DATA MINING , WAREHOUSING AND OLAP IN BUSINESS

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DATA MINING ,WAREHOUSING AND OLAP IN BUSINESS

• CS 425
• By
• SUGANYA RAVIKUMAR
• SID 999 29 0512

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INTRODUCTION

• Need for mining/warehousing/OLAP in Business due
  to the following reasons
• ?Data Explosion.
• ?Business re-engineering and organizational
  decentralization.
• ?Faster product cycles.
• ?Globalization and enterprise topologies.

 
Web-based information Resources
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Definitions

• Data mining
• decision support analysis process to find
  buried knowledge in corporate data and deliver
  understanding to business professionals.
• encompasses
• data warehousing
• database management
• data analysis algorithms
• visualization

• OLAP
• presents relational data to users to facilitate
  understanding of the data and important hidden
  patterns .
• transforms Data Warehouse data into strategic
  information.

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• Challenges in data mining
• - provide continuous rather than one-time
  value to e-commerce
• - scaling to very large data sets
• Solution
• Distributed and cooperative data-warehouse,
• OLAP and data mining infrastructure.
• Supportive Infrastructure
• Multiple Local Data-warehouse/OLAP
  Stations(LDOS)
• Global Data-warehouse/OLAP Station(GDOS).

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• LDOS
• serve as distributed data collection, aggregation
  and reduction stations.
• dynamically improve the parallelism of data
  mining to reduce the data load and computation
  load on the GDOS.
• GDOS
• integrates the summary information or partial
  knowledge fed from the LDOS
• generates more complete knowledge than any
  single LDOS

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SIMPLIFIED INTEGRATED DATA MINING ARCHITECTURE
INTEGRATED DATA MINING ARCHITECTURE IN DETAIL
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PLAYERS IN THE WEB-FARMING SYSTEM
WEB FARMING SYSTEM
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Mining and OLAP combined with warehousing
enhance Business IQ And enable better decisions
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OBTAINING USEFUL DATA

• Data warehouses in conjunction with OLAP
• and data mining drives decisions and improves
• business processes by
• identifying new clients,
• mapping market developments,
• calibrating customer loyalty,
• financial modeling (budgeting, planning)
• sales forecasting
• exception reporting
• resource allocation and capacity planning
• variance analysis
• promotion planning
• market share analysis

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Savvy corporations are beginning to use this
intelligence to develop marketing strategies,
target mailings, adjust inventories, minimize
risk and eliminate wasteful spending based on an
analysis of their data. They are increasing the
return of their investment on current resources
and improving their business advantage.
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CONCLUSION

• Data mining has evolved from manual statistical
  methods to desktop mining to enterprise mining.
  With appropriate skill sets, the right team, a
  warehousing infrastructure and data mining tools
  and OLAP, companies can transition into agile
  competitors who maneuver quickly with the global
  demands of the marketplace.