1. Data Quality Class 3

2. Goals Dimensions of Data Quality Enterprise Reference Data Data Parsing

3. Dimensions of Data Quality Poor data quality is similar to obscenity- – It seems as if there are no real ways to measure it, but you know it when you see it! In reality, data quality can be measured – The frame of reference for measurement is different

4. Dimensions of Data Quality 2 Data Models Data Values Data Presentation Data Policy

5. Example: Sales Database Sales and marketing database – Current customers – Sales leads Name, address, contact data For current customers, sales data

6. Data Quality of Data Models Clarity of definition Comprehensiveness Flexibility Robustness

7. Data Quality of Data Models 2 Essentialness Attribute granularity Precision of domains Homogeneity

8. Data Quality of Data Models 3 Naturalness Identifiability Obtainability Relevance

9. Data Quality of Data Models 4 Simplicity Semantic Consistency Structural Consistency

10. Data Quality of Data Values Accuracy Null values Completeness Consistency Currency

11. Accuracy Agreement with established sources Database of record Other corroborative sources

12. Null Values Null vs. Missing – Unavailable – Not applicable – No value – Not classified – Truly null

13. Completeness Mandatory attributes require values Optional attributes may hold values (when and how?) Inapplicable attributes may not have a value (also when and how?) Completeness constraints

14. Consistency Are values in one set consistent with values in another set? Consistency relations between attributes in the same table Consistency assertions across columns Consistency relationships between tables

15. Currency/Timeliness What data is current? How is it kept up-to-date? Time expectations for accessibility to data

16. Data Quality of Data Presentation Appropriateness Correct Interpretation Flexibility Format Precision

17. Data Quality of Data Presentation 2 Portability Representation Consistency Representation of Null Values

18. Data Quality of Data Policy Access Metadata Privacy Fault-tolerance Security

19. Reference Data Relatively static Referred to from within many tables Shared data Examples: – Product catalog – Security type classification – Currency codes

20. Reference Data 2 Metadata Data Domains Mappings between those domains

21. Domains A data domain is a subclassed data type Domains can be described using enumerations – Example: states, cities, product codes Domains can be described using functions – Example: formatted trouble ticket ids such as CC- NNNN

22. Domain Membership Data attributes can be affiliated with data domains Test a value to make sure it is valid within a domain – For enumerated domains, a lookup works – For generated/described domains, check to see if the value could be generated by function

23. Domains as Metadata The existence of a data domain is metadata Keep track of which tables have attributes making use of which domains Manage domains as enterprise reference data

24. Domain Tables Domains are sets of values Maintain in two tables: – Domain name table (domain_name, domain_id) – Domain value table (domain_id, value) Example: – (2, “STATES”) – {(2, “NY”), (2, “MA”), (2, “CT”), (2, “VA”)…}

25. Mappings Map values in one domain to values in other domains 1-1, 1-Many, Many-1, Many-Many Represents relations from one set to another set 1-1, Many-1 are functions

26. Mappings as Metadata Semi-static mappings are also metadata Keep track of which tables refer to mappings Enterprise reference data

27. Mapping Tables Maintain mapping information in two tables: – Mapping name table (from_domain, to_domain, mapping_id, mapping name) – Mapping table (mapping_id, source_value, target_value)

28. Example: (“Currency”, “Country”, 65, “CurrencyToCountry”) (65, “USD”, “US)

29. Tables Inference of data fields from source data In our case, this is straightforward based on SGML tags in the data Determine table structure in context of use of metadata and reference data In other words, try to maintain normal form

30. Keys Primary key: must be unique across all values in table Primary key may be assigned based on internal increasing value, or maybe extant in the data Foreign key: relates values in one table to values in another table Foreign keys must exist in target table (=referential integrity)

31. Data Profiling and Parsing Goals: – Identify data domains – Identify mappings – Identify candidate keys

32. Data Column Profiling For each table – For each column Type inference Subclassed Type inference Count the number of distinct values Enumerate the distinct values Sort the distinct values Look for patterns of usage Document discoveries

33. Data Table Profiling For each Table – Identify candidate keys – Identify data domains – Identify data mappings – Look for patterns

34. Multiple Table Profiling Identify foreign key relationships Validate referential integrity Look for patterns

35. Data Parsing 2 kinds of data domains – Enumerated, consisting of a predefined set of values – Inferred/Functional, consisting of values that are validated based on a set of rules

36. Type Inference Data value sets all conform to a data type All values within a column must belong to the same type Propose data type by a series of inferences – Initial assumption is that all values belong to strictest type – Test for violations to type restriction – As a violation is discovered, loosen the restriction and test again for violations – Measure conformance at each level

37. Subclassed Type Inference Once type has been inferred, look for additional restrictions within type Example: – Integer vs. Integer within a range (0..100) For integers and real values, look for ranges For character strings, look for length, context, character patterns

38. String Parsing Classify characters into groups – Alphabetic – Numeric/Digits – Punctuation (this can be further refined) Transform all values within a column set into their corresponding pattern Example: 789-23-1100 would change to – DDDPDDPDDDD

39. String Parsing Given a set of representative strings, perform column analysis again Look for high frequency counts for specific patterns This will provide proposed functional domains We can characterize certain domains by format (e.g., telephone numbers, SSNs, Tax Ids, UPC codes, etc.)

40. Word Parsing More advanced technique to explore domain types Classify word tokens in terms of predefined characterization – Example: Name words, business words, transaction words, connector words, titles, etc.

41. Primary Key Discovery Iterative process to find candidate keys A candidate key is one or more attributes whose values, when composed, can uniquely locate a record

42. Primary Key Discovery 2 For I = 1, number of attributes – For each set S composed of I attributes do Are the composed values unique across the tables? If so, add to candidate key set

43. Next Assignment Type Inferencer – Propose data types for analyzed columns Format Parser – Transform strings into pattern strings – Propose format for a column – Catalog discovered named formats (telno, SSN, etc.) – More details on posting on web site