communicate complex information in a visual and intuitive manner, making it easier for people to understand and analyze the data. By transforming raw data into visual representations, data visualization allows patterns, trends, and insights to be easily identified and interpreted.

Data visualization is also a powerful storytelling tool. Visual storytelling helps to uncover hidden patterns, relationships, and correlations that may not be apparent, or not visible in raw data. Through visualizations, data can be presented in a way that is engaging, impactful, and memorable, enabling effective communication and data-driven decision-making.

Data visualization is not limited to a specific field or industry. It's not only about marketing data and is used in various domains such as business, finance, healthcare, education, or journalism. In business, data visualization is used to analyze sales trends, key performance indicators, and present business metrics. In healthcare, it is used to visualize patient data, monitor disease outbreaks, and analyze medical research. In journalism, it is used to create better stories and increase reach and consumption.

If you want your Facebook post to be read by as many people as possible, what will you do? You’ll add an interesting visual. This trick works perfectly with reports too. Data-driven visuals attract more attention, are easier to understand, and assist in getting your message across to the audience quickly.

With the help of descriptive graphics and dashboards, even difficult information can be clear and comprehensible.

Why is that?

Most people are visual learners. So if you want the majority of your partners, colleagues, and clients to be able to interact with your data, you should turn boring charts into beautiful graphics. Here are some noteworthy numbers, based on research, that confirm the importance of visualization:

• People get 90% of information about their environment from the eyes.
• 50% of brain neurons take part in visual data processing.
• Pictures increase the wish to read a text up to 80%.
• People remember 10% of what they hear, 20% of what they read, and 80% of what they see.
• If a package insert doesn’t contain any data illustrations, people will remember 70% of the information. With pictures added, they’ll remember up to 95%.

Relevant visualization brings lots of advantages for your business:

• Fast decision-making. Summing up data is easy and fast with graphics, which let you quickly see that a column or touchpoint is higher than others without looking through several pages of statistics in Google Sheets or Excel... or even a database or a CRM or CMS system.
• More stakeholders are involved. Most people are better at perceiving and remembering information presented visually and delivered on time in a visual-appealing format. 
• Higher level of involvement. Beautiful and bright graphics with clear messages attract readers’ attention.
• Better understanding. Perfect reports are transparent not only for technical specialists, analysts, and data scientists but also for CMOs, CEOs and other C-levels or managers, and help each and every worker make decisions in their area of responsibility.

The first thing to do before creating any chat is to check all information for accuracy and consistency.

For example, if the scaling factor is 800%, whereas the average is 120–130%, you should check where this number is coming from. Maybe it’s some kind of an outlier that you need to delete from the graph so it doesn’t skew the overall picture: 800% downplays the difference between 120% and 130%. This kind of outlying data in a report can lead to incorrect decisions made.

To increase the chances of success in marketing, the right message should be delivered to the right person at the right time. 

The same three rules are applied for data visualization:

1. Choose the right chart to visualize the answer to specific question based on your goal.​
2. Confirm that the message to deliver the result of your report suits your audience (the stakeholder).
3. Use an appropriate design for the chart to deliver that message.

If your message is timely but the chat or graphic isn’t dynamic, or it provides incorrect insights. or the design is not attractive, then you won’t achieve the results you were dreaming of.

If you choose the wrong chart or graph, your readers will be confused or interpret or read the results incorrectly. That’s why before creating a report with charts, it’s important to decide what data you want to visualize and for what purpose, for example: 

• To compare different data points
• ​To show data distribution: for instance, which data points are frequent and which are not
• To show the structure of something with the help of data
• To follow the connections, references or correlation between data points

Let’s have a look at the most popular types of charts and the goals they can help you achieve.

A line chart is a type of data visualization that uses a series of data points connected by straight lines. It is commonly used to show the relationship between two variables over a continuous period of time. Foe example, the x-axis represents the time or the independent variable, while the y-axis represents the value or the dependent variable.

By plotting the data points and connecting them with lines, the line chart provides a visual representation of how the values change over time.

One of the main advantages of line charts is their ability to display trends and patterns in data. They make it easy to identify the overall direction of change, whether it is increasing, decreasing, or remaining stable.

Line charts also allow for the comparison of multiple data series on the same chart, making it simple to analyze the correlation between different variables.

Additionally, line charts are visually appealing and easy to understand, making them accessible to a wide range of audiences.

However, line charts also have some limitations. They are most effective when used with continuous data, such as time series data, and may not be suitable for categorical or discrete data.

Line charts can become cluttered and confusing if there are too many data points or series plotted on the chart. They may also not be the best choice for displaying data with irregular or inconsistent intervals. It is important to consider these factors when deciding whether to use a line chart for data visualization.

The best use cases for line charts include analyzing sales or revenue data over time, tracking website traffic or user engagement metrics, visualizing stock market trends, or monitoring changes in weather patterns.

Line charts are particularly useful when there is a need to understand the overall trend or pattern in the data and identify any significant changes or anomalies. They are also effective for presenting data to a non-technical stekholders, as they provide a clear and really easy and intuitive representation of the data.

Type of diagram that represents data using rectangular bars is called bar chart. Each bar corresponds to a specific metric or variable, while its length or height represents the value associated with that metric.

Bar charts are typically used to compare different metrics or track changes over time providing simplicity and versatility.

Horizontal bar charts are often used when you need to compare lots of data sets or to visually emphasize the distinct advantage of one of the data sets.

Vertical bar charts display how data points change over time — for example, how the annual company profit has changed over the past few years.

Bar charts are ease of read and consume, no background in data analysis is required. 

The clear and straightforward presentation of data in bar charts allows for quick insights and understanding.

Additionally, bar charts can accommodate large datasets and display multiple variables simultaneously (and stay usable).

Continuous data, such as temperature measurements over time, may not be as suitable for bar charts.

Bar charts may also not be the best choice for displaying complex relationships or correlations between variables, as they primarily focus on comparing values within categories.

Some common use cases include sales analysis, market research, financial reporting, and survey results. For example, a bar chart can be used to compare the market share of different companies in a specific industry, or to visualize the responses to a survey question with multiple answer options.

A bar chart can also represent the sales figures of different products in a given month, with each bar representing a product or a category, and its height indicating the sales quantity. This visual representation allows for easy comparison and identification of trends or patterns in the data.

The  of bar charts make them a valuable tool for data visualization in various domains.

A histogram is often mistaken for a bar chart due to their visual similarities, but the goals of these charts are different.

 A histogram shows the distribution of a dataset across a continuous interval or a definite time period. It is a graphical representation of the frequency of data values in different intervals or bins. The x-axis of a histogram represents the range of values in the dataset, divided into equal intervals, while the y-axis represents the frequency or count of data values falling within each interval. The height of each bar in the histogram corresponds to the frequency of data values in that interval. This chart provides a visual summary of the underlying data distribution.

Unlike a histogram, a bar chart doesn’t show any continuous interval; each column displays a category of its own. It’s easier to demonstrate the number of purchases in different years with the help of a bar chart. 

If you want to know the number of order beween $10 and 100, $101 and 200, $201 and 300, etc. of purchases, it’s better to choose a histogram. The histogram will show you the frequency of orders falling within each price range, allowing us to identify patterns such as a normal distribution, skewed distribution, or outliers.

Histogram allows you to quickly identify the central tendency, spread, and shape of the dataset. Histograms are particularly useful when dealing with large datasets or continuous data, as they provide a visual summary without overwhelming the viewer with individual data points.

What are the limitation of the histogram?

First, the choice of bin size or interval width can impact the interpretation of the data. A smaller bin size can provide more detailed information but may also result in a cluttered or noisy chart. At the same time, a larger bin size can oversimplify the data distribution.

Second, histograms may not be suitable for datasets with categorical or ordinal variables, as they require numerical data to create meaningful intervals.

A pie chart is a type of data visualization that displays shares of each value in a data set.

It is divided into slices, where each slice represents a proportion or percentage of the whole. The size of each slice is determined by the value it represents in relation to the total value of the data set. Pie charts are commonly used to show the distribution or composition of a categorical variable.

The pie chart visually displays the relative proportions of different categories within a data set. It allows viewers to quickly grasp the overall distribution of the data and easily compare the sizes of different categories.

The angles of the slices in the pie chart represent the proportions of the categories, making it easy to understand the relationship between the parts and the whole. For instance, what percentage of general sales is attributed to each product category?

Pie charts are particularly useful when dealing with data that has a small number of categories or when the emphasis is on comparing the parts to the whole. They can also be useful for highlighting a specific category or identifying outliers. However, pie charts don't represent the trends over time.

But really the biggest pie chart limitation is that they can become difficult to interpret when there are too many categories or when the differences between the categories are small. It can be challenging to accurately compare the sizes of the slices, especially if they are similar in magnitude.

To overcome the challenge of many categories, use pie chart calculator for numerous or similarly sized categories. This tool is highly beneficial to interpret data visualization easily with a detailed percentage of each slice in a pie chart. It gives quick insights into the distribution of data for each category.

Pie charts are commonly used in business and marketing to represent market share, customer demographics, or product sales by category. Pie charts are also used in survey data to display the distribution of responses to multiple-choice questions. Overall, pie charts are most effective when the data is simple, the categories are distinct, and the emphasis is on comparing the parts to the whole.

A scatter plot chart displays the relationship between two numerical variables. It uses a Cartesian coordinate system, where each data point is represented by a dot or marker on the chart.

The x-axis represents one variable, while the y-axis represents the other variable. By plotting the data points on the chart, you can visually analyze the correlation or pattern between the variables.

The scatter plot chart allows you to identify trends, clusters, or outliers in the data.

Additionally, scatter plots can be used to detect any patterns or irregularities in the data distribution. 

The main limitation is that it can only represent two variables at a time. If there are more than two variables to analyze, additional charts are required. Also, scatter plots may not be suitable for large datasets, as the overlapping data points can make it difficult to make decisions based on the chart accurately.

For example, with the help of a scatter plot, you can find out how the conversion rate changes depending on the size of the product discount.

This is an interesting chart that allows you to compare two parameters by means of a third.

It is a variation of a scatter plot, where the size of the bubbles is used to convey additional information. The bubble chart is particularly useful when visualizing three variables, as it allows for the representation of two continuous variables on the x and y axes, while the size of the bubbles represents the third variable. This makes it easy to identify patterns and relationships between the variables in a single chart.

Bubble chat allows you to display large amounts of data in a visually appealing and intuitive way. By using different colors or shades, you can also incorporate a fourth variable into the chart, further enhancing the information conveyed. The size of the bubbles provides a quick visual cue, allowing for easy comparisons between data points.

Additionally, the bubble chart can be interactive, allowing users to hover over or click on the bubbles to reveal more detailed information.

Basically, the main drawback of the bubble charts is that the size of the bubbles can sometimes be misleading, as it may not accurately represent the magnitude of the data point. This can be mitigated by scaling the size of the bubbles appropriately or by providing a clear legend or scale.

Also, it is important to strike a balance between the number of data points and the readability of the chart.

The best use cases for bubble charts are situations where you want to visualize relationships between three variables.

For example, you can use a bubble chart to show the relationship between the price, size, and number of orders of different products. It can also be used to compare data across different categories or groups, such as comparing the revenue, market share, and growth rate of different companies in an industry.

Bubble charts are particularly effective when the size of the bubbles is meaningful and provides valuable insights into the data.

The geo chart is a simple one. It’s used when you need to demonstrate a certain data distribution across regions, countries, and continents.

By visualizing data on a map, a geo chart provides a clear and intuitive way to understand spatial patterns and user behavior. For example, a geo chart can show shopping frequency across countries, GDP per capita by country, or election results by region. It allows viewers to quickly grasp the variations and disparities between different locations. Basically, geo chart works best if metric dimension is geographical.

By mapping data onto a familiar geographic locations, it becomes easier for viewers to interpret and remember the information.

Since a geo chart relies on colors or patterns to represent data, it is important to choose appropriate color schemes and legends to avoid confusion or bias. Furthermore, a geo chart may not be suitable for displaying complex or detailed data, as the level of granularity is often limited to the size and boundaries of the regions on the map. It is important to carefully select the level of detail and aggregation that best suits the purpose of the report.

For example, when analyzing sales data, a geo chart can show the distribution of sales across different regions, helping businesses identify potential markets or areas of improvement.

Overall, geo charts are particularly effective when the spatial dimension of the data is crucial for decision-making or storytelling.

The second important thing that you have to take into account while working with visualization is choosing the right message for the audience. The information you talk about, the story you tell in the report should be clear and informative for your readers.

Here’s a chart that was awarded the prestigious Data Journalism Award. 

For people who aren’t familiar with the background to the story, this chart looks like a picture made by a three-year-old. However, when you find out a little bit more about it, you can see the huge amount of work done by its authors.

Charles Seife and Peter Aldhous, Buzzfeed News editors, used the R language to visualize flight data obtained by FBI and DHS agents as part of air surveillance. Specifically, this chart shows flights above the house and mosque of those responsible for the mass shooting in December 2015 in San Bernardino, California.

While choosing the parameters you want to visualize on one chart, you have to confirm that they can be combined. Some combinations just aren’t logical, though at first sight the information correlates perfectly. Here’s an example of such a chart with a faulty correlation. It shows that the number of people who drowned by falling into a pool correlates with the number of Nicolas Cage films.

The next things you should take into account when creating a chart are the scale and scope. People are used to the fact that measurements on axes start from the bottom and from the left. If you change the direction of measurement, it will confuse an inattentive audience. Although we should mention that reversing the measurement is possible when used as a tactical maneuver, as in this example:

At first sight, it may seem that the number of murders committed using firearms has been decreasing over the years. In fact, it’s the opposite, as the scale starts from the top. Perhaps the author of the chart did this on purpose to decrease the negative response to the results shown.

A suitable scale also makes your chart clearer. If a report shows data points that are too close and you can’t see any movement, try to change the scale. Start the measurements not from zero or divide the scale into smaller parts and the picture will clear up.

Before giving a report to the stakeholder, make sure that the chart loads fast. Slow loading kills all your efforts.

For example, if you’re visualizing data in Google Sheets, most likely your data is stored on the same page or on the next page and doesn’t come from a third-party source.

But when you create a report in Looker Studio (ex. Data Studio) or Power BI, data will be imported from somewhere else. In this case, you have to pay close attention to the source accessibility and the data flow rate. Otherwise, you’ll see a sad looking picture when there’s a chart template but data hasn’t been loaded.

Remember, the golden rule when you're crafting your chart design is to keep it simple. 

When you're tasked with putting together a standard report, don’t fret about making it look fancy. You don't need to dress it up.

Avoid any extra elements that only clutter the chart: too many colors and structures, 3D volume, shadows, gradients, etc.

The simpler a chart is - the easier it is for the readers to understand the information you want to share.

Don’t make your visualizations too small, and don’t put all charts on the same dashboard page. It’s considered bad style to use more than three types of charts on one slide or the same dashboard page. If you really need so many chart types, put them on different pages, or make a clear separation, so it’s easy to understand them.

Don’t be afraid to experiment. If you have a task that's not typical, perhaps your solution should also be non-standard. In the infographic below, we can see the wing movement patterns of different animals. The dynamic visualization is totally relevant.

Let’s have a look at some data visualization tools examples and discuss how to choose the right one for your goals.

Visualizing data is an undeniable benefit in any niche, and it doesn’t matter if you’re building a career in marketing, design, retail or anything else.

Making information easy to consume and quickly make smart decisions is one of the keys to finding growth zones and developing your business.

When your colleagues would see the visual charts outlining the current state of the main metrics for you, it’s easier to make sure that all of the team members are  on the same page and everyone understands the strong and weak points of the current strategy.

While visualizing reports itself cannot fix the issues, it gives you the wheel to drive the car, to make the necessary changes and improve the KPIs.

Nowadays, there are lots of data visualization and reporting tools on the market. Some of them are paid, others are available for free. Some of them work fully on the web, others can be installed on a desktop but work online, and others are offline only. 

We’ve crafted a list of 10 most popular reporting and data visualization software:

1. Google Spreadsheets

2. Looker Studio (ex. Google Data Studio)

3. Tableau

4. Power BI

5. Excel

6. QlikView

7. R Studio

8. Visual.ly

9. Tangle

10 iCharts

First six tools and services are created by companies specializing in visualization. 

Numbers seven through ten are quite interesting tools, mostly free and online. They offer non-standard types of data visualization and may offer new ways of approaching your business information.

What to look for when choosing a reporting tool:

• Start from the goals and tasks you want to accomplish. For example, a major trend on the market nowadays is dynamic reports. If a tool cannot work with dynamic reports, that’s a strike against it.
• Consider the amount of money you’re ready to pay. If your team is big enough and every employee has to work with the visualization tool, then the cost per user may be a stop sign.
• Decide who will use the tool and how:
• Is there a possibility for group editing? 
• How simple is it to start working with the tool? 
• Is the interface user-friendly? 
• Is there a possibility to create a report without any knowledge of programming? 

For example, R Studio is a great service, especially for searching for trends and building attribution and correlation models. But if you are not familiar with coding, you won't be able to connect any specific libraries, and it would be difficult for you to start working with R Studio.

We'll dive deeper into a few services and guide you through their pros & cons, as well as the main features and advantages.

But before we start, let us explain how dynamic data visualization and dynamic reports differs.

Dynamic reports refer to the possibility to import data from different sources in real time. 

For example, Looker Studio (formerly Google Data Studio) doesn’t have dynamic reports in place. Let’s say we’ve connected a Looker Studio request from Google BigQuery and then changed something in this request. To record these changes in the report, we need to at least refresh the page. 

However, if we add or delete some fields in Google BigQuery (not just change the logic of the calculation but change the table structure), then Looker Studio would show an error. You’ll have to rebuild the dashboard to get the visualizations in place.

Dynamic visualization concept refers to the possibility to look at summary statistics over different dates during one session. 

For example, in Google Analytics 4 you can change the time period and get statistics for the date range you need.

OWOX BI is a comprehensive analytics platform that covers everything from data collection and streaming to attribution modeling and reporting. With OWOX BI, companies get a complete view of their marketing activities across various channels, empowering advertising specialists to optimize their ad spending and achieve better ROI.

OWOX BI Pipelines facilitates seamless data collection from various advertising platforms, CRMs, and website builders, enabling organizations to consolidate all their data in one place in order to have a data source of truth and gain better insights.

OWOX BI Streaming is a cookieless real-time user behavior tracking system, ensuring privacy compliance with regulation and extending the lifespan of cookies. Marketers can accurately track the entire conversion journey, find the true sources of conversions, and gain a deeper understanding of customer behavior.

OWOX BI Transformation saves time on data preparation (avg. of 70 hours per month). With pre-built low- or no-code transformation templates (based on 100’s delivered projects across multiple industries), businesses can quickly produce trusted datasets for reporting, modeling, and operational workflows:

• Sessionization: Group on-site events into sessions to find conversion sources
• Cost data blending: Merge ad cost data across channels to compare campaign KPIs in a single report
• Attribute ad costs to sessions to measure cohorts and pages' ROI;
• Create cross-device user profiles across different devices
• Identify new and returning user types for accurate analysis
• Apply a set of attribution models: Choose from standard attribution models like First-Click, LNDC, Linear, U-shape, and Time Decay, or create a custom Machine Learning Funnel-based attribution model
• Use modeled conversion for cookieless measurements and conversion predictions
• Prepare data for marketing reports in minutes

Lastly, OWOX BI integrates with visualization tools like Looker Studio, Tableau, or Power BI, enhancing data-driven decision-making by building customizable reports & keeping the data always up-to-date.

• No technical background, coding experience or knowledge of SQL is required.
• Simple and user-friendly interface: you can collect all of the data and generate reports using our dashboard templates and customize what matters for you the most.
• If you want to working with your data in Google Sheets, you can easily export an aggregated dataset from BigQuery to Google Sheets with our reports add-on. 
• You can copy SQL queries generated by OWOX BI. 
• You can then modify those queries or use them, for example, to automate a data-based report in Google Sheets or BigQuery.
• You retain complete control over access to that data.
• You can merge digital marketing data with CRM/CMS data.
• Full transperancy.

Note: For enterprise customers, OWOX BI expert team will set up a data model tailored to your business. You’ll be able to evaluate the impact of all marketing efforts — both online and offline.

User actions on your site:

• You can set up the collection of raw data from the site in Google BigQuery using OWOX BI Streaming.
• Or you can use the native standard export from Google Analytics 360 or Google Analytics 4 to Google BigQuery.

Transactions Data:

• Google Analytics → Google BigQuery
• Google Sheets → Google BigQuery
• CRM → Google BigQuery

Advertising campaign costs:

• Advertising services → Google Analytics 4
• Advertising services → Google BigQuery
• Other marketing tools → Google BigQuery

Looker Studio, also known as data studio allows you to connect data sources, easily build charts, reports and add elements to visualize and share reports with colleagues in a way that’s similar to other Google products.

Advantages:

Looker Studio is a free tool with 21 native connectors provided by Google:

• Connect to your Looker semantic models. 
• Connect to Google Analytics 4 reporting views. 
• Connect to Google Ads performance report data.
• Connect Google Sheets.
• Connect to BigQuery tables and custom queries.
• Connect to AppSheet app data.
• File Upload - Use CSV (comma-separated values) files. 
• Connect to Amazon Redshift.
• Connect to Campaign Manager 360 data.
• Connect to MySQL databases.
• Connect to Display & Video 360 report data.
• Connect to Microsoft SQL Server databases.
• Connect to PostgreSQL databases.
• Connect to Search Console data.
• Connect to YouTube Analytics data. 

and more...

They’re checked, approbated, work well, and perfectly suit to the most common reporting tasks. 

There are also connectors provided by Google partners, though you have to understand that connectors can be presented by developers with different skill levels and there’s no guarantee they’ll perform correctly.

By the way, if you want to see any Facebook or Yahoo Gemini statistics in reports built in Looker Studio, you can import ad cost data into Google BigQuery with OWOX BI. While you may lose some of the important data with other data connectors, with our Facebook Ads to Google BigQuery pipeline you receive complete data ready for analysis and reporting from your Facebook account.

You can also merge your Facebook Ads data with the advertising cost data from Google Ads, Twitter Ads, and LinkedIn ads and get a helicopter view of your marketing performance and optimize your cross-channel budget easily.

We also have a ready-to-use dashboard templates of our own that we want to share.

We've prepared a comprehensive Looker Studio dashboard template gallery with ready-to-use templates so that you can quickly create a guide to your business results, KPIs and performance.

The first is a All-in-one Performance Dashboard. With this dashboard, you can find all of the basic metrics and metrics to stay on top of your advertising and marketing performance and achieve the desired ROI.

Another dashboard template we'd like to share is the Digital Marketing Paid Channels KPI dashboard, which is segmented by data sources (shown in detail). In other words, it shows filtered data on Facebook marketing campaigns, etc.

Those are the dashboard templates. Make a copy, change the data sources to your own, and use them to build beautiful reports based on your data. 

One of the recent Looker Studio updates adds the possibility to filter information by view. For example, you can compare data points over the current period and the previous year.

One more interesting update allows you to change the type of an already created chart, graph or element. Earlier, when changing a chart, you had to delete it and create a new one. 

Useful links:

• Webinar: Mastering Marketing KPIs
• Google Looker Studio dashboard template gallery
  

This is one of the two most popular data reporting tools (together with Microsoft Excel) that’s used by any marketing specialist at least once. The Google Sheets interface is quite simple and easy-to-use, especially for those who just starting analytics out.

Advantages:

• Free
• Flexible — supports dynamic parameters, vlookups, pivot tables, formulas, app scripts etc.
• Easy to integrate with data sources (but not so easy to automate updates)
• Convenient to share reports via links

The charts and reports types in Google Sheets are the same as that is in Looker Studio.

Conditional formatting in Google Sheets allows users to apply formatting rules to cells based on specific conditions.

These conditions can be based on the cell's value, text, or even a formula. By using conditional formatting, users can visually highlight important data, identify trends, and make their spreadsheets more visually appealing and easier to understand.

For example, let's say you have a sales report in Google Sheets and you want to highlight all the cells that have sales numbers above a certain threshold. With conditional formatting, you can set a rule that applies a different background color to those cells automatically. This makes it easier to quickly identify the high-performing sales figures without manually scanning through the entire spreadsheet.

Perhaps the main advantage of Google Sheets as your go-to reporting tool is pivot tables.

Pivots allow users to summarize and analyze significant amounts of data. They are used to transform raw flat table data or data sets into insights by organizing and summarizing it in an easy and structured relatively small table.

With Pivot tables you can quickly explore data from different perspectives, change columns and rows, sort values, identify patterns, and uncover trends or anomalies. They are particularly useful for data aggregation tasks.

For example, let's say you have a spreadsheet with sales data for a company. The data includes columns for product names, sales dates, sales quantities, and sales amounts. By creating a pivot table, you can easily summarize this data to answer questions like: 'What are the total sales amounts for each product?' or 'What are the average sales quantities by month?'

Pivot tables allow you to group and aggregate data based on different criteria, such as product, sales date, or any other relevant attribute.

VLOOKUP is a function in Google Sheets that stands for vertical lookup. It is used to search for a specific value in the leftmost column of a range of cells, and then return a corresponding value from a different column in the same row. Vlookup is commonly used to find and retrieve data from large datasets or tables.

Imagine having a list of products and their corresponding prices. You can use VLOOKUP to search for a specific product name in the leftmost column, and then retrieve the price of that product from a different column in the same row.

This can be useful for tasks such as pricing analysis.

The syntax of the VLOOKUP function in Google Sheets is as follows: =VLOOKUP(search_key, range, index, is_sorted).

The search_key is the value you want to search for, the range is the range of cells where the search will be performed, the index is the column number from which the corresponding value should be returned, and the is_sorted is an optional parameter that specifies whether the range is sorted in ascending order or not.

If you want to get data from BigQuery to visualized in Google Sheets, there is a Google sheets extension that allows you to query data directly from Google BigQuery and build reports based on the imported data.

Basically, you can request any data stored in your Google BigQuery project directly from the Sheets interface. 

Last but not least, we wanted to share with you one of our favorite reports for Google Sheets — the cohort analysis report.

Cohort analysis is a powerful analytical technique used to understand the behavior and common details of a specific group of individuals over time. 

It involves dividing a larger audience into smaller groups, or cohorts, based on a common characteristic or event. These cohorts are then analyzed to identify patterns and trends that can help businesses make informed decisions and improve their strategies.

The most common case of cohort analysis in marketing is to track the behavior of customers who made their first purchase in a particular month. By analyzing this cohort, businesses can determine the retention rate, average purchase value, and lifetime value of these customers. This information can be valuable in identifying the most effective marketing channels, optimizing customer acquisition strategies, and improving customer loyalty.

Additionally, you can read our detailed guide to cohort analysis in Google Analytics 4 and Google Sheets, where we provide very detailed instructions. We’ve also hosted a webinar on cohort analysis.

Finally, we want to share some useful links and books on data visualization:

• Edward Tufte, The Visual Display of Quantitative Information
• Stephen Few, Big Data, Big Dupe
• «The Joy of Stats» (documentary film)

Visualization services can help you make your reports visually appealing and comprehensive, you can highlight valuable insights in your data easily. 

If you want to keep up with the pace of modern business, adding visual storytelling and data exploration to your reports will allow you to accelerate the process of decision-making.

If you still don’t know which of all data visualization tools would fit your business needs, book a free demo to discuss your specific situation with our data experts and discover the ideal solution designed for you.