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Data Quality

(Usage hints for this presentation)

Winter Term 2020/2021
Dr. Jens Lechtenbörger (License Information)

DBIS Group
Prof. Dr. Gottfried Vossen
Chair for Computer Science
Dept. of Information Systems
WWU Münster, Germany

1 Introduction

1.1 Learning Objectives

  • Explain data quality (DQ) as multidimensional concept
  • Identify DQ issues with reference to quality dimensions
    • Apply data profiling (as part of project work)
  • Apply cleaning process (as part of project work)

1.2 Motivating Examples

  • Who wrote this?
    • Lechtenberger, Jens Lechtenboerger, Jens Lechtenborger, Jens Lechtenb�rger, J. Lechtenbörger, lechten
  • Where?
    • Grevener Straße 91, Steinfurter Str. 107, Steinfurter Straße 109, Leonardo Campus 3, Leonardo-Campus 3
  • When?
    • 2020-11-01, Nov 1st, 1. November, 01/11, 11/01

1.2.1 An Impressive Example

1.3 Why do we care?

  • Quotes from [Mar05] (2005)
    • “88 per cent of all data integration projects either fail completely or significantly over-run their budgets”
    • “75 per cent of organisations have identified costs stemming from dirty data”
    • “33 per cent of organisations have delayed or cancelled new IT systems because of poor data”
    • “$611bn per year is lost in the US in poorly targeted mailings and staff overheads alone”
    • “According to Gartner, bad data is the number one cause of CRM system failure”
    • “Customer data typically degenerates at 2 per cent per month or 25 per cent annually”

1.4 Costs of Poor Data Quality

  • Attributing costs or assessing impact of poor quality is hard
    • See [HZVL11] for four types of costs
      • Two-by-two matrix
        • Costs may be direct or indirect
        • Quality affects operational tasks and strategic decisions
      • E.g., payment errors are direct and operational, poor production planning is indirect and strategic
    • Negative effects cited in [CR19]
      • According to Gartner in 2018, organisations attribute losses of 15 million USD per year on average
      • 2016 IBM research estimates total annual losses in US to be 3 trillion USD
      • According to KPMG 2017 Global CEO Outlook, 56% of CEOs worry about negative impact on their decisions
      • Compliance risks

2 Data Quality Frameworks

2.1 DQ Framework Overview

  • Data quality frameworks offer processes for strategic DQ improvement
  • See [CR19] for an overview
    • Comparison of twelve DQ frameworks that cover
      • DQ definition
      • DQ assessment
      • DQ improvement
    • Decision guide for organizations
      • Criteria to narrow down choice of framework

2.1.1 Data Quality (DQ)

  • “Fitness for use” (with background in quality literature, see [WS96])
    • Quality is judged by consumer according to context and purpose
  • Lots of data quality dimensions, see [SSP+12] for survey
    • According to [CR19], most commonly (going back to [WS96]):
      • Completeness: Sufficient breadth, depth, scope for task at hand
      • Accuracy: Correct, reliable, certified
      • Timeliness: Age is appropriate for task at hand
      • Consistency: Same formats, compatible with previous data
      • Accessibility: Available, or easily and quickly retrievable

2.1.2 DQ Assessment

  • Need metric per quality dimension
    • Subjective, e.g., measurements with surveys among data consumers
    • Objective, e.g., count NULL values, constraint violations, duplicates, or measure number of erroneous decisions

2.1.3 DQ Improvement

  • Improve information products
    • E.g., de-duplicate data, fill in missing values, standardize, fix errors
  • Improve information processes
    • May start from root cause analysis
      • Why did low-quality data arise?
      • Change processes to avoid root causes.

3 Data Profiling

3.1 Getting to Know Your Data

  • See Chapter 3 in [HKP11]
    • Inspect types of attributes
      • and their statistical properties such as mean, media, mode, quartiles, variance
    • Use visualizations
    • Along the way, identify data quality issues

3.2 Data Profiling Aspects

  • Methodical inspection of data instead of manual “eye-balling”
  • See [AGN15] for a survey
    • “Data profiling is the set of activities and processes to determine the metadata about a given dataset.”
      • Single column, e.g., cardinalities, value distributions, data types
      • Multiple columns, e.g., correlations, topic overlap, duplicates
      • Dependencies, e.g., (foreign) keys, functional dependencies and their violations
  • Aside

3.3 Talend Open Studio for Data Quality

4 Sample Cleaning Process

Kimball, Dealing with Dirty Data, DBMS, 1996

  • Process description
    • Six steps, may be part of ETL process
      1. Elementizing
      2. Standardizing (next slides)
      3. Verifying
        • Check whether elements are mutually consistent, e.g., ZIP code 48149 cannot be in Bavaria (08…, 09…)
        • Integrity constraints
      4. Matching
        • Check whether “equivalent” element does already exist; if yes, augment with new information (subsequent slides)
      5. Householding
        • Try to group elements, e.g., married couples
      6. Documenting

4.1 Standardizing (1/2)

  • Consider “gender”
    • Varying source representations
      • Possibly “real” values mixed with salutations, academic titles
        • Possibly all of male/female/diverse/etc., m/w, m/f, Mann/Frau, Herr/Frau/Firma, Dipl.-Ing., Dr., Prof., ?, unknown, NULL
        • Use profiling results (e.g., count distinct, histogram)
    • Define convention, e.g.:
      • TargetCode TargetName
        0 unknown
        1 female
        2 male
        3 diverse
    • May use lookup table
      • Data type of column Source?
      • Join source data with lookup table
        • Beware of NULLs!
      • View or ETL process to produce target data
Lookup Data Source Source TargetCode
  S1 unknown 0
  S1 female 1
  S1 male 2
  S1 diverse 3
  S2 f 1
  S2 m 2
  S2 ? 0
  S2 NULL 0
  S3 1 2
  S3 2 1
 

4.2 Standardizing (2/2)

  • NULL values are related to the completeness dimension of DQ
  • Avoid NULL values, explicitly represent degree of knowledge
    • Three types of NULLs
      • Not existing (inapplicable); no incompleteness issue
      • Value exists for sure, but we don’t know it; incomplete
      • We don’t know whether a value exists; unknown whether incomplete
ID Name Surname Birthdate E-Mail
1 John Smith 03/17/1974 smith@abc.it
2 Edward Monroe 02/03/1967 NULL (does not exist)
3 Anthony White 01/01/1936 NULL (existing but unknown)
4 Marianne Collins 11/20/1955 NULL (not known if existing)

(Source: [SMB05])

4.3 Matching

  • Matching = object identification = duplicate detection
    • Matching is easy for exact duplicates or with “real” keys (e.g., social security number)
    • Otherwise, need quasi-identifiers
      • Groups of attributes, possibly with similarity matching for probabilistic matching
      • E.g., name, date of birth, and address in presence of spelling mistakes

4.3.1 Matching Outlook

  • Finding of similar items to be revisited in several sessions
    • More efficient approaches than naive comparison of all pairs with quadratic complexity?
    • Measures/metrics for similarity?
  • Afterwards, data fusion is necessary
    • Given duplicates, create single object representation while resolving conflicting values

5 Conclusions

5.1 Summary

  • Data quality
    • is a pressing topic in practice,
    • is a multidimensional concept,
    • can be improved with cleaning steps (e.g., as part of ETL processes),
    • is the focus of data quality frameworks for strategic approaches.

Bibliography

License Information

Source files are available on GitLab (check out embedded submodules) under free licenses.

Except where otherwise noted, the work “Data Quality”, © 2005-2020 Jens Lechtenbörger and © 2006-2019 Gottfried Vossen, is published under the Creative Commons license CC BY-SA 4.0.

No warranties are given. The license may not give you all of the permissions necessary for your intended use.

In particular, trademark rights are not licensed under this license. Thus, rights concerning third party logos (e.g., on the title slide) and other (trade-) marks (e.g., “Creative Commons” itself) remain with their respective holders.