Leveraging our Technology services expertise to create a Data & Analytics strategy framework and define a roadmap to attain the ideal future state

Leveraging our Technology services expertise in understanding the current state and performing a wide range of gap-analysis to provide industry-standard future state definitions and recommendations.


Better tracking capability of business strategy

Involvement of roles to achieve business goals

“D Cube Analytics helped us ideate and strategize our strategy framework and define a future roadmap.

We are now able to map and track our business goals efficiently and have been able to involve and engage our team in a better way”


The client wanted to assess its current Data & Analytics strategy, benchmark it against best-in-class industry standards, and identify the gaps to streamline various processes helping them to attain the future state for a competitive edge. Also provided with prioritization grid with a sphere of influence to focus on low effort & high impact tasks and future next steps for data centralization, data assessment, and quality control.


Designed a Data Strategy framework to do an organization-wide assessment. Using various themes to capture client sentiments concerning system, processes, governance, and technology to validate and identify data sources to bridge existing gaps in technology and provide recommendations for improvement.


  1. Conducted interviews using a team-specific questionnaire, with themes – Data Strategy & Governance, Training, Technology, Process Maturity, Organizational Structure
  2. Based on our research and discussion with inhouse experts, identified key pillars for analysis and designed a data strategy framework to focus on interviews
  3. Developed insights for the themes to benchmark against the Industry maturity curve and to compare the client’s current state with ideal and competitive state
  4. Developed future state definition along with the recommendation to reach that state and provided recommended plan of action to deliver effective Advanced Analytics Solutions
  5. Identified stakeholders to interview and consulted with them during working sessions to understand challenges. Summarized & analyzed individual responses to extract common themes of pain points across domains
  6. Benchmarked current client process against similar processes in the industry and made recommendations to reach the desired future state, overcoming the current hurdles


  1. Short delivery cycles with incremental delivery to key stakeholders
  2. Align on data and KPIs before implementing data pipelines and applications via MVPs
  3. Performance Engineering initiatives to look at data partitions, skewness, and query patterns to improve query performance
  4. Data fabric is not at industry-standard raw / transform / consolidated/publish to ensure data isolation and easy issue triage
  5. AWS Well architect standards should be used for disaster recovery, high availability as storing in the same cluster forces losing data along with the main cluster

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