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Business Intelligence Testing

401-408, A-Wing, Pride Silicon Plaza, S.B. Road, Shivaji Nagar, Pune -411006,

Maharashtra, INDIA

Email: info@nitorinfotech.com Tel: +91-20-41020202

Global Delivery Centre:

Overview

Modern enterprises are constantly confronted with ever changing market dynamics and

competition. Rapid progression and transformation of the organization is characterized by the

swift and accuracy of the decisions made by the users at every juncture. Organizations rely on

enterprise data warehouse and business intelligence application to seek answers and aid in the

informed decision making.

During development of Data Warehouse high volume of data is transformed, cleansed,

integrated these various types of changes could lead to data corruption or data manipulation.

Therefore, DW testing is a very critical stage in the DW applications

.

BI system is a well-defined amalgamation of data from diverse heterogeneous system. Poor

data quality has diluted the confidence on the systems and hampered business reputation due

to bad decisions. The diversity of the data introduces multi-fold complexities and challenges and

hence a multi-faceted testing of the BI implementation is pivotal for successful adoption.

This document highlight the challenges associated with BI testing and approach for a successful

testing of the BI systems.

Challenges in BI testing

An effective Business Intelligence comprises of enterprise data warehouse at its core. Data

warehouse is a composite and aggregated data store encapsulating data from diverse

databases. The focus here is consolidating and modeling the data to support faster retrieval of

data. With the mounds of data and complex transformations and business logic on the data the

challenges for testing are diverse. Some of the challenges include:

High volume and data diversity

DWH is built by fetching data from heterogeneous sources such as flat files, transactional

systems, excel sheets etc. Different data sources pose different challenges such as data format,

capture and availability. This gets further complicated when basic validations lack in the

application. Hence, QA needs to acquaint with the diverse data sources and ensure data

movement is as per the defined process.

Data Quality and completeness

Data quality and completeness is paramount important in any BI implementation. The challenge

underlies to ensure all the data loaded from the source to target systems is complete, valid and

accurate as per the business requirements.

Data Loading

Data is fed into data warehouse from the source either in totality or incremental fashion as per

the business need. The objective is to ensure the data is regularly updated at regular intervals

at defined time. Testing should ensure that data is fed and the flow is appropriate as per the

defined business rules.

OLAP (Online Analytical Processing)

Data is stored in a multidimensional format categorized by different dimensions as per business

need. Testing of the OLAP should ensure the data in the OLAP cubes is designed as per the

end reporting needs with all the requisite dimensions and measures. The complexity further

increases when the cube design is complex with multiple tables.

Reports

Reports are the final outcome of any BI implementation. The major challenge is to verify the

representation of different reports to ensure that report conveys the right information to

empower users which in turns contributes in effective decision making.

BI Testing Approach

Nitor Test Center of Excellence (TCoE) with its diverse expertise in testing varied BI products

and applications has well-defined approach for testing. This section states our diverse testing

expertise.

ETL Testing

The objective of ETL Testing is to ensure that the data in the warehouse is accurate, consistent,

and complete in each subject area and across each layer. In order to achieve this objective

Nitor TCoE has come up with the testing approach considering the below listed key activities:

End – to- end data flow from source system to staging database, and data loaded in to

target data warehouse

Staging packages (dimension and fact tables’ packages)

Dimensions& Fact Tables used in data warehouse

Data truncation by populating all the data

Parent child relationship between data

Lookups used in ETL packages

Source to Target table mapping as per design documents (check column mappings in all

transformations and while loading the tables)

Error recovery methods

ETL application substitutes default values, ignores invalid data

Auditing is done properly

Slowly changing dimensions, testing of historical data archival

Validate correct processing surrogate keys

Validate connection strings and package configuration files

OLAP Testing

The objective of OLAP testing is to ensure that data from the data warehouse is mapped and

designed correctly in the OLAP Cube. Due consideration is given to following areas while

testing OLAP cubes.

Browse the cube with measures and dimensions

Testing cube performance by measuring the time to process and browse the cube

Verify all the dimension hierarchies in the cube are correctly defined

Validate the data in cube by testing the data back to the staging database

Validate Measure, Aggregations, KPIs ,Calculations used in the cube

Validate data presented in the cube by querying back to the system

Processing options Update/Full

Validate if data from data warehouse is mapped and designed correctly in OLAP cube

as per business needs and specifications

Report Testing

The objective of this testing is to ensure that the data to be presented should be thoroughly

validated and should be in understandable format and following all the standards mentioned in

design documents. The key aspects in the Report testing include.

Validate the reports that are built either from OLAP Cubes or independent data sources

Report’s Drill down, Drill Up and Drill through functionalities

Report’s layout should be as per design document

Validation of data displayed on Reports

Testing report data at lowest granular level

Report navigation to other reports

Report filters

Parameters

Validate data in the report with the backend data

BI/DW Testing

Testing the BI products/application require a pragmatic data centric approach. Effective testing

of the BI testing should encompass following at every stage of testing.

Data completeness 1. Compare record counts between source database data,

staging table data and data loaded to target DW

2. Compare unique values of key fields between source

database, staging database and target DW

3. Validate that no truncation occurs during the ETL process

Data Transformation

1. Validate the data transformation is as per the business logic

2. Create a spreadsheet of input data and expected results and

validate these with the output of ETL package

3. Set up data scenarios that test referential integrity between

tables

Data Quality

Data quality handles with the ability of an ETL system to take care of

any data modification, rejection, duplication and correction.

Meta-Data Testing

1. Table and column names should be as per requirement

document

2. Source and Target column data types should be as per

requirement document

Performance and

Scalability

1. Load the high volume of Production like data to check the

ETL process and check whether ETL process does it in an

expected timeframe

2. Execute joins single or multiple to validate query performance

on large volume databases

3. Validate the OLAP system performance by browsing the

Cube with multiple options

4. Analyzing ETL package loading time with small amount of

data

5. Analyze the maximum users load at peak and off peak time

that are able to access and process the BI reports

Batch execution

Testing

This testing is carried out to verify the data flow when ETL packages

are run in sequential order toload the high volume of Production like

data to check the data flow.

Job Restart Testing

In a production like environment, the ETL jobs fails for numerous

reasons (such as database related failures, connectivity failures

etc.). The jobs can fail half/partly executed. The Test should also

include such scenarios.

Connection Testing

This type of testing includes verifying connection strings from

different data sources to check that data is receiving and loading

from Source systems to Target systems.

Automation Testing

BI testing is a data driven testing and requires exhaustive testing. As the database grows,

manual testing becomes challenging and daunting. Regression test suite helps to minimize the

mundane activities and helps to test on a periodic basis. Test automation can be introduced in

following areas:

Test data generation

Reports testing

Performance testing

Conclusion

BI product/system builds its bedrock on diverse heterogonous systems.Enterprises actively rely

on BI systems to aid in day to day decision making. While comprehensive features and

reporting capabilities have helped in the rapid adoption, poor quality has stranded the adoption

and strangled the investments. A comprehensive and pragmatic testing is vital to avoid fatal

business loss and instill confidence on the system.

About Nitor Test Services

Nitor Test Center of Excellence (TCoE) is an independent test practice with well-defined testing

processes and profound knowledge of tools and techniques to cater to the testing needs of

varied products/applications. Our professional team of test analysts has worked on products of

varying complexity in terms of technology and functionality, projects and solutions across focus

areas such as Business Intelligence, Portals & Collaboration and Enterprise Mobility.

Nitor offers end to end testing services at aid our partners in continuous business growth through our

niche technology focus and diverse domain expertise. Our TCoE offering include

Highlights of Test Center of Excellence (TCoE)

ISTQB Gold Certified test partner

SSAE Soc- II Type-1 accredited organization

Technology and platform agnostic. NitorTCoE is technology /software / tool independent

70+ strong team of certified test engineers

Seven years of profound QA experience

Strong knowledge base with expertise in diverse methodologies and tools

Contact Us

NitorInfotech

401-408, A-Wing, Pride Silicon Plaza

S.B. Road

Pune -411006; Maharashtra, INDIA

Email: info@nitorinfotech.com