Analytics: A Data Driven World


A health plan company sought help developing a business intelligence and analytics roadmap for its transition to a governed data infrastructure. Our team worked to accomplish this and various related tasks by conducting BI/analytics information gathering sessions, making recommendations for data governance, organization structure, data architecture improvements, and more.



  • The health plan company needed an overhaul of present data architecture to accomodate various external data sources and restructure their data architecture to provide roboust scalable analytics with predictive and prescriptive capabilities.
  • The health plan company required a scalable, reliable and sustainable BI and analytics infrastructure to support the targeted growth, diversification, and operational excellence objectives established in its strategic plan.
  • The health plan company did not have the expertise inCternally to access their use of data for reporting analytics and if it was positioned for the future needs of business.


  • Our team conducted BI/analytics information gathering sessions with IT and business users to identify requirements and determine current state of data management.
    We identified findings related to expertise, data gathering methods, data architecture, process and methodology.
  • Built a roadmap to adapt analytics from various data sources with prescriptive and predictive capabilities.
  • We re-architectured their data and built data modeling to integrate BI/AI capabilities.
  • Built BI & AI capabilities and trained their IT team to on BI/AI tools.
  • Interviews, education sessions and training were completed to develop an improved appreciation of technical and business user needs, which will foster future collaboration and accountability between business and IT.

The Results

  • Our data modernization strategy was implemented immediately after business users were convinced about the huge ROI it provided to the company.
  • Our team’s talent recommendations were implemented to support the client’s data governance leadership and data steward business analytics roles and responsibilities.
  • The client’s enterprise data architecture is undergoing improvements (such as migrating it’s legacy data warehouse to an enterprise data warehouse).
  • A new enterprise data model and various document data flows for analytic functions are set to be completed as part of the client’s analytics roadmap.
  • Our team developed an analytics request process to standardize the intake (candidacy), prioritization, development, and escalation process for accessing data


  • Our data security strategy and implementation increased the security of the data by 19% and decreased threat factor by 41%.
  • Company was able to generate more analytical reports. The number of reports increased by 63%
  • By implementing our data capture strategy, Company was able to increase their data foot print by 38%
  • Run time for reports were reduced by 90%. A report which took 45 secs to generate was reduced to 5 secs inspite of complex system integration.