Big Data Assessment and Strategy at a Tier 1 Bank

Our Work

The client embarked on a strategic initiative of developing a robust Big Data Strategy that would provide a implementation plan and how it can successfully leverage data as a strategic asset (i.e. use data at an enterprise level in order to meet the strategic objectives of its many business units)

Business problem and key objectives

  • The client has a significant volume of data and it is growing at an exponential rate, however it is stored across a vast array of enterprise, departmental and personal computers. 
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The client wanted to make a critical decision on whether to move to the modern big data analytics platform immediately, in the near term or in the long term, and what are the advantages and disadvantages between remaining with the traditional data warehouse architecture, move to modern Hadoop based platform or go with a hybrid approach (staging/landing of data to be in the data lake and the organization / reporting to remain in the traditional data warehouse).

Approach & Outcome

  • This strategy was developed by understanding the current state of technology infrastructure, architecture, data operations, leveraging best practices and leading data concepts, and consulting stakeholders from across the organization. The project was conducted over three stages:
    1. Current State Assessment of Data Capabilities and Strategic Objectives 
    2. Development of Recommended options for Implementation with cost/benefit
    3. Updated implementation plan
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  • The engagement consisted of the following activities:
  • Assess current state architecture, technology infrastructure capabilities, data operations, data management capabilities, data analytics capabilities and current processes and operating model
  • Develop capability framework and assess against maturity model and leading practices
  • Formulate recommendations and options

Update the implementation plan

Impact & Benefit

The project helped further a multi-year journey to enable the client to harness the power of its data and make an informed decision on the path forward for its data capabilities (inclusive of architecture, platform, infrastructure, analytics insights).