5 Best Online Courses for Learning Tableau
We offer a wide range of services to help you get the most out of Tableau. However, at Thinklytics, we also believe in educating potential clients. For us, there is no better business intelligence tool on the market than Tableau. The following corporations use it: Amazon Deloitte LinkedIn Walmart Tableau is especially heralded in the following industries: Healthcare Management consulting IT Financial services However, even though it is marketed as easy to use, there is still a learning curve. If you are an experienced data analyst, you will have little difficulty using Tableau and figure out how to get the best out of the remarkable data visualization tool. However, inexperienced users could find it overwhelming, primarily due to its enormous range of features. As such, it makes sense to find an online course that can help you make sense of Tableau. Fortunately, there are a huge array of options. Unfortunately, not all of them are created equal. Some are extremely expensive and don’t offer value for money, while others are a rip-off. This is why we have created a list of five excellent online courses for Tableau. Tableau: Tableau Training Who else is better than the creators of the platform? The official Tableau website has a remarkable level of information and is laden with tricks, tips, and resources. No matter what the problem is, you will ultimately find the solution if you look hard enough. There are endless live training options where you can choose a topic for a one-hour webinar. Audience participation is welcomed with a Q&A session at every training session. There are also virtual training courses and eLearning options where you can explore Tableau at your own pace. There are also hundreds of videos with information ranging from showing you the basics to dashboards and analytics. While a lot of the knowledge is free to use, the eLearning courses charge a fee. Data Visualization with Tableau Specialization: Coursera This is one of the highest-rated Tableau training courses you’ll find. Over 61,000 people have enrolled at the time of writing, and it is free to use. The course offered by UC Davis has a rating of 4.6 stars from over 8,600 reviews. It promises to help you: Discover and learn the different Tableau features available. Analyze the quality of the data you find, and engage in an exploratory analysis. Design visualizations and dashboards for your target audience. Combine the data you use to present the story you want to tell. Overall, there are five course options: Data Visualization with Tableau Project Creating Dashboards and Storytelling with Tableau Visual Analytics with Tableau Essential Design Principles for Tableau Fundamentals of Visualization with Tableau When you complete a course, you receive a certification you can share with prospective employers. Although you can view the information for free, you need to join a membership program to earn the certification. The membership costs around $600 per annum. It is an official program and involves Tableau as an industry partner. The instructors are UC Davis faculty staff, and the institution recommends dedicating three hours a week. If you do that, you should finish the course in around six months. Tableau 2020 A-Z: Hands-On Tableau Training for Data Science: Udemy Kiril Eremenko created this tutorial, and he is known as an experienced instructor, having taught over half a million students online to date. Presumably, the course’s name will change to Tableau 2021 soon! At present, it has a rating of 4.6 from over 58,000 reviews! In general, Udemy courses have a reputation for being extremely expensive, but well worth the price. Initially, this course cost $199.99, but Udemy reduced it to $149.99. The brand often has flash sales where they knock 90% off the price for a short period. This course specializes in Tableau Desktop and is designed for complete beginners. It will help you: Create bar charts, pie charts, maps, and area charts. Work with parameters. Export results from Tableau into different software like PowerPoint. Add actions to dashboards. Create calculated fields in a blend. All you need to do is install Tableau Desktop and enroll in this course. The instructor will show you everything else. It is only 7.5 hours long, so make sure you pay attention! Data Visualization and Communication with Tableau: Coursera This particular course comes from Duke University. It has a 4.7-star rating from almost 2,800 reviews. Once again, you will need to pay for the membership with Coursera if you want to earn the qualification. However, you have the option to enroll for free and read the content. It is course #3 in the 5-part Excel to MySQL: Analytic Techniques for Business Specialization series. It is a four-week program that teaches you the following: Asking the right questions to succeed in data analysis projects. Visualizing data with Tableau. Dynamic data manipulation and presentation with Tableau. Getting the most out of your communication toolbox. You can change deadlines according to your schedule, and the entire course should take around 25 hours to finish. Tableau Essential Training: Lynda If you are a complete novice and have no idea where to begin with Tableau, this basic training course is ideal. It is designed for beginners and is a gentle introduction to the use of the business intelligence tool. The entire course is 4.5 hours long and includes the following topics: Using groups and sets in Tableau to combine data. Using various chart types to create basic visualizations. Explaining the platform’s data source configuration and management. Providing examples of data analysis in Tableau. Applying mapping techniques. At the end of this course, you will have a reasonable grasp of how to use Tableau. You can then proceed with a more advanced course. Final Thoughts on the Best Online Courses for Tableau These days, having a high level of Tableau knowledge can lead to a lucrative career. For example, a qualified Tableau developer can command a salary of over $100,000 per annum. If you run a business, Tableau can help you make sense
What Are the Pros & Cons of Tableau?
One could argue that the process of data visualization has only gained the attention of most companies in recent times. Indeed, we only formed Thinklytics in 2018 after seeing the rising tide of Business Intelligence tools. We decided to focus on the platform Tableau because we genuinely feel it is the #1 data visualization tool. What a lot of people don’t realize is that Tableau was founded in 2003. Its primary aim was to turn data analysis into a comprehensive and interactive process. Suffice to say; the brand has succeeded spectacularly. It is ideal for non-technical users and enables them to transform huge data sets into gorgeous and easy-to-understand graphics. Users with technical ability, such as the team at Thinklytics, can manipulate the graphics even further. As a result, we can use Tableau to create a tailor-made strategy and bring it to life. The result is greater insight into your company’s data, which you can use to innovate and expand. Indeed, Thinklytics is firmly in the pro-Tableau camp. However, we also recognize that it isn’t necessarily for everyone. While it is a brilliant BI tool, it isn’t perfect. Therefore, in this article, we outline Tableau’s downsides while also analyzing its many strengths. What Are the Downsides of Tableau? Although it has a huge array of remarkable features, Tableau has a few things it could improve. Let’s take a look. It is Too Expensive for Some Users Although Tableau isn’t at the very top-end of BI tools pricewise, it is more expensive than well-known rivals like Power BI. Individual users could pay in the range of $2,000 per annum (worst-case scenario) for Tableau Desktop. Larger organizations can invest in a server that supports up to 1,000 people. The downside? You’re likely paying six figures for the privilege. If budget is an issue, you can try Tableau Online and Tableau Public. However, neither offers near the level of features as Tableau Desktop. There is also a hefty maintenance fee to consider. Yet, for companies looking to make it to the top of their industry, Tableau’s cost is value for money. IT Knowledge Still Required While you can get a lot out of data from a CSV file or Excel, you still need someone with SQL skills to create the SQL query needed to connect to a database. This process gives you the full range of Tableau’s functions. Fortunately for your business, the Thinklytics team has the knowledge needed to get the best out of the platform! Sales Team Although Tableau is working hard to fix this issue, its sales team is still behind its rivals. Some users suggest that the Tableau sales team is aggressive. As it is a publicly-traded company, it answers to its shareholders. As a result, the company still needs to make as much profit as possible. This could explain why the sales team remains relatively inflexible. Lack of Functionality for Scheduling or Notification of Reports Tableau doesn’t offer automatic refreshing of reports to help companies with scheduling. Users are unable to create static layouts or data tables. On the plus side, it does provide users with the opportunity to leverage exceptional data visualization. Uploading the data in the back-end requires some effort, but the result is worth it. Too Many Features This is a strange thing to include in a list of Tableau cons! However, the remarkable number of features is potentially overwhelming for new users. The company could help by improving the level of hands-on training it offers. At least you can get in touch with the Thinklytics team. We will help you gain mastery of Tableau. What Are the Upsides of Tableau? Realistically, we could write a book on this subject! However, we’ll keep it down to five (for now). Next-Level Data Visualization Simply put, there is no BI vendor on the market that comes close to matching Tableau for data visualization. It was one of the industry’s first companies to offer users the chance to drag and drop complex data to create outstanding visualization. Newbies can perform a deep dive on charts or highlight sections without IT expertise. Tableau allows you to connect any number of data sources, which it converts into easy-to-read charts and graphs. The Tableau Community While the sales team comes off as aggressive, Tableau does provide a high level of customer support. There is also an active online community with forums that can quickly and easily answer many of your queries. You also have the option of submitting a request for technical support from Tableau’s expert team. Relative Ease of Use While getting the most out of Tableau requires expertise beyond many users, its drag-and-drop interface is extremely useful for novices. You can quickly build basic data visualizations and generate important insights. There is a learning curve if you want to become a Tableau expert. However, you get plenty from it without ever needing to reach that level. It Can Handle an Incredible Amount of Data While rivals such as Power BI have low limits on the data they can handle, Tableau stands alone. It is capable of dealing with millions of data rows with consummate ease. You can create various visualization types with enormous swathes of data without impacting dashboard performance. Outstanding Integration Tableau integrates with far more data types than most of its competitors. This increases the limit on how deep you can dive in the search for advanced data analysis. A prime example of its excellent integration is the fact it offers native support for Google BigQuery API. This is precisely what companies in need of detailed analytics need. Here is a quick overview of the Pros and Cons of Tableau: Final Thoughts on the Pros & Cons of Tableau At Thinklytics, we understand that Tableau is not for everyone. There are a few downsides that may drive companies to a different BI tool. However, in our opinion, Tableau remains the best Business Intelligence tool for organizations that want to
Tableau Versus Power BI
Data visualization and analytics is huge business these days. Brands now fully understand the importance of gathering and analyzing data. The information gleaned from this process can cut costs, boost customer engagement, and increase profits. There are dozens of business intelligence tools, but consumers want a combination of ease of use, price, power, and brand recognition. There are few offerings on the market that provide all of the above better than Tableau and Microsoft Power BI. They are considered two of the best options on the market and are often the subject of comparison. This is precisely what we intend to do today. We compare three key areas to see which option comes out on top: Features Ease of Use Value to a Business Features Tableau You have the option of taking a 14-day trial where you gain full access immediately. It is possible to begin connecting your data sources as soon as the trial begins. Those who build visualizations in Tableau Desktop can share them with their team via Tableau Online or Tableau Server. Tableau has access to a vast array of database sources and servers. Other features include: Importing data of all ranges and sizes. Sharing and embedding dashboards. DAX analysis function. Data blending. Data collaboration & data notifications. The ability to create “no-code” data queries. A vast product range, including Desktop, Desktop Personal, Desktop Professional, Public, Server, Online, and Reader. Power BI There is desktop, mobile, and service options. You may need all of these services to publish your visualizations. The Azure tenant setting is the most basic and involves connection via an Office365 Admin interface. Users can connect spreadsheets, data sources, and apps through built-in connections and APIs. Other relevant Power BI features include: Datasets Reports Q&A question box Trend indicators Real-time dashboards Winner – Tableau Tableau offers significantly more features than Power BI. It utilizes far more data sources with a vastly superior data capacity. On Power BI, each workspace/group can only handle 10GB of data. You need the data to be in the cloud via Azure to handle more. In contrast, Tableau uses a columnar based structure. It stores only unique values for every column. As a consequence, it can fetch billions of rows. Ease of Use Tableau There is something of a learning curve involved in the use of Tableau. One plus is that you don’t need to use any coding language to create charts and scatter plots. Also, Tableau doesn’t restrict the number of data points you can use. When dealing with large datasets, the system could potentially slow down. It could take some time to get a handle on using Tableau. It requires the user to understand the data they want to look at and why before beginning. This is necessary to get the most from Tableau’s integrations and connections. Users must identify the data to pull into the tool before making the connection. Some of the features are hidden behind menus, so it may take time to find the relevant ones. However, even though some users say Tableau has a cluttered appearance, it becomes easy to use once you have enough practice. The ability to drag and drop is also extremely useful. Power BI The Power BI interface is easy to use and emerged as an add-on to Microsoft Excel. It offers API access and pre-built dashboards. Power BI also has a drag and drop feature, which even novice users find simple to handle. With native apps, it is possible to gather data from anywhere. The natural language query tool is a nice addition. Winner – Power BI Overall, Power BI has the edge in user experience, at least in the early stages. It takes longer to get to grips with Tableau. However, this is down to the extra features and functions. There are no datapoint limits with Tableau. In Contrast, Power BI allows a maximum of 3,500. Users must decide if they want to sacrifice greater functionality and more features for simplicity of use. Value Tableau With no limits on data points, Tableau enables users to undergo as sophisticated analysis as they can understand. It recently made changes to its pricing model. Now, it uses a tiered system that looks at connections to files versus third-party apps. You will save money if you have plenty of data on spreadsheets. This is because you can export it from third party tools before uploading to Tableau. Even the lowest cost Tableau option costs more than Power BI, but its advocates say the extra features make it value. With the ability to use 24 different types of data visualization and outstanding customer support, Tableau can justify its extra pricing. It also offers extensive support tools to help you understand how to use the software. It handles a huge amount of data with better performance than Power BI. According to the Tableau website, customers experienced an ROI increase of almost 600% after using Tableau. It is designed for experienced analysts and large businesses. Power BI This product comes at a lower price point, though scaled features and extra users cost more. Companies already invested in Microsoft Software will also benefit from a lower overall price. Power BI doesn’t offer the same depth of data analysis as Tableau and is better suited to SMEs looking to analyze a moderate amount of data. Power BI’s level of customer support doesn’t match up with Tableau. It stores relatively little data outside of the cloud and has limited access to other servers and databases. Also, Power BI’s performance is inferior to its rival when faced with a huge amount of data. Winner – Tableau Ultimately, you get what you pay for with Tableau. It costs more, but you receive far more features, better support, and the ability to analyze more data. What Are the Pros & Cons of Tableau and Power BI? Tableau Power BI Tableau is Better Than Power BI At: Handling large volumes
How to Successfully Implement Tableau’s Row-Level Security
Published on March 8, 2017 by Chris Short, Senior Manager of HR Reporting and Analytics at Cox Enterprises. Article link I have been an avid user of Tableau for several years now, but their major upgrade to Tableau 10 has me super excited. You see, for a long time I have been struggling to find an extendable way to apply row-level security to my Tableau workbooks. Yes, I know there were several ways to do this prior to version 10, but let me walk you through our specific use case and how Cross Data Source Filtering has solved this problem for me. I’ll also explore some of the pros and cons of the approach. Before I even get started, first I want to give a shout out and a big THANK YOU to Chris Beck – a Tableau Technical Sales Consultant here in Atlanta. He was the one that had this idea and recommended I give it a shot. Row-level security is critical in my field of working with Human Resource data, so I really appreciate his help on this. Before we dive into the details, let me first convince you of some of the benefits of using this approach. You can easily retrofit existing workbooks. You don’t have to make a single change to an existing sheet or dashboard. This is very convenient because it allows you to design and build your workbook without worrying about security and then add it in at the end. It’s fast. This technique allows you to maintain data extracts for your main data source – which means you get all the performance benefits that go along with extracts. It is very extendable. We have used this technique with 7 different types of security – division level, department, even down to person level and more. All of them work well. It is very easy to add a new type of security – just make sure you have the proper security map and then follow the steps outlined in this article to apply it to your workbook. The Problem Summary: How can I apply row-level security using a live connection to a security table and an extract for my data set? I manage the HR Reporting & Analytics team at Cox Enterprises, and a part of my team’s job is to build reports/dashboards that can be deployed to a broad user base. Like most large organizations, we have somewhat complex security rules around who can see what… and whom they can see it for. Fortunately, these security rules are most often defined in our source applications. We were able to create a security map that defines what keys a user has access to, and through that we can define various types of security and leverage the same security map to secure our reporting. Let’s look at an example with the Superstore dataset. This dataset has the following fields: Next we need to define a security map. Now our organization has built a custom table and process to load this map, but in this example we’ll create a simple one in Excel. Let’s say we have two different users – Hal, who should only have access to the Corporate segment, and another, Chris, who has access to all segments: Corporate, Consumer, and Home Office. For this example I’ve chosen to implement row-level security based on the segment, but that is arbitrary… you could implement security for any key you’d like: by department, region, etc. Anyways, for this example the security map would look like this: Although in this example the Security Map is small, in reality this table can become quite large. Currently we have 500+ users with a combined count of ~92 Million security keys. Given the size of that table, it is unrealistic to create an extract for it – instead, we want connect to this table with a live connection, filter on the rows for the logged in user, and apply that security to our extract of the Superstore dataset. The Solution Summary: Use Cross Data Source Filtering to apply row-level security to our extract while maintaining a live connection to the security map. Next we will walk through how to apply this row level security to the Superstore dataset. 1.) First we will make sure our data set is an extract. Although this is not a required step, for us this was a big part of the value of this approach. Our data sets are often large, and being able to store them as an extract significantly improves the performance of the workbook. 2.) Next we will add a second Data Source for our security map. I loaded our simple security map into a table in an Oracle database for this example. It is important that each user only have a unique list of security keys for each security type. In other words, you wouldn’t want ‘CSHORT’ to have two rows for ‘Corporate’ in the example below. This is because when you apply the cross data source filter it essentially invokes a join. Having multiple rows will cause the results in your main data set to cartesian and may cause incorrect results. We will leave this connection as a “Live” connection: 3.) Next we want to limit the results of the Security Table based on the user that is logged into Tableau. We will do this by applying a data source filter. Select “Add” and pick the “Username” field. We will add a filter based on a Condition. The formula compares the username of the logged into Tableau user using Tableau’s delivered USERNAME() function. Both sides are wrapped in UPPER() to make sure that both sides are in the same case. After clicking “OK” I can see that the filter is working because it is only keeping my username: 4.) Now we can prepare our data set to have the security applied. In this example we have decided to secure the data set by segment. This means that the security map tells you which user has access to which segment. For this reason, we need to set segment as the security key for our data set (the extract). Select the Superstore
Tableau Acquires ClearGraph: Database Talk! – Forbes
Simpler to use than database query language (and easier to look at too). Adrian Bridgwater , CONTRIBUTOR Data visualization company Tableau Software has acquired ClearGraph, a Palo Alto startup that produces what it calls ‘data discovery’ and analysis technology that works through spoken ‘natural language’ queries. There’s an almost mirror-like balance to what is going on here, so what do these two firms actually make? Data: easier out, easier in? Tableau’s technology allows users to graphically visualize data held inside databases. That means users don’t just have to look at columns and rows, they can more easily see trends in data when it is represented by heat maps, pie charts, scatter plots, gantts, and bubble charts. Equally, for users that aren’t skilled in how to run database queries, ClearGraph’s technology allows users to query data using the spoken word. With Tableau now integrating ClearGraph into its own IT stack, this is a kind of easier out (using interactive visualizations) and easier in (using simple voice commands) approach to how to work with database information. “We founded ClearGraph because we saw a need to bridge the gap between humans and computers through natural language, especially when it comes to exploring data,” said Andrew Vigneault, CEO of ClearGraph. “Tableau is a natural fit for us because we have similar missions, cultures and a genuine desire to help more people around the world access, interact with and get answers from their data.” The most recent updates from Tableau saw the firm upping the machine learning abilities of its data visualization tools i.e. if a user (perhaps one without a statistically-gifted brain) isn’t sure what kinds of trends they should be looking for in the data graphics they are looking at, then the system itself will start suggesting metrics that could be worth tracking. For the firm to buy ClearGraph and put natural language queries into its total offering, this almost flows in a similar vein i.e. it’s another extension to make big data analytics accessible to people outside the database engineering department itself. But what is (arguably) most interesting of all is the way this technology will deliver computational linguistics inside the context of semantic inference-based natural language interfaces. That sounds complex, but it’s not… it’s all about how well we humans present our spoken queries to computers. Humans speak with ‘too much meaning’ If we ask a computer system the price of BMWs for sale in the state of Maryland, it’s logical reply will include ALL possible answers. We might more logically want to know the price of cars that are either used or new. We might want to filter out cars that are more than two hours journey away. We might also want to set a certain price bracket. How about color? What about interiors? The problem is that there is ‘too much meaning’ in our original query. So the only way of asking for this query accurately would be to say: ‘give me the details of BMWs in Maryland within a radius of 100 miles under $20,000 excluding any with yellow paint jobs’… and even then we haven’t managed to specify leather interiors. This is where the most recent advances in natural language computing also have an appreciation for human ‘intent’ so that queries can be automatically narrowed and refined before they are delivered. Accessing and analyzing data using ClearGraph requires no technical training, as the system can infer users’ intent. For example, people could ask questions such as, “Total sales by customers who purchased staples in New York,” then filter to, “orders in the last 30 days,” then group by, “project owner’s department” for example. Tableau is saying that ClearGraph brings a consumer-like experience to users by connecting disparate data sources and making them accessible and intelligible through simple conversational style search. ClearGraph’s natural language query technology stores semantic data in knowledge graphs that can expand and learn over time. “We are thrilled to bring the ClearGraph team to Tableau to enable people to ask questions of their data using natural language,” said Francois Ajenstat, chief product officer at Tableau. “Natural language queries will make it easier for more people to interact with Tableau, whether you’re an executive who needs an answer quickly, or on a mobile phone and want an answer from your data on the move. We’re excited about this acquisition as the ClearGraph team shares our mission and is aligned with our innovation perspectives on conversational analytics.” Will Tableau worry Microsoft or Oracle? Will Tableau win over fans of Microsoft Power BI and leave Redmond fuming with this the (only) third acquisition in its company history? Could firms who are not sold into the Microsoft Azure cloud computing stack see this as a way of getting easier access to database query and analysis tools to use across a wider transept of computing platforms? Yes, a bit, somewhat, but not enough to a cause too many ructions in Redmond or indeed inside the hallowed halls of Oracle’s inner sanctum. Instead, we might reasonably argue that this logical enough acquisition will strengthen and diversify Tableau’s own customer base who will now get some extra smart tools and help more users to start getting exposed to data analytics. It’s a big (smart) Business Intelligence pie and there’s enough for several cooks to stir this cauldron. [/fusion_text][fusion_text animation_direction=”left” animation_speed=”0.3″ animation_delay=”0″ hide_on_mobile=”small-visibility,medium-visibility,large-visibility” sticky_display=”normal,sticky”] Curious to know more? Schedule a consultation with Thinklytics to know more about how data analytics can make your life easier and make you a hero at work.
5 Expert Data Visualization Tips for SME Tools
NAZIRAH GARRISON MAY 10, 2016 Let’s talk about data—specifically visualizing your data in a way that unveils insight faster. You already know that using visualizations to communicate data is fast, easy, and efficient (pictures are so much easier to interpret than a crosstab full of numbers!). But you might be wondering how to communicate with your data even faster. Here are five tips to let your data speak for itself. 1. Less is more. Keep it simple! Strip off extraneous “non-data” ink (i.e. anything that doesn’t help you understand the data), and keep your chart types simple and easy to interpret. Ask yourself (or even your colleagues): Does this help people understand the data? Let’s look at Girl Scout cookies (because who doesn’t love Girl Scout cookies!) and see what our most popular varieties are: image: https://cdnl.tblsft.com/sites/default/files/blog/newnaz1.png We can see from the table (after a bit of reading) what our two most popular cookies are: Samoas and Thin Mints. But ranking the rest of them takes a little more brain power. The human brain can deal with about seven bits of information before it starts to drop things. And we’re going to push our limit if we just stick to the table. Let’s visualize the information. image: https://cdnl.tblsft.com/sites/default/files/blog/naz2.png Now I have my cookies represented by different colors in a bar chart. Except which one’s Rah-Rah Raisins and which one’s Trefoils? The Samoas and Thin Mints kind of look reasonably similar in terms of numbers, and the Samoas look closer to 70 than 60. I think we can tidy this up a bit and remove the 3D effects. And we only need one color if we use labels: image: https://cdnl.tblsft.com/sites/default/files/blog/naz3.png There! Much better. We removed all the stuff we didn’t need. And now, instead of being overloaded with all the information presented, your brain can quickly and easily understand what’s going on. We used the brain’s ability to process certain things (like position and length) and removed things that confuse our brains (like 3D images on a flat plane) to help us understand our data much more quickly. 2. Pretty doesn’t mean effective To misquote Lorelei Lee: “Pretty doesn’t mean effective, but my goodness, doesn’t it help?” There’s a misconception that an effective data visualization must be aesthetically pleasing to be effective. Not true! Sure, “pretty,” as in so many cases, can help, but make the focus “aesthetically pleasing to the eye,” and you risk drawing your viewer’s eye away from the most important thing in your visualization—the data. Take a look at the chart below. It shows the Seattle-area housing market, more specifically which counties the for-sale homes are located. How easy is it to compare counties? Where are the most houses are listed? Where would I have the hardest time buying a house? Where is your eye drawn preferentially? Is that where it should be drawn? Are you missing important information? But this is something that is also audience-dependent. Sometimes you want something pretty and eye-catching so that people are drawn to what you’re saying. But when that comes at the expense of being able to communicate what your data shows, you’ll lose someone’s interest just as quickly as you gained it. 3. Use color judiciously I could write an entire post on color usage, but I’ll sum it up in three tips. Color for Numerical Scales? Use with Caution. This can get confusing. The way you interpret a shade depends on other colors around it. Take a look at this map. If I ask you whether Idaho is doing better than Nevada, what would you say? Instinctively, you’d say Nevada’s doing the same, but the reality is that Nevada is doing better than Idaho. Nevada just looks paler because it’s next to California, which is doing really well. image: https://cdnl.tblsft.com/sites/default/files/blog/map_color.png We might be tempted to make a map with our data just because we have geographical fields and maps are so easy to make with Tableau. But, again, ask yourself the question: Does this help answer the question? In the case of a map, they are really useful for looking at clusters, especially if the data contains geographical grouping. Perhaps we have fewer sales in northern states in the winter because it’s too cold, for example. Maps tend not to do so well for simple comparison between states or regions for a number of reasons (we’ll talk about those next). But remember: Color perception, is, as mentioned earlier, a tricky thing. And having light and dark colors next to each other can lead to false conclusions. Choosing the right chart type for the question you’re asking is critical! The bar chart here answers the question of “which state is doing better?” far more effectively than the map. image: https://cdnl.tblsft.com/sites/default/files/blog/bars2.png Leverage Color Associations Everyone knows that strawberries are red, pears are green, and oranges are, well, orange. Use that! And not just for everyday things. If you have a brand identity that your users strongly associate with, take advantage of that. Color is an instant key that leverages long-term memory as soon as you see it. We could confuse people: image: https://cdnl.tblsft.com/sites/default/files/blog/fruit1.png Or we could make it so that people don’t even need the legend to interpret the figure: image: https://cdnl.tblsft.com/sites/default/files/blog/fruit2.png Use Bright Colors to Highlight Want to attract attention to a certain part of your data? Alarm colors draw the eye quickly to areas that need attention, and help get that message across. Are our sales meeting targets? We can use visual best practices, minimize data ink, and create a bullet chart for easy comparison. We can color-code products that are meeting targets and those that aren’t: image: https://cdnl.tblsft.com/sites/default/files/blog/colorbars1.png The orange certainly captures my attention and tells me where I have problems, but that blue is pretty bright, too (and we’re using Tableau’s default color palette here, which was designed so that any one color doesn’t stand out too much). We want to draw attention to the orange. So let’s try a different approach: image: https://cdnl.tblsft.com/sites/default/files/blog/colorbars2.png Much better. The
