Many companies have jumped on the Analytics bandwagon quite quickly without seriously understanding what constitutes a right investment, or what investment would provide what business value. The core purpose of analytics being “data driven-proactive decision making”, It is critical to understand Organizational imperatives “what Key decisions require what data and what analysis” to start with, and what “incremental business value” comes from such analytics and proactive-informed decision making. At Data-by –Choice, we strongly recommend starting off with a comprehensive due-diligence to work out a business case and Road-map for analytics investments.

 

  What We Deliver
  • Stake-holder approved analytics road-map
  • Prioritization (ranking) of initiatives based on relative business value and perceived business criticality
  • Business case with dcf.
  • Technology recommendation
  For Each Identified Initiative
  • Scalable-reusable algorithm – it solution to provide predictive / prescriptive analytics
  • Early warning systems / intelligent alerts
  • Data visualization models where appropriate.

Making a Business Case And Strategic Road-map

Big-data and applied analytics if planned and implemented right can potentially help automate majority of the decisions, free-up managerial bandwidth, substantially cut-down the cycle time, and remove subjectivity in decision-making at all levels therefore creating a competitive advantage for the organization hitherto unheard of.

Due-Diligence Methodology

Data-by-Choice recommends a comprehensive due-diligence to create a business case and a road-map before embarking on any strategic analytics investments.

Data-by-choice deploys a proprietary methodology for the due-diligence, broadly covering the following areas:

  Identifying and Prioritizing Key-Decisions Which Need To Be       Data-Driven
  • Comprehensive listing of all key decisions taken in the organization where right data can make a material impact on the revenue and/or customer experience.
  • The key decisions are further categorized and prioritized based on – size of the prize($), and business criticality, and the seniority of the decision makers.
  • Identifying 20% of the decisions which impact 80% of ($) revenue, and customer experience.
  Data Gap Analysis
  • For each of such decisions, further analysis on what data would support what decision, the sources of such data, current avlability and gap analysis
  • Where from and how to source the missing data
  Analytics
  • What kind of analytics, early-warning-systems, automation and alerts can make a significant difference
  • What kind of visualization can make managers comprehend the analysis better
  Process
  • Data collections through web-based survey followed by global due-diligence – essentially though focus groups, workshops and key-person interviews.
  • A cross-functional team consisting of data scientists, domain specialists, and it specialists to be deployed.
  • Develop analytics, and dss for 20% of business decisions that provide 80% business value.
  Bench-marking With Industry Best-Practices and Competition
  • Data governance practices prevailing currently
  • Data standards and definitions
  • Document and content life-cycle management
  • Data archiving, retrieval, re-purposing practices
  • Data quality
  • Audit-trail for data and document authoring and approval
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