Introduction To Business Intelligence Interview Questions and Answer
So you have finally found your dream job in Business Intelligence but are wondering how to crack the Business Intelligence Interview and what could be the probable Business Intelligence Interview Questions. Every interview is different and the scope of a job is different too. Keeping this in mind we have designed the most common Business Intelligence Interview Questions and Answers to help you get success in your interview.
Below are the top Business Intelligence Interview Questions and Answers so that you can easily crack these Business Intelligence Interview Questions which are asked in an interview
1.What is Business Intelligence?
Answer:
The term ‘Business Intelligence’ (BI) provided the user with data and tool to answer any decision making an important question of an organization, it can be related to run the business or part of a business. In short, the business intelligence is used for reporting the specified data of any business which is very important and using which higher management of any organization will take the decision for the growth of their business. Normally below decisions can be decided by any organization from Business Intelligence tool:
•BI is used to determine whether a business is running as per plan.
•BI is used to identify which things are actually going wrong.
•BI is used to take and monitor corrective actions.
•BI is used to identify the current trends of their business.
2.What are different stages and benefits of Business Intelligence?
Answer:
There are following five stages of Business Intelligence:
Data Source: It is about extracting data from multiple data source.
•Data Analysis: It is about providing proper analysis report based on useful knowledge from a collection of data.
•Decision-Making Support: It is about to using information in the proper way. It always targets to provide proper graph on important events like take over, market changes, and poor staff performance.
•Situation Awareness: It is about filtering out irrelevant information and setting the remaining information in the context of the business and its environment.
•Risk Management: It is about to discover that what corrective actions might be taken, or decisions made, at different times.
Following are different benefits of Business Intelligence:
•Improving decisions making.
•Speed up on decision making.
•Optimizing internal business process.
•Increase operational efficiency.
•Helping or driving for new revenues.
•Gaining an advantage in terms of competitive markets with another close competitor.
3.What are different Business Intelligence tools available in the market?
Answer:
There are a lot of intelligence tools available in the market, in between them below are most popular:
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•Oracle Business Intelligence Enterprise Edition (OBIEE)
•Cognos
•Microstrategy
•SAS Business Intelligence
•Business Object
•Tableu
•Microsoft Business Intelligence Tool
•Oracle Hyperion System
4.What is a universe in Business Analytics?
Answer:
The universe is kind of semantic layer in between database and user interface or more correctly it is one of the interfacing layers in between the client (business user) and data warehouse. It actually defines an entire relationship between various tables in a data warehouse.
5.Define or list the differences between OLAP and OLTP?
Answer:
In general, we can assume OLTP is actually helping to provide source data in a data warehouse and OLAP help to analyze the same.
| OLTP System
|
OLAP System
|
|
| Source of Data | Operational data, OLTP are an original source of data. | Consolidated data, OLAP data has come from various OLTP databases. |
| Purpose of Data | For any kind of current or fundamental business tasks. | To help with future planning, problem-solving or decision making. |
| Data Updating | End users initiated data frequently insert or update in the transactional database. | Data updated based on a batch job after one defined time interval. This time can be less or more than one day. |
| Processing Speed | As usual typically very fast. | Obviously depends on the amount of data. After refreshing batch data, sometimes complex queries are taken more than hours. Common habit to add an index to improve the speed. |
| Space requirement | Again relative small considering historical data in the archived state. | Obviously larger as it has to hold all the historical data, the existence of aggregation structures, also require more indexes than OLTP. |
| Database Architecture | Normalized data, so all the tables and data have a proper relationship. | Typically de-normalize of few tables (like factor dimensions). It normally used a star or snowflake schema. |
| Backup and Recovery | Back up is essential requirements on OLTP, as it’s a day to day data, so any loss of data is likely to entail significant monetary loss and legal liability. | Instead of regular backups, some environment may consider simply reloading the OLTP data as a recovery method. |
6.What is dashboard in a data warehouse?
Answer:
The dashboard is nothing but the arrangement of all the reports and graphs on one page. It is nothing but the collection of reports in a different format which has same functionality display on the same page.
7.Explain the difference between data warehouse and transnational system.
Answer:
| Transactional System | Data Warehouse System |
| It normally designed to process with day to day data, so mainly concentrate on high volume transaction processing, rather than backend reporting. | It normally designed to processed high volume analytical reporting and subsequence. It also elaborating report generation. |
| It normally processes driven, means an action of it depends on business-specific task or execution. | It is actually subject-oriented, means it load data from a transactional system, then open to use for any kind of analytical reporting which helps organization for taken proper decision based on that specific subject. |
| It normally handling current transactional data. | It normally handling historical data. |
| Data within a transactional system can insert or update or delete in each task. | Data warehouse data is called as non-volatile, meaning that new data can be added regularly, but once loaded those data are rarely changed. |
| In case of performance or speed, we should always prefer a transactional system for inserting, updating or deleting small volumes of data. | We should always prefer data warehouse to fast retrieval of a relatively large volume of data. |
8.Explain Fact and Dimension table with an example.
Answer:
A Fact table is the center table in star schema of a data warehouse. It actually holding quantitative information for analysis, and maximum time it de-normalized.
A dimension table is one of the important tables in star schema of data warehouse, which stores attribute, or dimension, that describe the objects in a fact table.
Fact table mainly holds two types of columns. The foreign key column allows joins with dimension tables, and the major columns contain the data that is being analyzed.
Example: suppose one company sells products to customers. So every sale will be one fact, so fact table holds that information like below:
| Time ID | Product ID | Customer ID | Unit Sold |
| 4 | 17 | 2 | 1 |
| 8 | 21 | 3 | 2 |
| 8 | 4 | 1 | 1 |
Now in the fact table, there has customer id, so we need to maintain one dimension table for a customer like below:
| Customer ID | Name | Gender | Income | Education | Region |
| 1 | SS | M | 2 | 3 | 4 |
| 2 | AC | M | 3 | 5 | 1 |
| 3 | MS | F | 1 | 7 | 3 |
9. Define or list the differences between a snowflake schema and star schema.
Answer:
| Snowflake Schemas | Star Schema | |
| Maintenance | No redundancy, so easier to maintain. | Holding redundant data, so less easy to maintain. |
| Complexity | More complex query, hence less easy to understand. | Lower complex query, so easy to understand. |
| Query Performance | More foreign key, so longer query execution time. | Less number of foreign key, so query execution is faster compared to snowflake. |
| Utilization | Good to use for data warehouse core to simplify complex relationship (many: many). | Good for Data Mart with simple relationship (1:1 or 1: many). |
| Dimension Table | A snowflake schema may have more than one dimension table for each dimension. | Star schema contains only single dimension table for each dimension. |
| De-normalize | Fact table is in de-normalized form, but dimension table is in normalized form. | Fact and Dimension both the tables are in de-normalized form. |
10.Explain or Define a RAGGED hierarchy.
Answer:
Ragged hierarchy actually maintaining a relationship in case of parent member of at least one member of the dimension is not in the level immediately above the member. As an example, if we think about geographical hierarchy and considering North America as continent then it has a country (like the United States), province or state (like California), and city (like San Francisco). But if we consider Europe, Greece, or Athens it doesn’t have this kind of hierarchy. So in this example, Europe, Greece or Athens branches descend to a different depth, creating a ragged hierarchy.
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