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DATA WAREHOUSING AND DATA MINING

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Data warehousing combines data from multiple sources into a single database to provide businesses with analytics results from data mining, OLAP, scorecarding and reporting. It extracts, transforms and loads data from operational data stores and data marts into a data warehouse and staging area to integrate and store large amounts of corporate data. Data mining analyzes large databases to extract previously unknown and potentially useful patterns and relationships to improve business processes.

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DATA WAREHOUSING AND DATA MINING PRESENTED BY :- ANIL SHARMA B-TECH(IT)MBA-A REG NO : 3470070100 PANKAJ JARIAL BTECH(IT)MBA-A REG NO : 3470070086
DATA WAREHOUSING Data warehousing is combining data from multiple sources into one comprehensive and easily manipulated database. The primary aim for data warehousing is to provide businesses with analytics results from data mining, OLAP, Scorecarding and reporting.
NEED FOR DATA WAREHOUSING Information is now considered as a key for all the works. Those who gather, analyze, understand, and act upon information are winners. Information have no limits, it is very hard to collect information from various sources, so we need an data warehouse from where we can get all the information.
TODAYS BUISNESS INFORMATION
Retrieving data Analyzing data Extracting data Loading data Transforming data Managing data DATA WAREHOUSING INCLUDES:-
DATA WAREHOUSE ARCHITECTURE Data warehousing is designed to provide an architecture that will make cooperate data accessible and useful to users. There is no right or wrong architecture. The worthiness of the architecture can be judge by its use, and concept behind it . Data Warehouses can be architected in many different ways, depending on the specific needs of a business. 
Typical Data Warehousing Environment
An operational data store (ODS) is basically a database that is used for being an temporary storage area for a datawarehouse. Its primary purpose is for handling data which are progressively in use. Operational data store contains data which are constantly updated through the course of the business operations.
ETL (Extract, Transform, Load) is used to copy data from:- ODS to data warehouse staging area. Data warehouse staging area to data warehouse . Data warehouse to data mart . ETL extracts data, transforms values of inconsistent data, cleanses "bad" data, filters data and loads data into a target database.  
The Data Warehouse Staging Area is temporary location where data from source systems is copied.  It increases the speed of data warehouse architecture. It is very essential since data is increasing day by day.
The purpose of the Data Warehouse is to integrate corporate data. The amount of data in the Data Warehouse is massive.  Data is stored at a very deep level of detail. This allows data to be grouped in unimaginable ways. Data Warehouses does not contain all the data in the organization ,It's purpose is to provide base that are needed by the organization for strategic and tactical decision making.  
ETL extract data from the Data Warehouse and send to one or more Data Marts for use of users. Data marts are represented as shortcut to a data warehouse ,to save time. It is just an partition of data present in data warehouse. Each Data Mart can contain different combinations of tables, columns and rows from the Enterprise Data Warehouse. 
REASONS FOR CREATING AN DATA MART Easy access to frequently needed data. Creates collective view by a group of users. Improves user response time. Ease of creation. Lower cost than implementing a full Data warehouse
DATA MINING The non-trivial extraction of implicit, previously unknown, and potentially useful information from large databases. – Extremely large datasets – Useful knowledge that can improve processes – Cannot be done manually
Where Has it Come From ?
Motivation Databases today are huge: – More than 1,000,000 entities/records/rows – From 10 to 10,000 fields/attributes/variables – Giga-bytes and tera-bytes Databases a growing at an unprecendented rate The corporate world is a cut-throat world – Decisions must be made rapidly – Decisions must be made with maximum knowledge
How does data mining work? Extract, transform, and load transaction data onto the data warehouse system. Store and manage the data in a multidimensional database system. Provide data access to business analysts and information technology professionals. Analyze the data by application software. Present the data in a useful format, such as a graph or table
DATA MINING MEASURES Accuracy Clarity Dirty Data Scalability Speed Validation
Typical Applications of Data Mining
ADVANTAGES OF DATA MINING Engineering and Technology Medical Science Business Combating Terrorism Games Research and Development
Engineering and Technology In Electrical Power Engineering - used for condition monitoring of high voltage electrical equipment - vibration monitoring and analysis of transformer on-load tap-changers Education - to concentrate their knowledge
Medical Science Data mining has been widely used in area of bioinformatics , genetics DNA sequences and variability in disease susceptibility which is very important to help improve the diagnosis, prevention and treatment of the diseases
BUSINESS In Customer Relationship Management applications It Translate data from customer to merchant Accurately Distribute Business Processes Powerful Tool For Marketing
Combating terrorism Concept used by Interpol against terrorists for searching their records by Multistate Anti-Terrorism Information Exchange In the Secure Flight program , Computer Assisted Passenger Pre screening System , Semantic Enhancement
Games for certain combinatorial games, also called table bases (e.g. for 3x3-chess) It includes extraction of human-usable strategies Berlekamp in dots-and-boxes and Joh Nunn in chess endgames are notable examples
Research And Development Helps to Develop the search algorithms It offers huge libraries of graphing and visualisation softwares The users can easily create the models optimally
List of the top eight data-mining software vendors in 2008 Angoss Software Infor CRM Epiphany Portrait Software SAS G-Stat SPSS ThinkAnalytics Unica Viscovery
THANK YOU

More Related Content

DATA WAREHOUSING AND DATA MINING

  • 1. DATA WAREHOUSING AND DATA MINING PRESENTED BY :- ANIL SHARMA B-TECH(IT)MBA-A REG NO : 3470070100 PANKAJ JARIAL BTECH(IT)MBA-A REG NO : 3470070086
  • 2. DATA WAREHOUSING Data warehousing is combining data from multiple sources into one comprehensive and easily manipulated database. The primary aim for data warehousing is to provide businesses with analytics results from data mining, OLAP, Scorecarding and reporting.
  • 3. NEED FOR DATA WAREHOUSING Information is now considered as a key for all the works. Those who gather, analyze, understand, and act upon information are winners. Information have no limits, it is very hard to collect information from various sources, so we need an data warehouse from where we can get all the information.
  • 5. Retrieving data Analyzing data Extracting data Loading data Transforming data Managing data DATA WAREHOUSING INCLUDES:-
  • 6. DATA WAREHOUSE ARCHITECTURE Data warehousing is designed to provide an architecture that will make cooperate data accessible and useful to users. There is no right or wrong architecture. The worthiness of the architecture can be judge by its use, and concept behind it . Data Warehouses can be architected in many different ways, depending on the specific needs of a business. 
  • 8. An operational data store (ODS) is basically a database that is used for being an temporary storage area for a datawarehouse. Its primary purpose is for handling data which are progressively in use. Operational data store contains data which are constantly updated through the course of the business operations.
  • 9. ETL (Extract, Transform, Load) is used to copy data from:- ODS to data warehouse staging area. Data warehouse staging area to data warehouse . Data warehouse to data mart . ETL extracts data, transforms values of inconsistent data, cleanses "bad" data, filters data and loads data into a target database.  
  • 10. The Data Warehouse Staging Area is temporary location where data from source systems is copied.  It increases the speed of data warehouse architecture. It is very essential since data is increasing day by day.
  • 11. The purpose of the Data Warehouse is to integrate corporate data. The amount of data in the Data Warehouse is massive.  Data is stored at a very deep level of detail. This allows data to be grouped in unimaginable ways. Data Warehouses does not contain all the data in the organization ,It's purpose is to provide base that are needed by the organization for strategic and tactical decision making.  
  • 12. ETL extract data from the Data Warehouse and send to one or more Data Marts for use of users. Data marts are represented as shortcut to a data warehouse ,to save time. It is just an partition of data present in data warehouse. Each Data Mart can contain different combinations of tables, columns and rows from the Enterprise Data Warehouse. 
  • 13. REASONS FOR CREATING AN DATA MART Easy access to frequently needed data. Creates collective view by a group of users. Improves user response time. Ease of creation. Lower cost than implementing a full Data warehouse
  • 14. DATA MINING The non-trivial extraction of implicit, previously unknown, and potentially useful information from large databases. – Extremely large datasets – Useful knowledge that can improve processes – Cannot be done manually
  • 15. Where Has it Come From ?
  • 16. Motivation Databases today are huge: – More than 1,000,000 entities/records/rows – From 10 to 10,000 fields/attributes/variables – Giga-bytes and tera-bytes Databases a growing at an unprecendented rate The corporate world is a cut-throat world – Decisions must be made rapidly – Decisions must be made with maximum knowledge
  • 17. How does data mining work? Extract, transform, and load transaction data onto the data warehouse system. Store and manage the data in a multidimensional database system. Provide data access to business analysts and information technology professionals. Analyze the data by application software. Present the data in a useful format, such as a graph or table
  • 18. DATA MINING MEASURES Accuracy Clarity Dirty Data Scalability Speed Validation
  • 19. Typical Applications of Data Mining
  • 20. ADVANTAGES OF DATA MINING Engineering and Technology Medical Science Business Combating Terrorism Games Research and Development
  • 21. Engineering and Technology In Electrical Power Engineering - used for condition monitoring of high voltage electrical equipment - vibration monitoring and analysis of transformer on-load tap-changers Education - to concentrate their knowledge
  • 22. Medical Science Data mining has been widely used in area of bioinformatics , genetics DNA sequences and variability in disease susceptibility which is very important to help improve the diagnosis, prevention and treatment of the diseases
  • 23. BUSINESS In Customer Relationship Management applications It Translate data from customer to merchant Accurately Distribute Business Processes Powerful Tool For Marketing
  • 24. Combating terrorism Concept used by Interpol against terrorists for searching their records by Multistate Anti-Terrorism Information Exchange In the Secure Flight program , Computer Assisted Passenger Pre screening System , Semantic Enhancement
  • 25. Games for certain combinatorial games, also called table bases (e.g. for 3x3-chess) It includes extraction of human-usable strategies Berlekamp in dots-and-boxes and Joh Nunn in chess endgames are notable examples
  • 26. Research And Development Helps to Develop the search algorithms It offers huge libraries of graphing and visualisation softwares The users can easily create the models optimally
  • 27. List of the top eight data-mining software vendors in 2008 Angoss Software Infor CRM Epiphany Portrait Software SAS G-Stat SPSS ThinkAnalytics Unica Viscovery