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E-Data Jill Dych é

E-Data Jill Dych é. Turning Data into Information with Data Warehousing. What is a Data Warehouse?. A repository of subject-oriented, historical data A collection of smaller “data marts” A separate hardware platform A computer different from the other computers in your IT environment

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E-Data Jill Dych é

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  1. E-DataJill Dyché Turning Data into Information with Data Warehousing

  2. What is a Data Warehouse? • A repository of subject-oriented, historical data • A collection of smaller “data marts” • A separate hardware platform • A computer different from the other computers in your IT environment • A combination of the hardware, specialized software, and data

  3. What is a Data Warehouse? • Data on the data warehouse is used for decision making • Duplicates data that already exists elsewhere in the business

  4. The Value of a Data Warehouse • It makes new business knowledge available literally at the touch of a button • Provides information to customer service reps who can use it to tailor their responses to service requests and new orders.

  5. Why the “Rush” to Design a DW? • Mainframe offload • Company’s mainframe system was busy already • Dirty Data • Different data from all over the enterprise was hard to find and impossible to understand • Security • DW offers a more generally accessible environment for frequently requested information

  6. New Benefits • Enables companies to answer different questions with the same information. • Simpler implementation • Because DWs are “informational” and not “operational” they don’t require the security, design and technology restrictions as operational systems • Customer intimacy

  7. Data-Warehousing Objectives (p.14) • To provide a single view of our customers across the enterprise • To put as much business information as possible into the hands of as many different users as possible • To improve turnaround time for common reports • To monitor customer behavior • To predict purchases

  8. Data-Warehousing Objectives (p.14) • To perform statistical analysis on data from one location instead of many • To compensate the recent downsizing of our IT department • To improve responsiveness to business issues • To increase the accuracy of measurements • To improve productivity • To increase and distribute responsibilities

  9. Data Warehousing AphorismsRight or Wrong • If you build it, they will come • The hardest part about data warehousing is sourcing and loading the data • The most difficult part is getting everyone to agree on its objectives and defining the deliverables and implementation priorities correctly the first time out

  10. Data Warehousing AphorismsRight or Wrong • The data warehouse can be all things to all people • Only if you understand their requirements, can provide the appropriate data and can build a technology architecture to deliver • The data warehouse is the only remedy for being “data rich and information poor” • Data warehousing is not a technology, it’s a process

  11. Buzzwords and What They Mean • Database • A collection of related tables • Tables=collection of fields that describe a person, place object event, or idea • Field=a single characteristic or attribute of a person, place, object, event or idea • Record=set of field values • Database Management Systems • A software program that lets you create databases and then manipulate data in them

  12. Buzzwords and What They Mean • Relational Databases • See DB • Data mart • A subset of a data warehouse • Focused portion of the data warehouse for a specific functional area, department • Enterprise Data Warehouse • Larger corporate warehouse that feeds the data marts

  13. OLTP • Online transaction processing • Involves gathering input information, processing that information, and updating existing information to reflect the gathered and processes info. • Most organizations use databases and DBMS to support OLTP • OLAP • Online analytical processing • Manipulation of information to support decision making

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