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EFL Data Warehouse

Employing the Objectivity Consulting Auditing Framework.

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923 Case Study EFL Data Warehouse Due Diligence Navy 800X400
Europejski Fundusz Leasingowy


EFL S.A. (Credit Agricole group) began its operations in June 1991 as one of the first leasing companies in Poland. Today, the company provides a comprehensive set of financial solutions, including lease, loan, long-term hire, factoring, and more. They serve more than 320,000 customers and have financed nearly PLN 61 billion worth of investments. In 2019, EFL S.A. was given the title Premium Brand of 2019 and received both the Financial Brand of the Year 2019 award and the Quality International 2019 gold medal for service quality.

Industry Financial Services
Technology An analytical and workshop-based methodology

Key Achievements

  • A successful ‘Due Diligence’ audit that generated actionable recommendations
  • The implementation of the proposed architecture would reduce manual work, lower costs, and improve data quality
  • The new architecture would be cloud-ready and scalable

The challenge

Business Need

To support EFL’s expansion plans, their data warehouse solution needed to be improved in order to facilitate faster data loading, the optimisation of overnight processing, and the on-time delivery of good quality data. Similarly, their reporting architecture required a range of enhancements to meet the needs of their growing organisation.

Having successfully collaborated with Objectivity on previous consulting activities, EFL selected Objectivity to perform a ‘Due Diligence’ audit with two primary goals in mind. The first goal was to identify ‘quick wins’ that would stabilise their system and improve processing through the implementation of tactical level solutions. The second goal was to audit their existing data warehouse and build a prototype transition architecture that would identify the possible future direction for a new data warehouse solution. At this stage, EFL knew that the future solution should be scalable, should allow for the expansion of the warehouse with new data, and should support creating effective, interactive reports based on the existing warehouse.

The additional expectation was to propose solutions that would follow well-established market standards and be easily transported into the future solution when it came time for implementation.

The solution

Project Details

Objectivity performed a strategic data warehouse audit – ‘Due Diligence’ – using both an analytical and workshop-based methodology. The audit was delivered through onsite interviews and workshops with the business team and technical teams. The approach and methods which were used followed the Objectivity Consulting Auditing Framework.

The workshop and audit activities allowed the Consulting team to identify ‘quick wins’ and produce a tactical level solution proposal focused on system stability and process efficiency. The team identified 27 tactical recommendations with the potential to deliver gains in data load and data transformation speeds, increase the stability of the data warehouse, and simplify data warehouse administration.

Objectivity grouped the recommendations to facilitate the future delivery of the greatest business value, while first considering the total cost of its implementation:

  • Quick wins to achieve an immediate and significant gain on overnight performance and user queries performance.
  • Longer projects which should be started to improve security, stability, and the development process.
  • Best practices in maintenance tasks that provide better visibility of issues.
  • Other recommendations that have lower impact on performance but should be considered for the long-term.

The output from the audit was then used to prepare a transition architecture proposal for the data warehouse. The transition architecture was designed to be scalable and to support larger data sets, as well as to allow business users to leverage modern reporting tools, facilitating the early delivery of the top business critical reports.

Objectivity’s Consulting team presented the findings to EFL in the form of a comprehensive audit report. The final report documented the analysis and findings and presented a set of clearly defined and prioritised recommendations for the implementation of both the tactical level solutions and the strategic architecture transition. The recommendations were prioritised based on the best return on investment for EFL and included an estimation of effort and additional identified costs.

The results

Business Benefits

Having assessed the output of Objectivity’s ‘Due Diligence’ audit, EFL agreed that the proposed recommendations would be able to help them optimise their business. As such, they decided to consider implementing them in phases and validate the expected results starting from the subset of recommendations.

The company found that the implementation of the tactical level recommendations could provide them with gains in data load and data transformation speeds, increase the stability of the data warehouse, and simplify data warehouse administration.

The implementation of the proposed transition architecture would introduce business-oriented, well-designed and understandable Data Marts. This, in turn, would allow business users to leverage modern reporting tools, thus enabling EFL to replace their legacy reporting solution. The new architecture proposed by Objectivity would also be scalable, easy to maintain, reusable, and easily transferable to any data warehouse destination. The Consulting team’s proposal was also tailored to address data source changes and industry standards for data warehouses.

Once implemented, the proposed architecture would be able to deliver the following benefits to EFL:

  • Accelerated report preparation and issuance
  • Reduced manual work, leading to lower reporting costs
  • Improving data quality and consistency
  • Increased security
  • Verification of Logical Data Model (LDM) and assumptions
  • Scalability and cloud migration readiness
  • The possibility to replace the source system with relatively low rework effort
  • Mature and well-known technologies (SQL Server, Integration Services, Analysis Services, PowerBI)

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