11 Essential Business Intelligence Interview Questions *

Toptal sourced essential questions that the best Business Intelligence developers and engineers can answer. Driven from our community, we encourage experts to submit questions and offer feedback.

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Interview Questions


What is a data cube (or “OLAP cube”)?

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A data cube describes the BI data structure in memory before it is shipped to a BI UI tool to be displayed to the user. It is a multi-dimensional data representation made for better visualization, data slicing, and drill-down techniques. The UI usually does not display a literal cube, but generally 2D slices of it for better human readability:

A typical dashboard backed by a BI database designed around the data cube concept. It visualizes data using graphs, pie charts, and color-coded maps.

A data cube is usually based on a single denormalized fact table and some number of dimension tables representing data cube dimensions. The star and snowflake schemas were specifically designed to aid in building data cube structures in memory.

An example schema might consist of:

  • Time buckets—time dimension table
  • Customers—customer dimension table
  • Products—product dimension table
  • Sales amount (units sold)—fact table

The data cube structure for this schema can be thought of like this:

A cube made of smaller cubes. Its axes are Customers, Products, and Time, and the smaller cubes each represent Measures, which in this example consist of the sales amount.


Describe fact and dimension tables.

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A fact table contains dimension keys and numerical values for some measures. Each dimension key represents a dimension that measures are for. Measures can be aggregated across dimensions to build a drillable data cube.

Dimension tables are dictionary tables used to display dimension labels and information on BI visual interfaces.


What are the steps to implement company BI analytics from the ground up?

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  1. Build company analytical data storage (data warehouses, data marts).
  2. Devise an analytical data storage schema based on both actual company data and BI demands.
  3. Initially, populate analytical data storage with existing company data, and then update it regularly.
  4. Set up BI tools on top of analytical data storage.
  5. Develop BI reports.
  6. Maintain and modify BI reports according to changing needs.

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Name some benefits of data normalization.

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The candidate should name at least two benefits from those listed below. It can be in their own words, as long as it’s close in meaning. The more benefits they can name, the better.

Data normalization:

  1. Removes data duplication.
  2. Allows finer transaction granularity. Each referenced table data could be changed independently in its own transaction without affecting its foreign key relationships.
  3. Enables clearer referential integrity. The smaller entities produced by normalization allow modeling business objects and their relations as close to the real world as is possible.
  4. Allows incremental schema changes. Adding or deleting columns in one table does not affect the structure of referenced tables.

What is a data mart? When is it appropriate to use data marts instead of a single data warehouse?

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A data mart stores a subset of company data that focuses on a specific department, activity type, or set of subproblems.

Separating data into data marts allows for better performance and separation of tasks for BI analysts and business users.

This strategy is a matter of design and operational convenience. While there is no definitive answer on when to use it or not, it’s usually considered appropriate to build a data mart when a company runs different lines of businesses that are very much independent in terms of their underlying data and reporting needs.

For example, if the same company is building trucks and running an online game application, it likely makes sense to handle these sub-concerns in separate data marts.


What are the star and snowflake schemas?

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The star schema consists of dimension and fact tables. Each dimension table represents a “metric” that can be used in BI reporting. A fact table references dimension tables for each corresponding metric the fact table covers.

An example star schema. A central Sales Fact table has a sales ID and sales units, and four IDs referring to dimensional tables: Customer, Product, Date, and Store.

The snowflake schema is an extension of the star schema in such a way that dimension tables could be further normalized and split into main and secondary dictionary tables.

The previous star schema extended to become a snowflake schema. The original five tables are still present, but each dimension table now links to further sub-dimension tables. For example, the Customer Dimension table consisted of a customer ID, name, address, and city; here the city is replaced with a city ID, linking it to a City Dimension table that stores a city name, state name, and zip/postal code for each city id.


Define OLTP and OLAP. What is the difference? What are their purposes?

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OLTP stands for “online transactional processing.” It is used for company business applications. They are most often customer- (i.e., people- or business-) facing.

OLAP stands for “online analytical processing.” It is used for a company’s internal analysis by department leads and company top management to steer the company.


Which BI tools have you used, and what are their good and bad sides?

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There are numerous BI tools on the market, but among the best-known are:

  1. Oracle Business Intelligence Enterprise Edition (OBIEE)
  2. IBM Cognos Analytics
  3. MicroStrategy
  4. The SAS product line
  5. SAP BusinessObjects
  6. Tableau
  7. Microsoft Power BI
  8. Oracle Hyperion
  9. QlikView

This type of free-form question isn’t about the candidate providing a correct answer, per se. It’s more about sparking a discussion so interviewers can get a sense of the depth of the candidate’s expertise, and where that overlaps with the company’s current needs.


What is the purpose of BI?

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BI provides quick and simple methods to visualize company metrics, generate reports, and analyze data.

These methods, in turn, help top management to:

  1. Analyze existing trends.
  2. Lay out company development plans.
  3. Ensure such plans are executed as scheduled.
  4. Detect anomalies and problems.
  5. Apply corrective actions.

Name some benefits of data denormalization.

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The candidate should name at least two benefits from those listed below. It can be in their own words, as long as it’s close in meaning. The more benefits they can name, the better.

Data denormalization provides:

  1. Simpler initial data schema design.
  2. Better data write/read performance.
  3. Direct applicability in data warehouses. Fact and dimension tables in data warehouses are usually designed without regard to data normalization to ensure fast and straightforward data retrieval.
  4. Precomputation and query performance improvements for data cube BI slice-and-dice and drill-down analysis.

What are the primary responsibilities of a BI developer?

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BI developers are generally expected to:

  1. Analyze company business processes and data.
  2. Standardize company data terminology.
  3. Gather reporting requirements.
  4. Match the above requirements against existing data.
  5. Build BI reports.
  6. Analyze the fleet of existing reports for further standardization purposes.

This question can be useful as an opening one—not just to help filter undesirable candidates and put more qualified candidates at ease but also to provide an opportunity to discuss any nonstandard responsibilities that may be involved in the particular job at hand.

There is more to interviewing than tricky technical questions, so these are intended merely as a guide. Not every “A” candidate worth hiring will be able to answer them all, nor does answering them all guarantee an “A” candidate. At the end of the day, hiring remains an art, a science — and a lot of work.

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