4 essential skills for data analysts in Tableau

Data—and the power to leverage it—is arguably the top strategic advantage of businesses today. And companies are understandably building up their data workforces and investing in the hiring and development of key roles like business intelligence and data analysts. These analysts must be adept at the suite of data tools available today, but especially Tableau, the dominant analytics platform for enterprises.  

Accessing, synthesizing and sharing data as a modern data analyst specializing in Tableau requires technical, analytical and communication acumen. Rocking this linchpin role requires mastery of interdisciplinary domains ranging from statistics, to hands-on computer skills, color theory and facilitation. Here are the four essential skills you need to excel as an analyst. 

Day-to-day, analysts are required to interrogate data sets, answer questions and provide direction based on interpretations. As such, you need a sound foundation of mathematical and statistical concepts. Summarization and aggregation are often required to synthesize and interpret data. As an example, wielding the median and quartiles opposed to the average with outlier-sensitive data can assist in obtaining an accurate picture.

Analysts are also well versed in techniques to test data: from simplistic A/B testing, to developing models that fit and describe your data, to ANOVA and hypothesis testing.  

Beyond knowing the right statistical methods to apply, you should understand how data relates to itself. Analysts must be adept at understanding different data structures and storage methods and how to create robust data sets using concepts like primary keys, one-to-many relationships, dimensions and facts.

To truly leverage data, analysts need a strong grasp of Structured Query Language (SQL). Commonly thought to be the most popular programming language, SQL is the key to accessing data inside of databases. Familiarity with how to retrieve data, create joins, develop stored procedures, create aggregations and work with data types will go a long way in getting your hands on the data you crave.  

Additionally, the ability to effectively offload intensive computations and customize structure at the data model level can be critical in creating sophisticated visualizations. The proficient data analyst will have enough fluency in these concepts to partner with data engineers and architects to facilitate the creation of custom data models for analysis.  

Lastly, Tableau uses VizQL to translate user actions to SQL, so depth in this skill aids in creating optimized, performant work that scales.

Tableau Desktop has positioned itself as the predominant tool used by analysts to connect, interact and visualize data. It has the ability to access data from a wide range of data sources, easily work with and commingle multiple data sets and quickly create analytical content designed for mass consumption. Its responsive interface and drag-and-drop approach allows you to stay in the flow of analysis. Tableau Desktop is key to each phase of analysis: data exploration, curation, presentation and communication.  

Analysts should know how to effectively create different views or visualizations and how to maximize Tableau Desktop’s built-in features. Highly skilled analysts put aside the “show me” menu to create bespoke content aimed directly at complex business questions. True mastery means you start with the marks card, are comfortable performing manipulations on top of views by using table calculations and adept at fine-tuning data granularity by using level of detail expressions. In the world of self-service analytics, you need a firm grasp on adding interactivity and using actions and sets to aid end users in drilling into their burning questions.

Communicating your insights and findings from data caps off the other skills you’ve built, and is possibly the most critical component. It’s important for every analyst to focus on how their findings are communicated. This means:

• Effective usage of chart types based on the analysis
• Understanding pre-attentive attributes like size and position
• Simplifying solutions that maximize cognition and understanding.  

These pillars of data visualization literacy build the bridge needed for others to extract important findings from your work.  Today’s top analysts know the right chart to answer the most common business questions and are skilled enough to bend best practices to suit their audience. Concepts in color and how to apply to categorical vs. numerical data, layout, and the accompaniment of text will be top priorities as you push to continually refine and simplify your approach.

It’s safe to say there’s never been a better time to be an analyst; the demand for data experts in on the rise. Speed up your success in this booming sector by honing your essential skills and identifying what you need to work on to get to the next level. 

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