The value and availability of data has become one of the defining characteristics of 21st-century business—and music-related industries are among those who stand to benefit the most. From record labels, music publishers, and artist management companies to streaming services, radio stations, and music technology startups, everyone wants to take advantage of the wealth of information provided by data in order to improve their sales and marketing strategies.
For this they need the help of data analysts, business professionals who specialize in acquiring, manipulating, analyzing, and explaining data. If that sounds a little vague, it's only because data analysis can be used in so many different ways: depending on the type of company employing them, a music industry analyst could be working to discover the next hot artist, identifying core demographics for a concert venue or festival, or using sales data to identify a label artist's next career move.
Ultimately, while technical abilities are the main barrier to entry, it's interpersonal, critical thinking, and problem-solving skills that make a great data analyst.
Data analysis, itself a subcategory of business intelligence, is a broad field. On the more technical side are data scientists and engineers, whose primary job consists of acquiring data, writing processes that clean, organize, and transform said data, and managing the company's data warehouse using Python and SQL. On the other side are dedicated analysts, who utilize a mix of original code and data wrangling software to blow up and examine data from every angle. While they too must possess a solid technical background, their primary focus lies in finding the practical and valuable information hidden within datasets and conveying this information to higher-ups and members of other departments—a part of the process known as "reporting," in which analysts use software like Tableau to create graphical representations of their findings.
Most data analysis positions lie somewhere in the middle of this spectrum, including both technical database-oriented tasks and open-ended analytical projects and presentations. Specialized subjects like machine-learning and neural networks can also be highly valuable for data analysts, allowing them to rapidly scan complex sources like social media posts and transform them into useful data.
At a Glance
Data analysts frequently get their start as interns in business intelligence or analytics departments. For these programs, requirements frequently include prior study of marketing, statistics, or data science; technical skills like SQL and Python; and strong supporting knowledge of the relevant industry. Successful interns are often hired directly on completion of the program or recommended to companies with entry-level openings. Skilled and experienced data analysts may go on to become section heads, department directors, or even high-level independent data consultants.
In the music industry, data analysts are often employed by record labels, music publishers, artist management companies, concert promoters, venue management companies, streaming services, radio stations, and music software companies. Advanced knowledge of Excel is usually sufficient to start a position in data analytics in the music industry. Outside of the industry, analysts can find work almost anywhere there's data and a desire to put it to good use; this can include nonprofits as well as businesses, and even companies without existing business intelligence or data analysis departments. While the majority of data analysis positions are full-time opportunities, freelance opportunities also exist, particularly in the world of independent labels and venues.
- Ability to identify trends in large datasets
- Expert with electronic spreadsheets (pivot tables, V-Lookups, index functions, CSE functions)
- Experience with data reporting and visualization software (e.g. Tableau)
- Knowledge of database management processes and best practices (e.g. data wrangling and/or ETL processes)
- Familiarity with (and sometimes fluency in) SQL and Python
- Outstanding knowledge of Microsoft Excel and Jupyter Notebook
- Experience with major Python libraries like pandas and NumPy
- Written and verbal communication skills
- Basic computer science
- Knowledge of advanced topics like machine learning
Ultimately, while technical abilities are the main barrier to entry, it's interpersonal, critical thinking, and problem-solving skills that make a great data analyst. Analysts must possess exceptional attention to detail, a strong intuition about which questions will produce the most valuable results, and the flexibility to perform well in both independent, self-guided roles and collaborative, team-based ones. Additionally, the ideal analyst should possess developed knowledge of the relevant industry; this not only eliminates the need for a separate subject matter expert (SME) but also greatly reduces the likelihood of costly mistakes—which occur far too often when analysts don't understand the real-world systems and ideas behind the data. Most people would be surprised to discover that strong communication skills are absolutely vital for data analysts, who are frequently called upon to convey their findings to clients and executives who may lack their mathematical background—for which it's essential to be able to find (or create) the narrative behind the data.
Most data analysts are full-time employees who work regular hours in an office setting. A typical day might include routine database management tasks, complex analytical projects, project presentations, and participation in interdepartmental teams.