Data Mining from Big Data 4V-s

Volume - the simpliest
1. High dimensions
2. Large number of records
3. New sources
Velocity harder
1. Interaction with a customer
  1. Capture data, learn and act
    1. Enhance customer's journey
      1. Iteratively improve user expericence
Variety - the hardest
1. Number of data owners exploded
Value
1. KYC (know your customer)
  1. Difficult for internet companies - never sees customers
2. Being able to exploit all the data available
  1. Age of analythics
    1. Access to Data
      1. SQL
        Look-up a few records
        Populate standard report
      2. OLAP, mining
        Create new report
      3. Data Mining
        Locate a problem
        Optimize business process
        Answer a tough question
        Understand something new
        Finding interesting structure in data
        Interesting patterns
        Segmentation, data clustering
        Predictive models
        Classification, regression
        Hidden relations
        Affinity (summarization) - relation between fields, associations
        Work for Data scientist
        Understands business needs
        Able to close those gaps
        Algorithms
        Knows more about statistics than programmer
        Data logic
        Knows more about programming, than statistician
        Technologies
        Summarization
        Variable corellation
        Frequent itemsets
        Association rules
        Clustering
        Distance
        Partition
        Sequence analysis
        Classification / prediction
        Decision trees
        Neural networks
        Bayers nets
        Regression
        Support vector machines
      4. But
        End user is not a statistician
        Lack data warehousing expertise
        IT focus is to keep running
        Building data warehouse is too expensive
  2. Proliferating analytics throughout the organization
    1. Make every part of business smarter
    2. Embedding analytics into every area
      1. Significant business value
  3. Acquire and enhance actions
    1. Marketing and sales
      1. Identify potential customer
      2. Establish campaign effectiveness
    2. Manufacturing process
      1. Causes of manufacturing problems
    3. Customer behaviour
      1. Affinities, propensities
    4. Fraudulent transaction detection
    5. Loan approval
      1. Establish credit worthiness of customer
    6. Web analytics and metrics
      1. Model user preferences
      2. Recommendation, targeting

Siguiente

Data Mining from Big Data 4V-s

Descripción

Resumen del Recurso

Similar

	Creado por Prohor Leykin hace casi 7 años