Data Quality and Data Cleaning

nullData Quality and Data Cleaning: An OverviewData Quality and Data Cleaning: An OverviewTheodore Johnson johnsont@research.att.com AT&T Labs – Research (Lecture notes for CS541, 02/12/2004)Based on:Based on:Recent book Exploratory Data Mining and Data Quality Dasu and Johnson (Wiley, 2004) SIGMOD 2003 tutorial. nullTutorial FocusTutorial FocusWhat research is relevant to Data Quality? DQ is pervasive and expensive. It is an important problem. But the problems are so messy and unstructured that research seems irrelevant. This tutorial will try to structure the problem to make research directions more clear. Overview Data quality process Where do problems come from How can they be resolved Disciplines Management Statistics Database MetadataOverviewOverviewThe meaning of data quality (1) The data quality continuum The meaning of data quality (2) Data quality metrics Technical tools Management Statistical Database Metadata Case Study Research directionsnullThe Meaning of Data Quality (1)Meaning of Data Quality (1)Meaning of Data Quality (1)Generally, you have a problem if the data doesn’t mean what you think it does, or should Data not up to spec : garbage in, glitches, etc. You don’t understand the spec : complexity, lack of metadata. Many sources and manifestations As we will see. Data quality problems are expensive and pervasive DQ problems cost hundreds of billion $$$ each year. Resolving data quality problems is often the biggest effort in a data mining study.ExampleExampleCan we interpret the data? What do the fields mean? What is the key? The measures? Data glitches Typos, multiple formats, missing / default values Metadata and domain expertise Field three is Revenue. In dollars or cents? Field seven is Usage. Is it censored? Field 4 is a censored flag. How to handle censored data? T.Das|97336o8327|24.95|Y|-|0.0|1000 Ted J.|973-360-8779|2000|N|M|NY|1000Data GlitchesData GlitchesSystemic changes to data which are external to the recorded process. Changes in data layout / data types Integer becomes string, fields swap positions, etc. Changes in scale / format Dollars vs. euros Temporary reversion to defaults Failure of a processing step Missing and default values Application programs do not handle NULL values well … Gaps in time series Especially when records represent incremental changes.Conventional Definition of Data QualityConventional Definition of Data QualityAccuracy The data was recorded correctly. Completeness All relevant data was recorded. Uniqueness Entities are recorded once. Timeliness The data is kept up to date. Special problems in federated data: time consistency. Consistency The data agrees with itself. Problems …Problems …Unmeasurable Accuracy and completeness are extremely difficult, perhaps impossible to measure. Context independent No accounting for what is important. E.g., if you are computing aggregates, you can tolerate a lot of inaccuracy. Incomplete What about interpretability, accessibility, metadata, analysis, etc. Vague The conventional definitions provide no guidance towards practical improvements of the data.Finding a modern definitionFinding a modern definitionWe need a definition of data quality which Reflects the use of the data Leads to improvements in processes Is measurable (we can define metrics) First, we need a better understanding of how and where data quality problems occur The data quality continuumnullThe Data Quality ContinuumThe Data Quality ContinuumThe Data Quality ContinuumData and information is not static, it flows in a data collection and usage process Data gathering Data delivery Data storage Data integration Data retrieval Data mining/analysisData GatheringData GatheringHow does the data enter the system? Sources of problems: Manual entry No uniform standards for content and formats Parallel data entry (duplicates) Approximations, surrogates – SW/HW constraints Measurement errors. SolutionsSolutionsPotential Solutions: Preemptive: Process architecture (build in integrity checks) Process management (reward accurate data entry, data sharing, data stewards) Retrospective: Cleaning focus (duplicate removal, merge/purge, name & address matching, field value standardization) Diagnostic focus (automated detection of glitches). Data DeliveryData DeliveryDestroying or mutilating information by inappropriate pre-processing Inappropriate aggregation Nulls converted to default values Loss of data: Buffer overflows Transmission problems No checksSolutionsSolutionsBuild reliable transmission protocols Use a relay server Verification Checksums, verification parser Do the uploaded files fit an expected pattern? Relationships Are there dependencies between data streams and processing steps Interface agreements Data quality commitment from the data stream supplier.Data StorageData StorageYou get a data set. What do you do with it? Problems in physical storage Can be an issue, but terabytes are cheap. Problems in logical storage (ER  relations) Poor metadata. Data feeds are often derived from application programs or legacy data sources. What does it mean? Inappropriate data models. Missing timestamps, incorrect normalization, etc. Ad-hoc modifications. Structure the data to fit the GUI. Hardware / software constraints. Data transmission via Excel spreadsheets, Y2K SolutionsSolutionsMetadata Document and publish data specifications. Planning Assume that everything bad will happen. Can be very difficult. Data exploration Use data browsing and data mining tools to examine the data. Does it meet the specifications you assumed? Has something changed?Data IntegrationData IntegrationCombine data sets (acquisitions, across departments). Common source of problems Heterogenous data : no common key, different field formats Approximate matching Different definitions What is a customer: an account, an individual, a family, … Time synchronization Does the data relate to the same time periods? Are the time windows compatible? Legacy data IMS, spreadsheets, ad-hoc structures Sociological factors Reluctance to share – loss of power.SolutionsSolutionsCommercial Tools Significant body of research in data integration Many tools for address matching, schema mapping are available. Data browsing and exploration Many hidden problems and meanings : must extract metadata. View before and after results : did the integration go the way you thought?Data RetrievalData RetrievalExported data sets are often a view of the actual data. Problems occur because: Source data not properly understood. Need for derived data not understood. Just plain mistakes. Inner join vs. outer join Understanding NULL values Computational constraints E.g., too expensive to give a full history, we’ll supply a snapshot. Incompatibility Ebcdic?Data Mining and AnalysisData Mining and AnalysisWhat are you doing with all this data anyway? Problems in the analysis. Scale and performance Confidence bounds? Black boxes and dart boards “fire your Statisticians” Attachment to models Insufficient domain expertise Casual empiricismSolutionsSolutionsData exploration Determine which models and techniques are appropriate, find data bugs, develop domain expertise. Continuous analysis Are the results stable? How do they change? Accountability Make the analysis part of the feedback loop. nullThe Meaning of Data Quality (2)Meaning of Data Quality (2)Meaning of Data Quality (2)There are many types of data, which have different uses and typical quality problems Federated data High dimensional data Descriptive data Longitudinal data Streaming data Web (scraped) data Numeric vs. categorical vs. text data Meaning of Data Quality (2)Meaning of Data Quality (2)There are many uses of data Operations Aggregate analysis Customer relations … Data Interpretation : the data is useless if we don’t know all of the rules behind the data. Data Suitability : Can you get the answer from the available data Use of proxy data Relevant data is missingData Quality ConstraintsData Quality ConstraintsMany data quality problems can be captured by static constraints based on the schema. Nulls not allowed, field domains, foreign key constraints, etc. Many others are due to problems in workflow, and can be captured by dynamic constraints E.g., orders above $200 are processed by Biller 2 The constraints follow an 80-20 rule A few constraints capture most cases, thousands of constraints to capture the last few cases. Constraints are measurable. Data Quality Metrics?nullData Quality MetricsData Quality MetricsData Quality MetricsWe want a measurable quantity Indicates what is wrong and how to improve Realize that DQ is a messy problem, no set of numbers will be perfect Types of metrics Static vs. dynamic constraints Operational vs. diagnostic Metrics should be directionally correct with an improvement in use of the data. A very large number metrics are possible Choose the most important ones.Examples of Data Quality MetricsExamples of Data Quality MetricsConformance to schema Evaluate constraints on a snapshot. Conformance to business rules Evaluate constraints on changes in the database. Accuracy Perform inventory (expensive), or use proxy (track complaints). Audit samples? Accessibility Interpretability Glitches in analysis Successful completion of end-to-end process Data Quality ProcessData Quality ProcessData GatheringData Loading (ETL)Data Scrub – data profiling, validate data constraintsData Integration – functional dependenciesDevelop Biz Rules and Metrics – interact with domain expertsValidate biz rulesStabilize Biz RulesVerify Biz RulesData Quality CheckRecommendations Quantify Results Summarize LearningnullTechnical ToolsTechnical ApproachesTechnical ApproachesWe need a multi-disciplinary approach to attack data quality problems No one approach solves all problem Process management Ensure proper procedures Statistics Focus on analysis: find and repair anomalies in data. Database Focus on relationships: ensure consistency. Metadata / domain expertise What does it mean? Interpretation Process ManagementProcess ManagementBusiness processes which encourage data quality. Assign dollars to quality problems Standardization of content and formats Enter data once, enter it correctly (incentives for sales, customer care) Automation Assign responsibility : data stewards End-to-end data audits and reviews Transitions between organizations. Data Monitoring Data Publishing Feedback loops Feedback LoopsFeedback LoopsData processing systems are often thought of as open-loop systems. Do your processing then throw the results over the fence. Computers don’t make mistakes, do they? Analogy to control systems : feedback loops. Monitor the system to detect difference between actual and intended Feedback loop to correct the behavior of earlier components Of course, data processing systems are much more complicated than linear control systems.ExampleExampleSales, provisioning, and billing for telecommunications service Many stages involving handoffs between organizations and databases Simplified picture Transition between organizational boundaries is a common cause of problems. Natural feedback loops Customer complains if the bill is to high Missing feedback loops No complaints if we undercharge.ExampleExampleCustomerSales OrderBillingCustomer Account InformationProvisioningCustomer CareExisting Data FlowMissing Data FlowMonitoringMonitoringUse data monitoring to add missing feedback loops. Methods: Data tracking / auditing Follow a sample of transactions through the workflow. Build secondary processing system to detect possible problems. Reconciliation of incrementally updated databases with original sources. Mandated consistency with a Database of Record (DBOR). Feedback loop sync-up Data PublishingData PublishingData PublishingMake the contents of a database available in a readily accessible and digestible way Web interface (universal client). Data Squashing : Publish aggregates, cubes, samples, parametric representations. Publish the metadata. Close feedback loops by getting a lot of people to look at the data. Surprisingly difficult sometimes. Organizational boundaries, loss of control interpreted as loss of power, desire to hide problems.Statistical ApproachesStatistical ApproachesNo explicit DQ methods Traditional statistical data collected from carefully designed experiments, often tied to analysis But, there are methods for finding anomalies and repairing data. Existing methods can be adapted for DQ purposes. Four broad categories can be adapted for DQ Missing, incomplete, ambiguous or damaged data e.g truncated, censored Suspicious or abnormal data e.g. outliers Testing for departure from models Goodness-of-fit Missing DataMissing DataMissing data - values, attributes, entire records, entire sections Missing values and defaults are indistinguishable Truncation/censoring - not aware, mechanisms not known Problem: Misleading results, bias. Detecting Missing DataDetecting Missing DataOvertly missing data Match data specifications against data - are all the attributes present? Scan individual records - are there gaps? Rough checks : number of files, file sizes, number of records, number of duplicates Compare estimates (averages, frequencies, medians) with “expected” values and bounds; check at various levels of granularity since aggregates can be misleading.Missing data detection (cont.)Missing data detection (cont.)Hidden damage to data Values are truncated or censored - check for spikes and dips in distributions and histograms Missing values and defaults are indistinguishable - too many missing values? metadata or domain expertise can help Errors of omission e.g. all calls from a particular area are missing - check if data are missing randomly or are localized in some way Imputing Values to Missing DataImputing Values to Missing DataIn federated data, between 30%-70% of the data points will have at least one missing attribute - data wastage if we ignore all records with a missing value Remaining data is seriously biased Lack of confidence in results Understanding pattern of missing data unearths data integrity issuesMissing Value Imputation - 1Missing Value Imputation - 1Standalone imputation Mean, median, other point estimates Assume: Distribution of the missing values is the same as the non-missing values. Does not take into account inter-relationships Introduces bias Convenient, easy to implementMissing Value Imputation - 2Missing Value Imputation - 2 Better imputation - use attribute relationships Assume : all prior attributes are populated That is, monotonicity in missing values. X1| X2| X3| X4| X5 1.0| 20| 3.5| 4| . 1.1| 18| 4.0| 2| . 1.9| 22| 2.2| .| . 0.9| 15| .| .| . Two techniques Regression (parametric), Propensity score (nonparametric)Missing Value Imputation –3 Missing Value Imputation –3 Regression method Use linear regression, sweep left-to-right X3=a+b*X2+c*X1; X4=d+e*X3+f*X2+g*X1, and so on X3 in the second equation is estimated from the first equation if it is missing Missing Value Imputation - 3Missing Value Imputation - 3Propensity Scores (nonparametric) Let Yj=1 if Xj is missing, 0 otherwise Estimate P(Yj =1) based on X1 through X(j-1) using logistic regression Group by propensity score P(Yj =1) Within each group, estimate missing Xjs from known Xjs using approximate Bayesian bootstrap. Repeat until all attributes are populated.Missing Value Imputation - 4Missing Value Imputation - 4Arbitrary missing pattern Markov Chain Monte Carlo (MCMC) Assume data is multivariate Normal, with parameter Q (1) Simulate missing X, given Q estimated from observed X ; (2) Re-compute Q using filled in X Repeat until stable. Expensive: Used most often to induce monotonicity Note that imputed values are useful in aggregates but can’t be trusted individuallyCensoring and TruncationCensoring and TruncationWell studied in Biostatistics, relevant to time dependent data e.g. duration Censored - Measurement is bounded but not precise e.g. Call duration > 20 are recorded as 20 Truncated - Data point dropped if it exceeds or falls below a certain bound e.g. customers with less than 2 minutes of calling per monthnullCensored time intervalsCensoring/Truncation (cont.)Censoring/Truncation (cont.)If censoring/truncation mechanism not known, analysis can be inaccurate and biased. But if you know the mechanism, you can mitigate the bias from the analysis. Metadata should record the existence as well as the nature of censoring/truncationnullSpikes usually indicate censored time intervals caused by resetting of timestamps to defaultsSuspicious DataSuspicious DataConsider the data points 3, 4, 7, 4, 8, 3, 9, 5, 7, 6, 92 “92” is suspicious - an outlier Outliers are potentially legitimate Often, they are data or model glitches Or, they could be a data miner’s dream, e.g. highly profitable customersOutliersOutliersOutlier – “departure from the expected” Types of outliers – defining “expected” Many approaches Error bounds, tolerance limits – control charts Model based – regression depth, analysis of residuals Geometric Distributional Time Series outliersControl ChartsControl ChartsQuality control of production lots Typically univariate: X-Bar, R, CUSUM Distributional assumptions for charts not based on means e.g. R–charts Main steps (based on statistical inference) Define “expected” and “departure” e.g. Mean and standard error based on sampling distribution of sample mean (aggregate); Compute aggregate each sample Plot aggregates vs expected and error bounds “Out of Control” if aggregates fall outside boundsAn Example (http://www.itl.nist.gov/div898/handbook/mpc/section3/mpc3521.htm)An Example (http://www.itl.nist.gov/div898/handbook/mpc/section3/mpc3521.htm)Multivariate Control Charts - 1Multivariate Control Charts - 1Bivariate charts: based on bivariate Normal assumptions component-wise limits lead to Type I, II errors Depth based control charts (nonparametric): map n-dimensional data to one dimension using depth e.g. Mahalanobis Build control charts for depth Compare against benchmark using depth e.g. Q-Q plots of depth of each data setnullXYBivariate Control ChartMultivariate Control Charts - 2Multivariate Control Charts - 2Multiscale process control with wavelets: Detects abnormalities at multiple scales as large wavelet coefficients. Useful for data with heteroscedasticity Applied in chemical process control Model Fitting and OutliersModel Fitting and OutliersModels summarize general trends in data more complex than simple aggregates e.g. linear regression, logistic regression focus on attribute relationships Data points that do not conform to well fitting models are potential outliers Goodness of fit tests (DQ for analysis/mining) check suitableness of model to data verify validity of assumptions data rich enough to answer analysis/business question?Set Comparison and Outlier DetectionSet Comparison and Outlier Detection“Model” consists of partition based summaries Perform nonparametric statistical tests for a rapid section-wise comparison of two or more massive data sets If there exists a baseline “good’’ data set, this technique can detect potentially corrupt sections in the test data setGoodness of Fit - 1Goodness of Fit - 1Chi-square test Are the attributes independent? Does the observed (discrete) distribution match the assumed distribution? Tests for Normality Q-Q plots (visual) Kolmogorov-Smirnov test Kullback-Liebler divergence Goodness of Fit - 2Goodness of Fit - 2Analysis of residuals Departure of individual points from model Patterns in residuals reveal inadequacies of model or violations of assumptions Reveals bias (data are non-linear) and peculiarities in data (variance of one attribute is a function of other attributes) Residual plots nullhttp://www.socstats.soton.ac.uk/courses/st207307/lecture_slides/l4.docDetecting heteroscedasticit