1. Agenda 03/27/2014 Review first test. Discuss internal data project. Review characteristics of data quality. Types of data. Data quality. Data governance. Define ETL activities. Discuss database analyst/programmer responsibilities for data evaluation.

2. QuestionAnswerQuestionAnswerQuestionAnswerQuestionAnswer 1. B8. D15. A22. B 2. A9. C16. D23. D 3. C10. D17. D24. D 4. B11. B18. A25. A 5. A12. C19. C 6. C13. B20. A 7. E14. A21. C Answers to Multiple Choice Questions

3. Discussed in prior classes... Lots of data. Traditional transaction processing systems Non-traditional data Call center; Click-stream; Loyalty card; Warranty cards/product registration information, email, twitter, Facebook External data from government and commercial entities General classification of data Transaction data Referential data/master data Metadata

4. Data quality What is good quality data? Correct Accurate Consistent Complete Available Accessible Timely Relevant

5. How does data “go bad”? Does all “bad” data have to be fixed?

6. Data governance Policies, processes and procedures aimed at managing the data in an organization. Usually high-level cross-department committees that oversee data management across the organization. Responsible for defining what data is necessary to gather. Responsible for defining the source and store of data. Responsible for security policies, processes, procedures. Responsible for creating the policies, processes and procedures. Responsible for assigning blame. Responsible for enforcing policies.

7. Data quality in data warehouses Is it more important than data quality in source transaction and reference data? How is better quality data achieved? Automated ETL processes to populate the data warehouse Spot checking programmatically

8. Populating the data warehouse Extract Take data from source systems. May require middleware to gather all necessary data. Transformation Put data into consistent format and content. Validate data – check for accuracy, consistency using pre-defined and agreed-upon business rules. Convert data as necessary. Load Use a batch (bulk) update operation that keeps track of what is loaded, where, when and how. Keep a detailed load log to audit updates to the data warehouse.

9. Data Cleansing Source systems contain “dirty data” that must be cleansed ETL software contains rudimentary to very sophisticated data cleansing capabilities Industry-specific data cleansing software is often used. Important for performing name and address correction Leading data cleansing vendors include general hardware/software vendors such as IBM, Oracle, SAP, Microsoft and specialty vendors Informatica, Information Builders (DataMigrator), Harte-Hanks (Trillium), CloverETL, Talend, and BusinessObjects (SAP-AG)

10. Steps in data cleansing  Parsing  Correcting  Standardizing  Matching  Consolidating

11. Parsing Parsing locates and identifies individual data elements in the source files and then isolates these data elements in the target files. Examples include parsing the first, middle, and last name; street number and street name; and city and state.

12. Parsing

13. Correcting Corrects parsed individual data components using sophisticated data algorithms and secondary data sources.

14. Correcting

15. Standardizing Standardizing applies conversion routines to transform data into its preferred (and consistent) format using both standard and custom business rules.

16. Standardizing

17. Matching Searching and matching records within and across the parsed, corrected and standardized data based on predefined business rules to eliminate duplications.

18. Matching

19. Consolidating Analyzing and identifying relationships between matched records and consolidating/merging them into ONE representation.

20. Consolidating

21. Source system view – 3 clients Policy No. ME309451-2 Account# 1238891 Transaction B498/97

22. The reality – ONE client Account# 1238891 Policy No. ME309451-2 Transaction B498/97

23. Consolidating whole groups WilliamLewisBethParker KarenParker-LewisWilliam Parker-Lewis, Jr.

24. ETL Products SQL Server 2012 Integration Services from Microsoft Power Mart/Power Center/Power Exchange from Informatica Warehouse Builder from Oracle Teradata Warehouse Builder from Teradata DataMigrator from Information Builders SAS System from SAS Institute Connectivity Solutions from OpenText Ab Initio

25. ETL Goal: Data is complete, accurate, consistent, and in conformance with the business rules of the organization. Questions: Is ETL really necessary? Has the advent of big data changed our need for ETL? ETL vs. ELT Does the use of Hadoop eliminate the need for ETL software??? Does it matter if the data is stored in the “cloud”?