When Excel spreadsheets aren’t enough to connect the dots and there’s no possibility to involve analysts in building reports, data visualization services and tools come to the rescue.
Published in · 16 min read · Jul 10, 2020
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 data 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 illustrations, people will remember 70% of the information. With pictures added, they’ll remember up to 95%.
Relevant data 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.
- More people involved. Most people are better at perceiving and remembering information presented visually.
- Higher degree of involvement. Beautiful and bright graphics with clear messages attract readers’ attention.
- Better understanding of data. Perfect reports are transparent not only for technical specialists, analysts, and data scientists but also for CMOs and CEOs, and help each and every worker make decisions in their area of responsibility.
The first thing to do before creating any graphic is to check all data for accuracy and consistency. For example, if the scaling factor is 800%, whereas the average is 120–130%, you should check where this number comes from. Maybe it’s some kind of 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 an incorrect decision. In real life, we’re accustomed to the fact that the right message should be delivered to the right person at the right time. There are three similar principles for data visualization:
- Choose the right graphic depending on your goal.
- Confirm that the message of your graphic suits the audience.
- Use an appropriate design for the graphic.
If your message is timely but the graphic isn’t dynamic or there’s an incorrect insight or a difficult design, then you won’t get the result you hoped for.
If you choose the wrong graph, your readers will be confused or interpret the data incorrectly. That’s why before creating a graph, it’s important to decide what data you want to visualize and for what purpose:
- 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 between data points
Let’s have a look at the most popular types of charts and the goals they can help you achieve.
1. Line chart
A line chart shows how one or more variables change across data points. This type of chart is useful for comparing changes within data sets over time — for instance, traffic statistics for three landing pages by month over a one-year period.
2. Bar chart
The bar chart is another diagram that’s perfectly suited for comparing data sets. 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 illustrate how data points change over time — for example, how the annual company profit has changed over the past few years.
3. Histogram
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 data set across a continuous interval or a definite time period. On the vertical axis of this chart, you can see frequency, whereas on the horizontal you can see time intervals.
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 order values ($10–100, $101–200, $201–300, etc.) of purchases, it’s better to choose a histogram.
4. Pie chart
The pie chart displays shares of each value in a data set. It’s used to show the components of any data set. For instance, what percentage of general sales is attributed to each product category?
5. Scatter plot
The scatter plot shows the connection between data points. 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.
6. Bubble chart
This is an interesting chart that allows you to compare two parameters by means of a third. Let’s take the conversion rate and discount size from the previous example, add to them revenue (indicated by circle size), and we’ll get something like the following chart.
Looking at this chart, it’s easy to notice that products with a 30% discount have the highest conversion rate, while products with no discount or a 5% discount bring in the most revenue.
7. Geo chart
The geo chart is a simple one. It’s used when you need to demonstrate a certain distribution across regions, countries, and continents.
We’ve mentioned some of the most popular charts but not all of them. You can find other types of graphs in the Data Visualization Catalogue. Also, we recommend this handy infographic that helps you choose the right type of chart for your goal(s).
The second important thing that you have to take into account while working with data visualization is choosing the right message for the audience. The data you talk about in a report should be familiar and clear 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 data combinations just aren’t logical, though at first sight the data 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 data 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 end users, 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 Data Studio, data will be imported from somewhere else. In this case, you have to pay attention to 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.
Your graph design should always follow the principle of simplicity. If you have to prepare a standard report, there’s no need to dress it up. Avoid any extra elements that only clutter the chart: different colors and structures, 3D volume, shadows, gradients, etc.
The simpler a chart is, the easier it is for your 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 on the same dashboard page. If you really need so many chart types, put them on different pages so it’s easy to understand them.
Don’t be afraid to experiment. If you have a non-standard task, 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 data visualization tools and discuss how to choose the right one for your goals.
Nowadays, there are lots of data visualization tools on the market. Some of them are paid, others are 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 made a list of 10 popular tools for data visualization:
- Excel/Google Spreadsheets
- Data Studio
- Tableau
- Power BI
- QlikView
- R Studio
- Visual.ly
- Tangle
- iCharts
- Smart Data
The first five tools and services are produced by companies specializing in data visualization. Numbers six through ten are quite interesting tools, mostly free and online. They offer non-standard types of visualization and may offer new ways of approaching your data.
What to look for when choosing a reporting tool:
- Start from the 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 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 don’t know any programming languages, can’t connect any specific libraries, and aren’t a technical specialist, it will be difficult for you to start working with R Studio.
We’ve chosen five services and prepared a table comparing their advantages, disadvantages, and main characteristics. Before we start, let’s explain how dynamic data visualization and dynamic reports differ.
Dynamic reports refer to the possibility to import data from different sources in real time. Google Data Studio doesn’t have dynamic reports. Let’s say we’ve connected a Data Studio request from Google BigQuery and then changed something in this request. To record these changes in the report, we at least need to refresh the Data Studio page. However, if in Google BigQuery we add or delete some field (not just change the logic of the calculation but change the table structure), then Data Studio will close the report with an error. You’ll have to redo it.
Dynamic data visualization refers to the possibility to look at summary statistics over different dates during one session. For example, in Google Analytics you can change the time interval and get statistics for the dates you need.
Key characteristics of the top five visualization tools
We want to discuss in detail three tools that are actively used alongside OWOX BI: Google Data Studio, Google Sheets, and OWOX BI Smart Data.
Google Data Studio allows you to connect sources, visualize data, and easily share reports with colleagues in a way that’s similar to other Google products.
Advantages:
- Free
- More than 150 connectors that are easy to integrate
- Can use data from several sources via one dashboard
- Convenient to share reports
Google Data Studio is a free tool with 17 native connectors provided by Google. They’re checked, work well, and are enough for most tasks. There are also connectors provided by 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 Data Studio reports, you can import data into Google BigQuery with the help of OWOX BI. While some analytics can be left unaccounted for with other connectors, with BigQuery you receive complete data analysis from your Facebook account.
There’s a convenient Google Data Studio gallery with ready-to-use templates.
OWOX BI also has ready-to-use dashboard templates.
The first is a Marketing Attribution Dashboar d. In this dashboard, you can find all the basic parameters and metrics used by marketing specialists and analysts.
The second dashboard is the Digital Marketing Paid Channels KPI dashboard, which is segmented by sources of data (shown in detail). In other words, it shows filtered data on Facebook marketing campaigns, etc.
These are demo dashboards. You can copy them, change the data sources for your own, and use them in your work.
One of the recent Data Studio updates adds the possibility to filter data by view. For example, you can compare data points over the current period and the previous year.
One more interesting update to Data Studio allows you to change the type of an already created graph in the interface itself. Before, when changing a graph, you had to delete it and create a new one. Now it’s possible to change the graph style directly in the interface.
This is the most popular reporting tool that’s used by any marketing specialist at least once. The Google Sheets interface is quite simple and clear, especially for those who started working with analytics in Excel.
Advantages:
- Free Flexible — supports dynamic parameters, pivot tables, etc
- Easy to integrate with data sources
- Convenient to share reports via links
Useful links:
- Examples of simple SQL requests to create Google Sheets reports
- How to use dynamic parameters in reports
The chart and report set in Google Sheets is the same as that in Google Data Studio.
Also, there’s a possibility to manage the colors and choose the cell formatting:
Perhaps the main advantage of Google Sheets is pivot tables. Recently there was a Google Data Studio update that allows for calculating more than three fields and ten columns. It made the lives of analysts quite a bit easier, though the possibilities in Data Studio are still limited, and working with pivot tables is still more comfortable in Google Sheets.
Google Sheets has a free add-on that allows you to upload data directly from Google Analytics and build reports based on the imported data. Also, you can request Google Analytics data directly from Sheets. In this GIF, you can see how to import data and which parameters and metrics should be set up.
We want to share our favorite report in Google Sheets — the cohort analysis report.
This report template can be found here. You can see the instructions and the formulas used. The colored fields have to be filled in, and other colored fields are updated with the help of formulas. There are tons of calculated metrics, but this report is difficult and labor-intensive. We hope this template will be useful for you. Additionally, you can read a detailed guide to cohort analysis in Google Analytics and Google Sheets, where we provide very detailed instructions.
With OWOX BI Smart Data, you don’t need to know SQL syntax. It’s enough to ask a question in plain English using natural language. The service processes the request, translates it into technical language, and returns a beautiful graphic and table with the answer to your question.
Advantages:
- No need for special technical training
- Fast answers to questions
- Friendly interface
- Available in Russian and English
There is a detailed reference guide in which you can read about each type of report you can create in Smart Data.
User actions on your website:
- You can adjust Google Analytics → Google BigQuery streaming with the help of OWOX BI.
- Or use the standard Google Analytics 360 → Google BigQuery export.
Transactions:
Ad campaign costs:
After gathering all this data, you can start asking questions. We add all reports that are needed by our clients in OWOX BI Smart Data. Then we group them in blocks by topic to make it easier to search for reports. We have blocks with ROPO reports, attribution reports, CPA partner reports, CRM data reports, and many more.
Questions to ask based on your data:
- How did [metric] change over [time period] by [dimension]?
- What was the [metric] by [dimension]?
- How was the [metric] distributed to [dimension]?
- How many [metric] were on the website?
Attribution questions:
- What is the real value of ad channels, campaigns, and keywords?
- What were the ROAS, ROI, and CRR according to the funnel-based model?
- How was the conversion value (e.g. registration) distributed across channels and campaigns?
- What sources have the greatest and the least value according to the last non-direct click model?
- What campaigns and keywords performed best in attracting new users?
- What channels and campaigns perform best at each step of the funnel?
- What chains of actions by sources and channels lead to transactions?
Questions about CRM + online data:
- How does order execution vary across campaigns?
- How does gross profit differ by channel group on a daily basis?
- How does the number of CRM orders and CRM users vary across cities?
- What is ROAS gross profit by source and channel?
- What is the relationship between number of CRM orders and payment and delivery type?
- What is the relationship between conversion and average delivery time and city?
- What is the relationship between CRM orders, number of CRM users, and shops?
In this reference you can find the full data structure for CRM exporting.
CPA campaign questions:
- What are the sources of traffic fraud?
- How many ad purchases were there by brand request?
- Which partner should be paid for an action attributed to an overlapping transaction?
- What is the quality of sessions generated by CPA partners?
ROPO (research online, purchase offline) questions:
- What is the influence of online ads on offline purchases?
- What is the real ROPO purchase conversion window and what is the relationship between transaction value and number of days the user takes to make a purchasing decision?
- How are buyers, transactions, and income distributed by days before completing an offline purchase.
- How many days are needed for a user to come to a decision about buying the most expensive item?
Here is a small FAQ block for OWOX BI Smart Data that addresses how to build a request, what the structure should look like, how to show dimensions and metrics you want to see, etc.
How many metrics can be chosen at one time for a given dimension?
The Smart Data report doesn’t limit the number of metrics you can use. However, with lots of metrics, it’s easier to visualize data in Google Data Studio.
- A list of all possible metrics and dimensions can be found in the reference guide.
How to build a request and what the structure should look like
Examples and question structures can be found in our reference guide:
The best option is to just enter the dimensions and metrics you want to see.
Do these charts show the correct values?
Smart Data reports are based on your full data and ready SQL requests that you can copy and check in your Google BigQuery project.
