Business intelligence analyst vs. data analyst: A comparison

What is a business intelligence analyst?

Business intelligence analysts excel at translating business requirements into the appropriate graphs, charts, spreadsheets and dashboards. They tend to work close to the front line with business users and subject matter experts. They also need a solid understanding of business workflows, finance and accounting to translate the raw data into a form the users are familiar with.

BI analysts need a deep understanding of the technical side of working with structured databases and data warehouses. They must be fluent in writing complex SQL queries and creating complex joins across tables. Familiarity with various query optimization techniques helps ensure they create reports that cut database processing overhead.

BI analysts may come to a job with various SQL data transformation skills. For example, they may work with extract, transform and load tools to transfer subsets of data from an operational database into a data warehouse to support a new query.

Some basic user experience design skills can also help them identify the best way of presenting data to users that is simple and can explain the appropriate story.

The tools of choice for BI analysts tend to be Excel spreadsheets and SQL queries. They use BI tools such as Microsoft Power BI, Salesforce Tableau and Google Looker. BI analysts also tend to work with the data presentation tools baked into BI tools like Tableau, Power BI and Looker. They use these to present their findings to business users, data scientists and data engineers for further development.

A bachelor's degree in computer science or business with some training in SQL is enough to start a career as a BI analyst.

What is a data analyst?

A data analyst excels at exploring complex data sets to identify new patterns useful for specific business groups. They tend to start with broad goals specified by business leaders. Then, they'll spend more time behind the scenes looking for new data sets, mining this data for interesting patterns and wrangling this raw data into new data models.

Data analysts need a solid understanding of data mining techniques, machine learning and statistics so they can separate a promising new data model from mere coincidence. They tend to have solid skills for working with raw data using tools like R and Python for statistical analysis and statistics tools like SAS and KNIME.

A data analyst must also understand the origin and applicability of different data sets. This is more of a challenge with data analysis than BI. Data analysts tend to work with a wider variety of data types and sources of different quality. One day, they may be data mining sales transaction logs to figure out product correlations using tools like RapidMiner or SAS Enterprise Miner. The next day might include text mining email and customer chat logs to look for trends in how different customers feel about the same products using tools like Amazon Comprehend or Lexalytics.

They can also benefit from familiarity with data wrangling tools for combining diverse data sets and data cleansing tools to weed out bad data that might confound analysis.

A bachelor's degree in mathematics or computer science with some training in statistics and data manipulation tools is enough to start a career as a data analyst.

How do business intelligence analysts and data analysts differ?

Both business intelligence analysts and data analysts play valuable roles in understanding how the business has been performing and identifying new opportunities for improvement. In practice, these roles tend to work alongside a wide range of other types of data experts that focus on improving data infrastructure and data modeling and understanding business needs.

It is helpful to consider the four main categories of analytics to understand how the two roles differ and how they complement one another.

• Descriptive analytics helps make sense of what has happened. This can include tracking sales changes across different departments and product lines, as well as using charts, graphs and dashboards.
• Diagnostic analytics tries to explain why something occurred. For example, reduced rain correlates with slower umbrella sales.
• Predictive analytics looks for patterns in data to create models that can predict what might happen in response to a combination of new events. For example, sweltering summer weather predictions could lead to larger ice cream sales.
• Prescriptive analytics suggests actions to improve a particular outcome. For example, a business might stock up on ice cream for sales during warm weather or recommend an online shopper buy a sun umbrella to go with their sunglasses.

BI analysts tend to focus on descriptive and diagnostic tools for surfacing data about the past into charts, diagrams or numbers. This can help a business manager make better decisions. Data analysts tend to focus on exploring data to identify patterns for crafting better predictive analytics and prescriptive analytics that could improve outcomes in the future.

Both jobs need deep technical expertise and an intricate understanding of data. However, BI analysts in the U.S. tend to earn a much higher starting salary than data analysts. One factor may be that BI analysts tend to have a greater understanding of the business, finance and communication skills needed to translate raw data into ongoing business value.

A good data analyst tends to focus on data experimentation that may find patterns, and much of this time and research may lead to dead ends. They tend to have strong technical and statistics skills. However, data analysts do not need the same level of communication skills, as they tend to spend less time engaged with business users.

That said, both career paths contain some of the essential skills for more valuable roles, such as data scientists and data engineers. According to November 2021 numbers from Indeed.com, the average base salary for a data analyst role is around $60,000 per year, while a BI analyst averages around $86,000 per year in the U.S. Those who decide to focus on learning more technical skills could become a data engineer, which has an average base salary of around $117,000 per year. Those who focus on learning more statistical skills could become a data scientist, which offers a base salary of approximately $75,000 per year.