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Designing a Dashboard in Tableau for Business Intelligence

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Tableau is a data visualization platform that focuses on business intelligence. It has become very popular in recent years because of its flexibility and beautiful visualizations. Clients love the way Tableau presents data and how easy it makes performing analyses. It is one of my favorite analytical tools to work with.

A simple way to define a Tableau dashboard is as a glance view of a company’s key performance indicators, or KPIs. There are different kinds of dashboards available — it all depends on the business questions being asked and the end user. Is this for an operational team (like one at a distribution center) that needs to see the amount of orders by hour and if sales goals are achieved? Or is this for a CEO who would like to measure the productivity of different departments and products against forecast? The first case will require the data to be updated every 10 minutes, almost in real time. The second doesn’t require the same cadence, and once a day will be enough to track the company performance.

Over the past few years, I’ve built many dashboards for different types of users, including department heads, business analysts, and directors, and helped many mid-level managers with data analysis. Here are some best practices for creating Tableau dashboards I’ve learned throughout my career.

First Things First: Why Use a Data Visualization?

Visualizations are among the most effective ways to analyze data from any business process (sales, returns, purchase orders, warehouse operation, customer shopping behavior, etc.).

Below we have a grid report and bar chart that contain the same information. Which is easier to interpret?

Grid report

Bar Chart
Grid report vs. bar chart.

That’s right — it’s quicker to identify the category with the lowest sales, Tops, using the chart.

Many companies used to use grid reports to operate and make decisions, and many departments still do today, especially in retail. I once went to a trading meeting on a Monday morning where team members printed pages of Excel reports with rows and rows of sales and stock data by product and took them to a meeting room with a ruler and a highlighter to analyze sales trends. Some of these reports took at least two hours to prepare and required combining data from different data sources with VLOOKUPs — a function that allows users to search through columns in Excel. After the meeting, they threw the papers away (what a waste of paper and ink!) and then the following Monday it all started again.

Wouldn’t it be better to have a reporting tool in which the company’s KPIs were updated on a daily basis and presented in an interactive dashboard that could be viewed on tablets/laptops and digitally sliced and diced? That’s where tools like Tableau dashboards come in. You can drill down into details and answer questions raised in the meeting in real time — something you couldn’t do with paper copies.

How to Design a Dashboard in Tableau

Step 1: Identify who will use the dashboard and with what frequency.

Tableau dashboards can be used for many different purposes and therefore will be designed differently for each circumstance. This means that, before you can begin designing a dashboard, you need to know who is going to use it and how often.

Step 2: Define your topic.

The stakeholder (i.e., director, sales manager, CEO, business analyst, buyer) should be able to tell you what kind of business questions need to be answered and the decisions that will be made based on the dashboard.

Here, I am going to use data from a fictional retail company to report on monthly sales.

The commercial director would like to know 1) the countries to which the company’s products have been shipped, 2) which categories are performing well, and 3) sales by product. The option of browsing products is a plus, so the dashboard should include as much detail as possible.

Step 3: Initially, make sure you have all of the necessary data available to answer the questions specified.

Clarify how often you will get the data, the format in which you will receive the data (inside a database or in loose files), the cleanliness of the data, and if there are any data quality issues. You need to evaluate all of this before you promise a delivery date.

Step 4: Create your dashboard.

When it comes to dashboard design, it’s best practice to present data from top to bottom. The story should go from left to right, like a comic book, where you start at the top left and finish at the bottom right.

Let’s start by adding the data set to Tableau. For this demo, the data is contained in an Excel file generated by a software I developed myself. It’s all dummy data.

To connect to an Excel file from Tableau, select “Excel” from the Connect menu. The tables are on separate Excel sheets, so we’re going to use Tableau to join them, as shown in the image below. Once the tables are joined, go to the bottom and select Sheet 1 to create your first visualization.

Excel Sheet in Tableau
Joining Excel sheet in Tableau.

We have two columns in the Order Details table: Quantity and Unit Price. The sales amount is Quantity x Unit Price, so we’re going to create the new metric, “Sales Amount”. Right-click on the measures and select Create > Calculated Field.

Creating a Map in Tableau

We can use maps to visualize data with a geographical component and compare values across geographical regions. To answer our first question — “Which countries the company’s products have been shipped to?” — we’ll create a map view of sales by country.

1. Add Ship Country to the rows and Sales Amount to the columns.

2. Change the view to a map.

Map
Visualizing data across geographical regions.

3. Add Sales Amount to the color pane. Darker colors mean higher sales amounts aggregated by country.

4. You can choose to make the size of the bubbles proportional to the Sales Amount. To do this, drag the Sales Amount measure to the Size area.

5. Finally, rename the sheet “Sales by Country”.

Creating a Bar Chart in Tableau

Now, let’s visualize the second request, “Which categories are performing well?” We’ll need to create a second sheet. The best way to analyze this data is with bar charts, as they are to compare data across categories. Pie charts work in a similar way, but in this case we have too many categories (more than four) so they wouldn’t be effective.

1. To create a bar chart, add Category Name to the rows and Sales Amount to the columns.

2. Change the visualization to a bar chart.

3. Switch columns and rows, sort it by descending order, and show the values so users can see the exact value that the size of the rectangle represents.

4. Drag the category name to “Color”.

5. Now, rename the sheet to “Sales by Category”.

Sales category bar chart
Our Sales by Category breakdown.

Assembling a Dashboard in Tableau

Finally, the commercial director would like to see the details of the products sold by each category.

Our last page will be the product detail page. Add Product Name and Image to the rows and Sales Amount to the columns. Rename the sheet as “Products”.

We are now ready to create our first dashboard! Rearrange the chart on the dashboard so that it appears similar to the example below. To display the images, drag the Web Page object next to the Products grid.

Dashboard Assembly
Assembling our dashboard.

Additional Actions in Tableau

Now, we’re going to add some actions on the dashboard such that, when we click on a country, we’ll see both the categories of products and a list of individual products sold.

1. Go to Dashboard > Actions.

2. Add Action > Filter.

3. Our “Sales by Country” chart is going to filter Sales by Category and Products.

4. Add a second action. Sales by Category will filter Products.

5. Add a third action, this time selecting URL.

6. Select Products, <Image> on URL, and click on the Test Link to test the image’s URL.

What we have now is an interactive dashboard with a worldwide sales view. To analyze a specific country, we click on the corresponding bubble on the map and Sales by Category will be filtered to what was sold in that country.

When we select a category, we can see the list of products sold for that category. And, when we hover on a product, we can see an image of it.

In just a few steps, we have created a simple dashboard from which any head of department would benefit.

Dashboard
The final product.

Dashboards in Tableau at General Assembly

In GA’s Data Analytics course, students get hands-on training with the versatile Tableau platform. Create dashboards to solve real-world problems in 1-week, accelerated or 10-week, part-time course formats — on campus and online. You can also get a taste in our interactive classes and workshops.

Ask a Question About Our Data Programs

Meet Our Expert

Samanta Dal Pont is a business intelligence and data analytics expert in retail, eCommerce, and online media. With an educational background in software engineer and statistics, her great passion is transforming businesses to make the most of their data. Responsible for the analytics, reporting, and visualization in a global organization, Samanta has been an instructor for Data Analytics courses and SQL bootcamps at General Assembly London since 2016.

Samanta Dal Pont, Data Analytics Instructor, General Assembly London

Excel: Building the Foundation for Understanding Data Analytics

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If learning data analytics is like trying to ride a bike, then learning Excel is like having a good set of training wheels. Although some people may want to jump right ahead without them, they’ll end up with fewer bruises and a smoother journey if they begin practicing with them on. Indeed, Excel provides an excellent foundation for understanding data analytics.

What exactly is data analytics? It’s more than just simply “crunching numbers,” for one. Data analytics is the art of analyzing and communicating insights from data in order to influence decision-making.

In the age of increasingly sophisticated analytical tools like Python and R, some seasoned analytics professionals may scoff at Excel, which was first released by Microsoft in 1987, as nothing more than petty spreadsheet software. Unfortunately, most people only touch the tip of the iceberg when it comes to fully leveraging this ubiquitous program’s power as a stepping stone into analytics.

Using Excel for Data Analysis: Management, Cleaning, Aggregation, and More

I refer to Excel as the gateway into analytics. Once you’ve learned the platform inside and out, throughout your data analytics journey you’ll continually say to yourself, “I used to do this in Excel. How do I do it in X or Y?” In today’s digital age, it may seem like there are new analytical tools and software packages coming out every day. As a result, many roles in data analytics today require an understanding of how to leverage and continuously learn multiple tools and packages across various platforms. Thankfully, learning Excel and its fundamentals will provide a strong bedrock of knowledge that you’ll find yourself frequently referring back to when learning newer, more sophisticated programs.

Excel is a robust tool that provides foundational knowledge for performing tasks such as:

  • Database management. Understanding the architecture of any data set is one of first steps of the data analytics workflow. In Excel, each worksheet can be thought of as a table in a database. Each row in a worksheet can then be considered a record while each column can be considered an attribute. As you continue to work with multiple worksheets and tables in Excel, you’ll learn that functions such as “VLOOKUP” and “INDEXMATCH” are similar to the “JOIN” clauses seen in SQL.
  • Data cleaning. Cleaning data is often one of the most crucial and time-intensive components of the data analytics workflow. Excel can be used to clean a data set using various string functions such as “TRIM”, “MID”, or “SUBSTITUTE”. Many of these functions cut across various programs and will look familiar when you learn similar functions in SQL and Tableau.
  • Data aggregation. Once the data’s been cleaned, you’ll need to summarize and compile it. Excel’s aggregation functions such as “COUNT”, “SUM”, “MIN”, or “MAX” can be used to summarize the data. Furthermore, Excel’s Pivot Tables can be leveraged to aggregate and filter data quickly and efficiently. As you continue to manipulate and aggregate data, you’ll begin to understand the underlying SQL queries behind each Pivot Table.
  • Statistics. Descriptive statistics and inferential statistics can be applied through Excel’s functions and add-ons to better understand our data. Descriptive statistics such as the “AVERAGE”, “MEDIAN”, or “STDEV” functions tell us about the central tendency and variability of our data. Additionally, inferential statistics such as correlation and regression can help to identify meaningful patterns in the data which can be further analyzed to make predictions and forecasts.
  • Dashboarding and visualization. One of the final steps of the data analytics workflow involves telling a story with your data. The combination of Excel’s Pivot Tables, Pivot Charts, and slicers offer the underlying tools and flexibility to construct dynamic dashboards with visualizations to convey your story to your audience. As you build dashboards in Excel, you’ll begin to uncover how the Pivot Table fields in Excel are the common denominator in almost any visualization software and are no different than the “Shelfs” used in Tableau to create visualizations.

If you want to jump into Excel but don’t have a data set to work with, why not analyze your own personal data? You could leverage Excel to keep track of your monthly budget and create a dashboard to see what your spending trends look like over time. Or if you have a fitness tracker, you could export the data from the device and create a dashboard to show your progress over time and identify any trends or areas for improvement. The best way to jump into Excel is to use data that’s personal and relevant — so your own health or finances can be a great start.

Excel at General Assembly

In GA’s part-time Data Analytics course and online Data Analysis course, Excel is the starting point for leveraging other analytical tools such as SQL and Tableau. Throughout the course, you’ll continually have “data déjà vu” as you tell yourself, “Oh this looks familiar.” Students will understand why Excel is considered a jack-of-all-trades by providing a great foundation in database management, statistics, and dashboard creation. However, as the saying goes, “A jack-of-all-trades is a master of none.” As such, students will also recognize the limitations of Excel and the point at which tools like SQL and Tableau offer greater functionality.

At GA, we use Excel to clean and analyze data from sources like the U.S. Census and Airbnb to formulate data-driven business decisions. During final capstone projects, students are encouraged to use data from their own line of work to leverage the skills they’ve learned. We partner with students to ensure that they are able to connect the dots along the way and “excel” in their data analytics journey.

Having a foundation in Excel will also benefit students in GA’s full-time Data Science Immersive program as they learn to leverage Python, machine learning, visualizations, and beyond, and those in our part-time Data Science course, who learn skills like statistics, data modeling, and natural language processing. GA also offers day-long Excel bootcamps across our campuses, during which students learn how to simplify complex tasks including math functions, data organization, formatting, and more.

Ask a Question About Our Data Programs

Meet Our Expert

Mathu A. Kumarasamy is a self-proclaimed analytics evangelist and aspiring data scientist. A believer in the saying that “data is the new oil,” Mathu leverages analytics to find, extract, refine, and distribute data in order to help clients make confident, evidence-based decisions. He is especially passionate about leveraging data analytics, technology, and insights from the field of behavioral economics to help establish a culture of evidence-based, value-driven health care in the United States. Mathu enjoys converting others into analytics geeks while teaching General Assembly’s part-time Data Analytics course in Atlanta.

Mathu A. Kumarasamy, Data Analytics Instructor, GA Atlanta

The Skills and Tools Every Data Scientist Must Master

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women of color in tech

Photo by WOC in Tech.

“Data scientist” is one of today’s hottest jobs.

In fact, Glassdoor calls it the best job of 2017, with a median base salary of $110,000. This fact shouldn’t be big news. In 2011, McKinsey predicted there would be a shortage of 1.5 million managers and analysts “with the know-how to use the analysis of big data to make effective decisions.” Today, there are more than 38,000 data scientist positions listed on Glassdoor.com.

It makes perfect sense that this job is both new and popular, since every move you make online is actively creating data somewhere for something. Someone has to make sense of that data and discover trends in the data to see if the data is useful. That is the job of the data scientist. But how does the data scientist go about the job? Here are the three skills and three tools that every data scientist should master.

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Announcing General Assembly’s New Data Science Immersive

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Data science is “one of the hottest and best-paid professions in the U.S. More than ever, companies need analytical minds who can compile data, analyze it, and drive everything from marketing forecasts to product launches with compelling predictions. Their work drives the core strategies of modern business — so much so that, by 2018, data-related job openings will total 1.5 million. That’s why we’ve worked hard to develop classes, workshops, and courses to confront the data science skills gap. The latest addition to our proud family of data education is the new Data Science Immersive program.

Launching for the first time in San Francisco and Washington, D.C. on April 11, this full-time Immersive program will equip you with the tools and techniques you need to become a data pro in just 12 weeks.

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5 High-Paying Careers That Require Data Analysis Skills

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The term “big data” is everywhere these days, and with good reason. More products than ever before are connected to the Internet: phones, music players, DVRs, TVs, watches, video cameras…you name it. Almost every new electronic device created today is connected to the Internet in some way for some purpose.

The result of all those things connected to the Internet is data. Big, big data. What’s that mean for you? Simply put, it means if you can quickly, accurately, and intelligently sift through data and find trends, you are extremely valuable in today’s tech job market. More specifically, here are five job titles that require data analytics expertise to get ahead. 

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The Best Topical Data Visualizations of 2015 (So Far)

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Data-Visualization

Data visualization is a form of visual communication where data is presented in a pictorial or graphical format. By presenting complex data sets in a visual way, people can comprehend and analyze the information set faster and more clearly.

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