Case Studies


Big Russian bank

Big Russian bank

Adastra performs Data Warehouse Audit on a top 10 ranked Russian bank

Adastra carried out a complete Data Warehouse Audit for a bank ranked amongst the top ten most reliable banks in the Russian Federation (Forbes, 2015). All aspects of the Data Warehouse (DWH) were audited, ranging from business areas via architecture and processes to infrastructure and technological questions. Results of the project, presented to the bank's management and board, are inputs for strategic planning in the DWH/BI area. Strategies for Data Governance were an essential part of our delivery.

The problem we had to face

Long-term data warehouses operating in a dynamic environment can become rigid and unable to fulfill user needs fast enough due to changes within corporate management or at board level, or a merger with another entity, as well as changes in the market.

Enterprises also lose track of their data over time when data ownership is infrequently clearly set, when data quality is uncertain and ways of improving them are vague. There are times when organizations do not even know which data are available to users or where the data are ultimately accessible.

The Russian bank was in such a situation and requested an independent Data Warehouse Audit and Data Governance program.

Does your DWH fulfill the needs of its business users? Maybe it's time for a Data Warehouse Audit.

Our suggested solution

Adastra provided a complete Data Warehouse Audit for the bank including an audit of DWH content, architecture and usage. We also suggested methods to solve identified deficiencies. Each audited area was expertly evaluated in terms of the development of customer solution awareness in a particular region when compared with the ideal situation and with the state of the common market. Problems facing the existing solution were identified along with the means to eliminate them. The audit took the following guidelines into consideration:

  • Business needs – how DWH incorporates the bank’s business needs, the ability of DWH to deliver data in the structure required, the accuracy and applicability of the delivered data, the perceived quality and the reliability of the services provided
  • Architecture – the compatibility of the DWH architectural solution, its internal structures and data flows, adaptability to changing requirements, data model flexibility and performance optimization
  • Infrastructure – the hardware and network infrastructure used in the DHW, integration with surrounding systems, identification of “bottle necks” and optimizing the design of the infrastructure used
  • Data Quality – the overall quality level of processed and delivered data, the method of monitoring these data, the measurement and correction of identified mistakes and the evaluation of this process
  • Data Governance – the existence and effectiveness of data definition processes, their importance in business and related rules, a data dictionary, the existence of roles and responsibilities for the data and their quality, data understanding and its uniform interpretation  
2

parts of the project: firstly, we identified problems and designed solutions, then a roadmap to reach the proposed first phase was formulated.

What was the outcome of the project

Within a few weeks crucial shortcomings and problems relating to the existing situation were identified in the Data Warehouse Audit. The next phase was substantially longer and focused on confirming and developing all findings while designing the plan for future developments to eliminate the deficiencies. The overall project took roughly five months.

Processes within the scope of the project were mapped and new structures were designed in accordance with the level of each employee's res­ponsibilities. We also defined the role of the BICC (Business Intelligence Competency Center) and Data Governance processes. Moreover, we delivered a detailed architecture of the new solution incl. the technical infrastructure, which effectively eliminated: problems related to the duplicate processing of data; the existence of several “versions of the truth” rather than a “single version of the truth”; and any inconsistent data in separate parts of the data warehouse. Additionally, we designed a proposal overseeing how to extend business areas and IT systems covered by the data warehouse, and provided detailed reports and outputs.

All realms of the design incorporate recommendations associated with the central challenges of the existing solution (including risks and limitations arising from it), and the proposed final state which eliminates those shortcomings. Special consideration was paid to the design of a gradual steps realization resulting in a target state reflecting all technological, financial and business priorities, and limitations. The bank accepted our proposal and began a gradual implementation.

5

The number of months the Data Warehouse Audit took in this large Russian bank.

Who participated in the project

Our excellent reputation is built by competent people who bring our projects to a successful end. In this case, the client relied on these consultants.

Jan Fiala

Consultant

Jan Fiala

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