A Handbook for Building an Efficient
Business Intelligence Testing Strategy

A Handbook for Building an Efficient </br> Business Intelligence Testing Strategy

Introduction

While Business Intelligence (BI) is not a new concept, its definition has been modified over the years with reference to the changes happening around it. The most recent definition of BI is that it is a technology-driven process that unites business analytics, data processing, data visualization, data tools and infrastructure, and best practices to help organizations to form more data-driven decisions. The Business Intelligence environment involves business models, data models, and ETL tools to create and transform the information into useful knowledge. It consists of varied stages like collecting data, cleansing of knowledge, analyzing, combining, and sharing data to get actionable insights that accelerate business growth.

” A recent Business Intelligence survey reported that the global BI market could hit $29.48 billion by 2022.”

With the boom of Business Intelligence, the field of BI testing has also steadily gained prominence. BI testing is a process that ensures that the implementation is correct by verifying the ETL process, staging data, and BI reports. Some of the questions surrounding the field of BI testing are, What is the role of testing in Business Intelligence? How is it different? What are the benefits of BI testing? This whitepaper endeavors to answer these questions and explores the field of BI testing and its importance with reference to our past experience of implementing it.

A Handbook for Building an Efficient </br> Business Intelligence Testing Strategy

What does Business Intelligence offer?

Before we start talking about BI testing, let us take a look at the various benefits that Business Intelligence brings to businesses that employ it. Business Intelligence is a direct outgrowth of a sequence of past systems intended to help decision making. The development of the data depot as a repository, the improvements in data cleansing that drive to a single truth, the more prominent capabilities of hardware and software, and the growth of technologies that contributed to the widespread user interface combine to generate a richer Business Intelligence ecosystem than what was available earlier. Some of the prominent benefits of implementing Business Intelligence are:

  • Describing the technological support that collects, stores, and analyzes business data.
  • Parsing data and generating reports and data that support administrators to execute better decisions.
  • Enabling companies to perform more desirable decisions by determining present and past data within their industry context.
  • Business Intelligence can provide performance and competitor benchmarks to get the organization to run smoother and higher efficiency.
  • Easily spotting market trends to boost sales or revenue.

Processes involved in Business Intelligence

Now that we have discussed the benefits of Business Intelligence, let us move on to discuss the various processes involved in BI. Business Intelligence gives organizational data in such a way that the organizational information filters can quickly associate with this data and turn it into information for the organization. People involved in Business Intelligence processes may use application software and other technologies collectively, store, analyze and provide access to data, and present that data in a simple, useful manner.

  • Data Mining: Handling databases, statistics, and machine learning to reveal trends in massive datasets.
  • Data Preparation: Collecting multiple data sources, classifying the dimensions and measures, developing it for data analysis.
  • Performance Metrics and Benchmarking: Analyzing current performance data to archival data to trace performance against goals, using customized dashboards.
  • Data Visualization: Using data analysis into visual representations such as charts, graphs, and histograms to further efficiently consume data.
  • Reporting: Sharing data analysis to stakeholders so they can make decision making easier.

Role of Testing in Business Intelligence

One of the important factors for the success of BI projects is the level of confidence in the data displayed in BI reports and dashboards. The deficiency of trust in the data reduces user approval and often results in BI project failures. Business Intelligence Testing verifies the Source Data, ETL Method, BI Reports, and ensures that the implementation is accurate.

BI Testing guarantees data credibility and accuracy of the final decision making for business management. Effective integration of testing in the implementation method builds trust and confidence amongst business users as they execute crucial strategic decisions, based on the BI data created. Testing of Business Intelligence (BI) applications is a bit distinctive than testing traditional transactional applications as it requires a data-centric testing approach.

Proper testing is a necessity to ensure that the unified data are correct, complete, and up-to-date. Both the technical team and the client need to participate in the testing to ensure that the results are as expected. Performance is the most common issue in Business Intelligence projects. Performance issues could either be caused by a bad design or system capacity limitations.

A Handbook for Building an Efficient </br> Business Intelligence Testing Strategy

Different Phases of BI Testing

An absolute test strategy is the stepping stone of an effective test cycle. The strategy should include test planning for each stage, every time the data moves, and state the responsibilities of each stakeholder. Business Intelligence or BI testing enables companies to get better and detailed insight on data that supports them in making healthier decisions. This has been ample for both businesses and users, as businesses understand what is working and what is not, and the users get a better experience.

Business Intelligence, in itself, is a combination of technologies, applications, and components that constitute the whole implementation. It can’t be resolved through any particular tool or system. So, while testing Business Intelligence, you must test the complete flow, right from the time when you acquire the data from the source until the end reporting. There is a distribution of the flow of events, as shown below:

 

Testing the Source Data

Several organizational decisions depend on the data warehouse, and hence the source data should be of the highest quality. In order to assure that business owners make smart and accurate decisions, testing must be designed and executed very efficiently to avoid inaccurate data being pumped into the database.

The main aim of Source Data Testing is to ensure that all of the data that needs to be loaded in the target is extracted. During the Source Data Testing phase, it is important to understand the various data sources, the time boundaries of the data selected, and any other special cases that need to be considered. Some of the other key points to be kept in mind are:

  • Data Verification: Verifying the data required and the availability of the data sources from which this data needs to be fetched.
  • Data Profiling: Data profiling activity helps in procuring the data, uniquely identifying different data values, boundary value conditions, or any data problems in the early stages. Recognizing data problems at the beginning will considerably reduce the cost of fixing it later in the development cycle.

Testing Data Transformation

Testing within the data transformation phase is when business requirements get altered into transformation logic. Once the data is transformed, accurate testing needs to be performed to ensure that the underlying data complies with the exacted transformation logic. Essential areas of focus during this phase are:

  • Validating the Data Model: It includes verifying the data structure with business specs. This can be developed by comparing columns and their data models, with business specifications, and reporting column requirements ensuring data coverage at the source.
  • Reviewing the Data Dictionary: Testing metadata, which adds constraints like Nulls, Default Values, Primary Keys, Check Constraints, Referential Integrity, Surrogate keys, etc.
  • Validating the Source to Target Mapping: Guaranteeing traceability throughout will improve build quality aspects like consistency, accuracy, and reliability.

After choosing data from the sources, ETL procedures resolve problems in the data, convert data into a common model suitable for research and analysis, and write the data to stage and cleansing areas, then eventually to the target data warehouse.

Extract – Transform – Load (ETL) Testing: It is a process of how data is stored from the source system to the data warehouse. Data is obtained from an OLTP database, transformed to match the data warehouse schema, and stored into the data warehouse database. ETL testing makes sure that the data that has been stored from a source to the destination after business transformation is accurate. It also includes the verification of data at different middle stages that are being used among source and destination. The information loads can be one time, incremental, or in real-time. Fundamental areas that this phase should focus on are:

  • Test where business processes are transforming the data according to the expected rules and logic.
  • Verify that one-time transformation for historical initial loads is working.
  • Ensure that the detailed and aggregated data sets are created successfully.
  • Verify the transaction audit logs and time stampings that are recorded.
  • Test for no data loss or corruption of data during transformations.
  • Verify whether the transformations are completed within the expected timeframe.
  • Verify data loads based on time intervals.

Test Data Loading

In Data Loading, testing verifies whether the ETL team added the exact logic to arrange the data or not. We must also verify the data storage system for the following:

  • Parallel Execution and Precedence: Checking suitable parallel execution and priority during the ETL process is crucial as it may influence directly on performance and scalability of the system.
  • Performance and Scalability: Testing of fundamental and consequent loads with performance and scalability aspect ensures that the system is within adequate performance limits and can sustain further data growth.
  • Authorizing the Archival and Purge Policy: Ensures that the data history is based on business conditions.
  • Testing error logging, exception handling, and recovery from failure points.
  • Verifying whether there is no data loss or corruption throughout the loading process.
  • Checking all transformations throughout loading work as required.
  • Verifying the data sets in staging for storing destination work without data loss.
  • Testing the incremental data bundles with modified data.
  • Ensuring that the performance audit logs and time stampings are registered.
  • Verifying whether the loads are performed within the required timeframe.

Testing BI Report

Data reporting is a major component of any Business Intelligence project. Reports are the fundamental substance of BI tools wherein data is shown in different graphical ways for ease of understanding and to enable the users to examine and make effective business decisions. Reports
are developed on the Data Layer, and the data is visualized as tables, graphs, charts, etc. These reports can be exported to .pdf, .csv, or excel, or can be mailed. The reports can further be scheduled to run.

Front-end BI applications are usually used in web, mobile applications, and reports. They involve analysis and decision support tools, including online analytical processing (OLAP) report generators. These applications make it easy for end-users to build complex queries for requesting data from data warehouses without any complex programming skills. Consequently, the business can easily customize the report according to their needs.

Take a look at the below-given figure for a better understanding of the workflow.

 

BI Testing: A Use Case

Crafting an effective BI strategy for 8 Mn data sets of a leading water supply company

The client, a leading water supplier company in the United States, wanted to design a data warehouse solution with Business Intelligence and wanted to generate reports to visualize and monitor the information of consumers and also be able to view their average consumption and
monthly revenue. They also wanted to track the customer service calls handled by the CSRs and view the summary of calls by call type. The product engineering team finalized the technical requirement, and the QA engineering team designed a test strategy to build a quality product
for the client.

The Plan: A data warehouse test strategy was used to understand the end-to-end testing approach of the software development cycle. In order to implement the best approaches, the team decided to perform the functional testing and performance manually and automate the testing with OWASP ZAP for security, to cope up with the stringent timelines. Data warehouse testing or BI infrastructure elements were tested for scalability, security, reliability, and performance. Data warehouse testing ensures the quality of data used for reporting and decision making. Therefore, a well-planned testing strategy that helps all the teams and the phases within the workflow of the data warehouse was developed.

The Execution: The stepping stone of an effective test cycle is that it meets the requirements of test planning in every stage whenever the data moves from one environment to another, and states the responsibilities of every stakeholder like the business analysts, infra team, QA
team, DBA’s, developers, and business users. The testing process was segregated into different phases, like Data Acquisition, Data Integration, Data Storage, and Data Presentation. To ensure the testing readiness from all aspects, the key areas that the strategy specialized in are:

  • Describing the testing techniques and kinds to be used.
  • Testing the created environment.
  • Testing Data Availability: It’s recommended to own production-like data covering all/ critical business scenarios.
  • Verifying data quality and functional, performance, and security acceptance criteria.

The manual approach for BI Data validation testing used to be as follows:

  • Manually checking and ensuring that the data from source to the target system is working correctly as designed.
  • Cross-checking that the data mapping and workflow parameters are configured accurately to the Power BI reports for more integrity on the system.
  • Creating a test matrix to analyze the data manually so that validation on the data will be easier.
  • Reconciling the data visually from the reports of Power BI along with comparing the data in the report and then manually checking in the database tables.

The Result: This strategy and its execution enabled the development team to successfully develop and implement the specific requirements of the client with EBS, MySQL, Apache Nifi, and Power BI as a data visualization tool. In testing, the concerns of the client in terms of the data validation, data integrity, and performance of each report and security of the data were solved.

With the successful implementation of the project on production, the client was able to accommodate 4.7 million of data sets to the Monthly Revenue Report, 74.56K of data sets to the Dunning Information History, 83.09K of data sets to the Customer Service Call Report, 71.21K data sets to the Consumption History and 3.3 million (last 30 days) data sets to the Average Consumption for fixed-point customers and customer spikes.

A Handbook for Building an Efficient </br> Business Intelligence Testing Strategy

The Future of Business Intelligence

The future of BI testing is intertwined with that of the field of Business Intelligence as a whole. So, what does the future hold for Business Intelligence? It is undeniable that the Business Intelligence software has become a necessity for modern companies. In the future, one could expect an array of features that focus on streamlined workflows, ease of use, and predictive abilities. Some of the expected trends of BI for the future are:

Self Service BI: Reports suggest that 2020 is the year of Self-Service BI. Gone are the days when one thought of BI as a central data warehouse that only a few IT experts could use. Self-Service BI will enable business users to become self-sufficient and break the dependency on data teams for access to data. Self-Service BI would provide each role with the ability to conduct the assigned applicable tasks.

Automation and Augmented Analytics: Datasets have grown to become so huge that one cannot rely on traditional BI solutions to handle them. A report by Gartner stated that “Augmented Analytics is the Future of Data and Analytics.” Augmented Analytics is the combination of several automated steps intended to produce actionable and clear answers. Augmented Analytics would be the right strategy for businesses looking to leverage data faster than their competitors.

Business-Intelligence-as-a-Service: It would be no surprise if businesses start looking out for options like business-intelligence-as-a-service to simplify the process of accessing data and driving insights from it. With this, businesses would enjoy all the benefits of an end-to-end BI solution with the ease of a cloud deployment. Like the other similar models, this would enable the IT staff to perform other tasks rather than carrying out time-consuming analysis. Organizations will be able to overcome bottlenecks that arise due to their limited number of data scientists, and normal users would be able to create dashboards and reports on their own.

Data Governance: In the future, organizations are expected to put in more effort in trying to know the place from which the data is coming from, the people who have access to it, and how it is being put into use. With the number and complexity of data sources required to support analytics initiatives increasing considerably, organizations of all sizes will start prioritizing data governance. With data governance, organizations would be able to understand the information requirements of the enterprise and improve data quality while ensuring privacy and confidentiality at the same time.

Conclusion

Business Intelligence testing can enable businesses to make better and informed decisions by comparing present and historical data within their business. The growth of technology has plunged Business Intelligence into the heart of business data and analytics. It has become compulsory to make quick and efficient decisions with fewer errors and reduced risks.

This competitive growth needs a standard Business Intelligence testing service that can handle the higher velocity, volume, and complexity of new product releases while improving the software application or product quality. Business Intelligence (BI) systems need a continuous and comprehensive framework to test and certify these systems. Testing is a significant stage that helps understand the origin and quality of
data. In addition to BI, Big Data is also rising. Knowledge and experience with BI testing are useful in a Big Data context. BI testing plays a major role in the Data Validation and integrity of data that is shown in the BI report since businesses can blend the insights from a traditional method to a wider BI strategy. Hence BI testing plays a major role in improving business as a whole in the modern era.

Graceson Alias
Senior Software Test Engineer
RapidValue

If you’d like to know more about implementing QA Automation in DevOps, please reach out to us at contactus@rapidvaluesolutions.com

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