Data Analytics Journey Roadmap for Banks & Credit Unions

Become a data-driven organization to make better decisions and relentlessly measure, monitor in a continuous and automated manner


  • Generate & automate reports from data that are in different silo systems.
  • Enable business analysts and other stakeholders find the needed data, perform necessary data scrubbing and create reports with minimal IT involvement.
  • Create 360° view of member & loan portfolio with master data management.
  • Gain insights into data by correlating with external data sources and find patterns.
  • Improve key business metrics – loan growth, member satisfaction, profitability, acquisition, and retention.
  • Enhance existing marketing program capabilities with advanced member segmentation to better match members with next best products.
  • Develop an analytics roadmap with agile methodologies to quickly see the benefits and provide feedback.


  • Member & loan data in multiple silos.
  • Broad requirements to satisfy different CxO needs such as loan growth, better reporting process, member satisfaction, engineer’s productivity, and improved marketing program capabilities.
  • Restricted access to data sources and formats due to vendor compliance policy and regulations.
  • Proposed roadmap should coexist with past investments in IT applications and technologies.
  • Educate key stakeholders on the data analytics journey roadmap and provide cost benefit analysis on infrastructure and resources.


  • Treselle Engineering team created roadmap for data analytics journey that included Infrastructure, Analytics Foundation, Descriptive Analytics, Diagnostic Analytics, and Predictive Analytics.
  • Emphasized the data analytics maturity that the client will achieve over time by following the roadmap.
  • Identified high level capabilities that can be accomplished during each phase of the analytics journey using agile methodologies.
  • Created logical and physical data lake architecture to visually represent the data pipeline process.
  • Identified necessary tools and technologies based on Hadoop & Spark ecosystem.
  • Applied necessary industry standards such as CUFX, HMDA codes, masking PII (Personally Identified Information), etc.
  • Prepared the cost structure for both infrastructure and human resources needed to accomplish client’s goals in each of the analytic phases.
  • Created list of business benefits that client will accomplish so business executives understand the value.
  • Ensured that this new data analytics roadmap complements client’s investment in existing applications such as Tableau, Informatica, and MSSQL.