Systematic Approach to Data Analytics

Who Should Attend?

Duration: 2 Days

Big data analytics is a long-term journey for organizations to achieve desired business objectives. Nowadays, enterprises are using data strategically to help them become more competitive, increase revenue and profits, reduce risk and begin new initiatives or even deliver exceptional products and services. Thus, advanced analytics enable us to become more adaptable to predicting the future.

  • Stimulate ideas and identify potential high-impact applications in their respective organizations
  • Answer the ‘when, and how to start building BDA application’ question
  1. BDA Overview
    • 3 Elements – Data Source, Advanced Analytics & Data Presentation
    • Involves Technological Framework for Enablement
    • Success Cases around the World
    • Modern Data Scientist skillsets
    • CRISP-DM
  2. Importance of Advanced Analytics vs Traditional BI
    • Huge amount of information captured in unstructured data – sentiments from social media is a good example
    • Predictive & Prescriptive Analytics – in addition to Descriptive Analytics
    • Diagnostics Techniques
  3. Important Considerations in Building BDA Applications
    • Business Aspects
    • Domain Knowledge
    • Data Understanding
    • Determine Measurable Output (ROI)
    • Technical Aspects
    • Key Competency Areas
    • ICT Infrastructure
    • Big Data Technologies
    • Hadoop
    • Advanced Analytics
    • Web/Social Media Mining
  4. Recommended Approach to Start a BDA Project
    • Identify Business Cases with Tangible ROI
    • Prioritize by Communicating with Different Departments
    • Feasibility Study
    • Pilot Projects
    • Build Competency
    • Plan for Future Implementation/Scaling
    • Raise Example Use Cases
Brainstorming and Facilitating the Development of Ideas
  • Highly interactive session
  • 4 to 5 people in a group, identify potential business cases based on domain knowledge
  • Simulate a project team environment
  • Guide participants through the step-by-step process via templates:
    • What are the theoretical benefits of implementing such initiative for the organization?
    • How critical is the identified issue?
    • What will happen if problem not solved?
    • What are the available data?
    • Understanding the data on-hand
    • Is there a need to collect more data? How easy/difficult is that?
    • Potential ROI
    • Start small with pilot projects

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