Data Quality Class 4

Goals Discuss Project Midterm Statistical Process Control Data Quality Rules

Project Informtion is now on web site Final version is due on July 26 Data will be available by end of the week We will spend some time discussing goals today

Midterm Written exam on July 5 th Will cover: –Cost of low data quality –Dimensions of data quality –domains and mappings –SPC –Data Quality Rules

Statistical Process Control Developed by Shewhart at Bell Labs in the 1920’s through 1950’s Notions of Variation vs. Control Important in original context of both equpiment manufacture and service quality

Variation Natural variations Defects Errors Mistakes Some variations are meaningful, some are not

Causes of Variation Common, or Chance causes –minor fluctuations or differences –not necessarily important to correct –observed to form a normal distribution Assignable, or Special causes –(self explanatory) We expect to see the normal variations, but assignable cause variations are interesting

Example Measure railroad on-time performance –Trains are typically on time or a few minutes late –One night, the trains are all 1 hour late due to electrical problems – a special cause

Statistical Control State in which variations observed can be attributed to common causes that do not change with time

Pareto Principle In a population that contributes to a common effect, relaively few of the contributors account for the bulk of the effect Example: code performance analysis Can be used to direct analysis

Control Chart

Control Chart 2 Used to look for distinct variations from the mean Goal: predictable behavior Plot series of data over time Variations are represented as distance from the mean

Control Chart 3 Center Line: can be computed as mean of variable points Upper Contril Limit: three standard deviations above center line Lower Control Limit: three standard deviations below center line

Control Chart 4 As long as all points are between UCL and LCL, the variations are due to common causes, and the process is said to be in control, or stable Points above UCL or below LCL are indicative of abnormal variation, and are due to special causes – the process is not in control

Control Chart 5 Select variables chart or attributes chart Use data quality dimensions as guideline Select meaningful variables to measure (i.e., stuff that will point at a diagnosible problem)

Interpreting the Control Chart Lack of stability indicates potential problem Look for: –points utside of control limits –zone testing (clusters of points within certain standard deviation limits) –potential to split out data points into different logical data sets Look for cycles

SPC and Data Quality “The Information Factory” Use data quality dimensions as guideline for investigation Analyze the state of data as it passes through the information chain Probing can be automated with data quality rules

Inserting the Probes FInd a location in information chain that is: –nondisruptive –easy to access –easy to retool

Data Quality Rules Definitions Proscriptive Assertions Prescriptive Assertions Conditional Assertions Operational Assertions

Definitions Nulls Domains Mappings

Proscriptive Assertions Describe what is not allowed Used to figure out what is wrong with data Used for validation

Prescriptive Assertions Describe what is supposed to happen with data Can be used for data population, extraction, transformation Can also be used for validation

Conditional Assertions Define an assertion that must be true if a condition is true

Operational Assertions Define an action that must be taken if a condition is true

9 Classes of Rules 1) Null value rules 2) Value rules 3) Domain membership rules 4) Domain Mappings 5) Relation rules 6) Table, Cross-table, and Cross-message assertions 7) In-Process directives 8) Operational Directives 9) Other rules

Null Value Rules Null value specification –Define GETDATE for unavailable as “fill in date” Null values allowed –Attribute A allowed nulls {GETDATE, U, X} Null values not allowed –Attribute B nulls not allowed

Value Rules Value restriction rule Restrict GRADE: value >= ‘A’ AND value <= ‘F’ AND value != ‘E’

Domain Rules Domain Definition Domain Membership Domain Nonmembership Domain Assignment

Mapping Rules Mapping definition Mapping membership Mapping nonmembership

Relation Rules Completeness Exemption Consistency Derivation

Completeness Defines when a record is complete (I.e., what fields must be present) IF (Orders.Total > 0.0), Complete With {Orders.Billing_Street, Orders.Billing_City, Orders.Billing_State, Orders.Billing_ZIP}

Exemption Defines which fields may be missing IF (Orders.Item_Class != “CLOTHING”) Exempt {Orders.Color, Orders.Size }

Consistency Define a relationship between attributes based on field content –IF (Employees.title == “Staff Member”) Then (Employees.Salary >= AND Employees.Salary < 30000)

Derivation Prescriptive form of consistency rule Details how one attribute’s value is determined based on other attributes IF (Orders.NumberOrdered > 0) Then { Orders.Total = (Orders.NumberOrdered * Orders.Price) * 1.05 }

Table and Cross-Table Rules Functional Dependence Primary Key Assertion Foreign Key Assertion (=referential integrity)

Functional Dependence Functional Dependence between columns X and Y: –For any two records R1 and R2 in a table, if field X of record R1 contains value x and field X of record R2 contains the same value x, then if field Y of record R1 contains the value y, then field Y of record R2 must contain the value y. In other words, attribute Y is said to be determined by attribute X.

Primary Key Assertion A set of attributes defined as a primary key must uniquely identify a record Enforcement = testing for duplicates across defined key set

Foreign Key Assertion When the values in field f in table T is chosen from the key values in field g in table S, field S.g is said to be a foreign key for field T.f If f is a foreign key, the key must exist in table S, column g (=referential integrity)

In-process Directives Definition directives (labeling information chain members) Measurement directives Trigger directives

Operational Directives Transformation Update

Other Rules Approximate Searching rules Approximate Matching rules