1. Agenda 02/21/2013 Discuss exercise Answer questions in task #1 Put up your sample databases for tasks #2 and #3 Define ETL in more depth by the activities performed. Discuss the “controversy” in ETL activities

2. Discussed in prior classes... Lots of data. Traditional transaction processing systems Non-traditional transaction processing Call center; Click-stream; Loyalty card; Warranty cards/product registration information External data from government and commercial entities. Lots of poor quality data for lots of reasons that can be traced back to lots of people.

3. Master Data Management: What does it mean and why is it so difficult to manage master data?

4. 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.

5. 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 Information Builders (DataMigrator), Harte-Hanks (Trillium), CloverETL, Talend, and BusinessObjects (Centric)

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

7. 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.

8. Parsing

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

10. Correcting

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

12. Standardizing

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

14. Matching

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

16. Consolidating

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

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

19. Consolidating whole groups WilliamParkerBethLewis KarenParker-LewisWilliam Parker-Lewis, Jr.

20. ETL Products SQL Server 2012 Integration Services from Microsoft Power Mart/Power Center 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

21. What about unstructured data? What is unstructured data? What percentage of data in organizations is considered to be “unstructured”? Examples Why store it in a data warehouse? Does it do any good in large text fields? Special ETL for unstructured data

22. Unstructured Data Example Notes about post-service of a product: The hub bent when the bicycle hit a large pothole. The plane takes off sluggishly during high-altitude departures. The product won’t allow entry of a 1098-T when the person is declared as a dependent. “Text analytics” are used to transform the data.

23. Text analytics Parses text and extracts facts (complaints, problems, issues) about key entities (customers, products, locations). Uses natural language processes (NLP). NLP converts human language into more formal representations that are easier for a computer program to manipulate. Combination of computational linguistics and artificial intelligence.

24. Goal of ETL Structured and unstructured data stored in a relational database. Data is complete, accurate, consistent, and in conformance with the business rules of the organization.

25. Controversy in ETL Is it 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???