BI consulting and engineering for a commercial bank

BI consulting and engineering for a commercial bank

After a thorough analysis of the existing data architecture and stakeholders’ requirements, we prepared a detailed strategy and a roadmap for implementing a suitable banking BI solution.

Table of contents



The customer is a privately held Canadian bank that focuses on reverse mortgages and financial solutions for retired homeowners. The bank has a direct-to-consumer distribution model and a network of referral partners, including other banks, mortgage brokers, financial firms, and credit unions.


To analyze their performance and evaluate the effectiveness of their products, the customer was looking for a consultant on business intelligence in banking to consolidate and analyze data, generate daily, weekly, and monthly reports, and make data-driven decisions.

From the technological standpoint, the data integration processes in place were complicated while the database architecture was heavy, resource-consuming, and hardly extensible. Collected from disparate sources, data was inconsistent and required cleansing. That’s why, to maintain data integrity and accuracy, the company had assigned a dedicated employee to process data manually on a regular basis.

However, with no sufficient skills in database administration and data management, the admin still had to send frequent requests to the IT department specialists, who couldn’t respond quickly due to their heavy workload.

With the current data management process, it was impossible to move on to more sophisticated data analytics and implement a complex BI system. In addition, the customer couldn’t decide which platform, Power BI or Tableau, would suit their needs better.

To solve the above-mentioned challenges and develop a BI solution for non-tech users, the customer approached Itransition for BI consulting. We were to perform a high-level analysis of the company’s existing data architecture and reporting capabilities, develop a BI strategy, consult the bank on choosing the right BI suite, and engineer the data architecture along with ETL processes.


Experienced in business intelligence in banking, Itransition’s team started with assessing the existing environment, workflows and business processes. As a result, we prepared a detailed analysis of the data architecture, its pain points, and issues with data inaccuracy in the customer’s Excel files. Itransition business analyst defined required user roles, interviewed stakeholders, and gathered requirements from mortgage operations and other departments.

At the next stage, our team consolidated and structured all the obtained information from the target user groups, as well as defined the required KPIs and metrics. Based on the results, the team developed a roadmap and recommendations on how to enhance the existing data architecture, implement a BI solution, and satisfy the bank’ reporting needs.

In order to build an intuitive data model and automate data integration, cleansing, and synchronization, our team completed the following tasks:

  • We performed data modeling and designed scalable data stores
  • We redeveloped ETL operations
  • We engineered a data warehouse with the data structure understandable for all users

Analysis of the BI Suites

To choose a suitable BI suite, we prepared a detailed comparative analysis of Power BI and Tableau for data science, including their price options, convenience, and learning curve.

To demonstrate their differences and validate the chosen BI roadmap, our team built OLAP cubes and configured test reports based on real data in both tools.


The key deliverable of the Itransition’s work was an enhanced, transparent BI ecosystem that included:

  • SQL Server Integration Services (SSIS): a component for data integration, including ETL packages
  • SQL Server Analysis Services (SSAS): a tool for OLAP cube development
  • Power BI and Tableau tools for building BI reports
Development process


Itransition’s team assessed the customer’s existing data environment, gathered user requirements, and prepared a detailed strategy on how to improve the existing data architecture.

  • We developed an ETL system to improve the automated integration of data collected from disparate sources.
  • We engineered the data architecture that is resource-effective and ensures easy administration, maintenance, and adoption of the bank’s BI solution to be deployed.
  • We also conducted a detailed comparison of Power BI and Tableau platforms, mapped the obtained results to the customer’s needs, and developed a pilot report, thus laying the foundation for further implementation of business intelligence in banking.

Altogether, our detailed and well-grounded analysis helped the customer understand which BI solution is optimal for them to eliminate existing bottlenecks and provide employees with an intuitive toolkit to create BI reports.