Your browser is not supported. Please download another browser to be able to use all of the Maven features.

Business Intelligence Skills

Why We Chose Our BI Stack

7 min readView all articles
By Chris Dutton
Jul 28, 2021

When it comes to analytics and BI tools, it's easy to feel overwhelmed.

With dozens of platforms, libraries and languages all fighting for your attention, how do you know which ones to prioritize and which to ignore?

Analytics Tool Landscape

Better yet, how do you figure out where to even start?

If you’re an aspiring data analyst or business intelligence professional looking for some guidance, we’ve got your back.

In this post we’ll break down the landscape, introduce common types of tools, and share some insight into our favorite “stack” for aspiring business intelligence pros.

The Analytics Landscape

Generally speaking, most analytics tools fall into one of these 5 categories:

  1. Self-Service BI Platforms
  2. Database Tools
  3. Spreadsheet Tools
  4. Data Prep & ETL Tools
  5. Programming Languages

Let's take a minute to define each category, highlight the industry leaders (yes, we're probably biased), and introduce some common personas and use cases:

1. Self-Service BI Platforms

Full-stack tools designed to support the entire BI workflow, including data prep, modeling, analysis, visualization and administration.

Self-Service BI Platforms

Industry Leaders:

  • Microsoft Power BI
  • Tableau
  • Qlik

Typical User Roles:

  • Business Intelligence Analyst
  • Data Analyst
  • Data Visualization Specialist

Common Use Cases:

  • Performance reporting & administration
  • ETL & data prep
  • Ad hoc data visualization

2. Database Tools

Tools and query langauges used for creating, managing, and analyzing information stored in relational database management systems (RDMS).

Database Tools

Industry Leaders:

  • MySQL
  • Microsoft SQL Server
  • PostgreSQL

Typical User Roles:

  • Database Administrator
  • Business Intelligence Analyst
  • Data Scientist

Common Use Cases:

  • Storing large datasets
  • Building & querying databases
  • Ad hoc exploratory analysis

3. Spreadsheet Tools

Tools used for creating, managing, modeling and analyzing structured data stored in rows and columns.

Spreadsheet Tools

Industry Leaders:

  • Microsoft Excel
  • Google Sheets

Typical User Roles:

  • Data Analyst
  • Business Intelligence Analyst
  • Financial Analyst

Common Use Cases:

  • Ad hoc exploratory analysis
  • Performance reporting
  • Financial modeling

4. Data Prep & ETL Tools

Tools and platforms designed for extracting, cleaning, transforming, and loading data from disparate sources into a centralized location for storage or analysis.

Data Prep & ETL Tools

Industry Leaders:

  • Talend/Informatica (cloud)
  • Alteryx
  • Tableau Prep
  • Power Query (for Excel & Power BI)

Typical User Roles:

  • Data Engineer
  • Database Administrator
  • Business Intelligence Analyst

Common Use Cases:

  • Building data pipelines
  • Designing automated ETL processes
  • Integrating data for storage or analysis

5. Programming Languages

Coding languages and packages commonly used for statistical analysis, machine learning and data science.

Programming Languages

Industry Leaders:

  • Python
  • R
  • SQL

Typical User Roles:

  • Data Scientist
  • Machine Learning Specialist
  • Data Engineer

Common Use Cases:

  • Training ML models
  • Analyzing large datasets
  • Processing structured & unstructured data

The Business Intelligence Stack

Business intelligence requires a deep and diverse set of skills, which typically include:

  • Extracting, transforming & loading data from multiple sources
  • Cleaning, preparing & profiling data for analysis
  • Building & querying relational databases
  • Conducting exploratory analysis (EDA)
  • Designing effective visuals, reports & dashboards

As you begin to develop these skills, you'll begin to identify a collection (or "stack") of go-to tools which help you perform the responsibilities of your job.

Stacks vary from role to role and from person to person, so it's important to keep in mind that there's no "right" or "wrong" approach; what matters most is that the tools you use allow you to work effectively and efficiently.

Here a few examples of common stacks, for various types of analytics roles:

Common Tool Stacks

For Business Intelligence Analysts, we recommend a core stack comprised of Excel, MySQL, Power BI and Tableau.

This stack will give you everything you need to build a solid, well-rounded skill set, and support everything from database administration and data modeling to ETL, EDA, data visualization, and more.

Furthermore, mastering these tools will help you build strong fundamentals and shorten the learning curve as you pick up new tools and technologies down the line.

Let's break down each tool specifically, and why it earned a spot in our stack:

Why Excel?

Believe it or not, Microsoft Excel is an extremely versatile and highly capable analytics tool, even for heavy hitters. Once you become fluent with tools like Power Query, data models, DAX measures and dynamic arrays, you'll realize why we think Excel deserves to be the first tool in every analyst's kit.

💡 PRO TIP: Make Excel your first priority, and focus on mastering formulas, charts, PivotTables, Power Query and DAX. This alone will put you a step ahead of many analysts!

Key Benefits:

  • The most ubiquitous data tool on the planet (750 million+ users)
  • Free and highly accessible
  • Amazing platform for building strong, foundational skills
  • Handles ETL, data modeling, exploratory analysis & custom visualization
  • Microsoft continues to invest in new features & functionality (user-defined functions, collaboration tools, new data types, etc.)

✔️ Looking to master Excel? Check out our Excel Specialist Path.

Why Power BI?

Every business intelligence professional needs a full-stack tool that supports the entire workflow, from data prep to delivery. This is an area where Microsoft Power BI shines, earning its place as the #1 self-service BI platform and an industry leader for 14 consecutive years (Gartner, 2021).

Power BI Magic Quadrant 2021

💡 PRO TIP: Power BI and Excel share some of the same underlying calculation engines (M & DAX), so your skills will translate nicely between platforms.

Key Benefits:

  • Widely regarded as the #1 rated self-service BI platform
  • Integrates extremely well with familiar tools like Excel
  • Free to get started, and affordable to use professionally
  • Includes native ETL capabilities (Power Query & dataflows), visualization tools, and report administration (Power BI Service)
  • Microsoft continues to invest in new features & functionality (AI visuals, Python/R integrations, custom visuals, etc.)

✔️ Looking to master Power BI? Check out our Power BI Specialist Path.

Why Tableau?

Tableau is arguably the most popular data visualization tool in the world, a fan favorite amongst analytics professionals, and a perennial leader in the self-service BI space (right there with Microsoft). With the power of Tableau Prep, Desktop and Server, you'll be equipped to handle everything from ETL to dashboard design and administration.

💡 PRO TIP: Learning both Power BI and Tableau can be a powerful combo, but focus on one at a time. We recommend Power BI for those looking for solid, well-rounded BI capabilities, and Tableau for those who want more advanced data visualization features.

Key Benefits:

  • One of the most popular BI and data viz platforms in the world
  • Free to get started, and (relatively) affordable to use professionally
  • Offers powerful and highly customizable data visualization capabilities
  • Includes a suite of tools designed to support the entire BI workflow at scale (Tableau Desktop, Prep & Server)
  • Has an active user community and a huge library of free resources (Tableau Public, User Groups, Zen Masters, etc.)
  • Tableau rolls out minor updates monthly, and larger features quarterly

✔️ Looking to master Tableau? Check out our Tableau Specialist Path.

Why MySQL?

Structured Query Language (SQL) is the standard language for relational database management. MySQL is a powerful variant of SQL commonly used for creating, storing and analyzing data, and a core skill for Database Administrators and BI Analysts alike.

💡 PRO TIP: There are many "flavors" of SQL out there (MySQL, PostgreSQL, SQLite, etc.), but they are all based on the same universal standards. We recommend MySQL due to its flexibility, compatibility, and user-friendly syntax.

Key Benefits:

  • Completely free to use and practice (MySQL workbench + community server)
  • User-friendly syntax for database administration and analysis
  • Flexible, scalable, secure, and highly reliable
  • Compatible with both Mac + Windows operating systems
  • Trusted by some of the biggest tech companies in the world

✔️ Looking to master MySQL? Check out our MySQL Specialist Path.

Tips for Success

Last but not least, let's wrap up with some tips for success.

#1: Think about skills first, tools second. Don't focus on which tools you want to learn; focus on which skills you want to build.

#2: Don't be afraid to specialize. Aim to go deep rather than broad, and try to build 1-2 expert-level skills (vs. 10 mediocre ones)

#3: During the learning process, stay focused. Prioritizing one tool at a time helps you learn more efficiently and retain the skills you build.

#4: Build the right stack for your role. At the end of the day, the best stack is the one that allows you to do your job effectively, that's it!

Happy learning 🤟


Ready to build practical, job-ready data skills of your own?

Create your custom learning plan today, and save up to 50% on all-access memberships when you upgrade to a paid account.

This is a limited-time deal, make sure you take advantage of the savings!


Maven Paths


To get analytics tips & tricks delivered directly to your inbox.


Chris Dutton

Chris is an EdTech entrepreneur and best-selling Data Analytics instructor. As Founder and Chief Product Officer at Maven Analytics, his work has been featured by USA Today, Business Insider, Entrepreneur and the New York Times, reaching more than 1,000,000 students around the world.

Chris is an EdTech entrepreneur and best-selling Data Analytics instructor. As Founder and Chief Product Officer at Maven Analytics, his work has been featured by USA Today, Business Insider, Entrepreneur and the New York Times, reaching more than 1,000,000 students around the world.

You May Also Like

Ready to become a

data rockstar?

Start learning for free, no credit card required!

Sign Up for Free