Data cleansing


Data cleansing or data cleaning is the process of identifying and correcting corrupt, inaccurate, or irrelevant records from a dataset, table, or database. It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. Data cleansing can be performed interactively using data wrangling tools, or through batch processing often via scripts or a data quality firewall.
After cleansing, a data set should be consistent with other similar data sets in the system. The inconsistencies detected or removed may have been originally caused by user entry errors, by corruption in transmission or storage, or by different data dictionary definitions of similar entities in different stores. Data cleaning differs from data validation in that validation almost invariably means data is rejected from the system at entry and is performed at the time of entry, rather than on batches of data.
The actual process of data cleansing may involve removing typographical errors or validating and correcting values against a known list of entities. The validation may be strict, or with fuzzy or approximate string matching. Some data cleansing solutions will clean data by cross-checking with a validated data set. A common data cleansing practice is data enhancement, where data is made more complete by adding related information. For example, appending addresses with any phone numbers related to that address. Data cleansing may also involve harmonization of data, which is the process of bringing together data of "varying file formats, naming conventions, and columns", and transforming it into one cohesive data set; a simple example is the expansion of abbreviations.

Motivation

Administratively incorrect, inconsistent data can lead to false conclusions and misdirect investments on both public and private scales. For instance, the government may want to analyze population census figures to decide which regions require further spending and investment on infrastructure and services. In this case, it will be important to have access to reliable data to avoid erroneous fiscal decisions. In the business world, incorrect data can be costly. Many companies use customer information databases that record data like contact information, addresses, and preferences. For instance, if the addresses are inconsistent, the company will suffer the cost of resending mail or even losing customers.

Data quality

High-quality data needs to pass a set of quality criteria. Those include:
  • Validity: The degree to which the measures conform to defined business rules or constraints. ' When modern database technology is used to design data-capture systems, validity is fairly easy to ensure: invalid data arises mainly in legacy contexts or where inappropriate data-capture technology was used. Data constraints fall into the following categories:
  • * Data-Type Constraints: values in a particular column must be of a particular data type, e.g., Boolean, numeric, date.
  • * Range Constraints: typically, numbers or dates should fall within a certain range. That is, they have minimum and/or maximum permissible values.
  • * Mandatory Constraints: Certain columns must not be empty.
  • * Unique Constraints: A field, or a combination of fields, must be unique across a dataset. No two persons may have the same social security number.
  • * Set-Membership constraints: The values for a column come from a set of discrete values or codes. For example, a person's sex may be Female, Male, or Non-Binary.
  • * Foreign-key constraints: This is the more general case of set membership. The set of values in a column is defined in a column of another table that contains unique values. For example, in a US taxpayer database, the "state" column is required to belong to one of the US's defined states or territories: the set of permissible states and territories is recorded in a separate table. The term foreign key is borrowed from relational database terminology.
  • * Regular expression patterns: Occasionally, text fields must be validated this way. For example, North American phone numbers may be required to have the pattern 999-999–9999.
  • * Cross-field validation: Certain conditions that utilize multiple fields must hold. For example, in laboratory medicine, the sum of the components of the differential white blood cell count must be equal to 100. In a hospital database, a patient's date of discharge from the hospital cannot be earlier than the date of admission.
  • Accuracy: The degree of conformity of a measure to a standard or a true value. ' Accuracy is very hard to achieve through data cleansing in the general case because it requires accessing an external source of data that contains the true value: such "gold standard" data is often unavailable. Accuracy has been achieved in some cleansing contexts, notably customer contact data, by using external databases that match up zip codes to geographical locations and also help verify that street addresses within these zip codes actually exist.
  • Completeness: The degree to which all required measures are known. Incompleteness is almost impossible to fix with data cleansing methodology: one cannot infer facts that were not captured when the data in question was initially recorded.
  • Consistency: The degree to which a set of measures are equivalent across systems. . Inconsistency occurs when two data items in the data set contradict each other: e.g., a customer is recorded in two different systems as having two different current addresses, and only one of them can be correct. Fixing inconsistency is not always possible: it requires a variety of strategies - e.g., deciding which data were recorded more recently, which data source is likely to be most reliable, or simply trying to find the truth by testing both data items.
  • Uniformity: The degree to which a set of data measures are specified using the same units of measure in all systems. ' In datasets pooled from different locales, weight may be recorded either in pounds or kilos and must be converted to a single measure using an arithmetic transformation.
The term integrity encompasses accuracy, consistency and some aspects of validation ' but is rarely used by itself in data-cleansing contexts because it is insufficiently specific.

Process

  • Data auditing: The data is audited with the use of statistical and database methods to detect anomalies and contradictions: this eventually indicates the characteristics of the anomalies and their locations. Several commercial software packages will let you specify constraints of various kinds and then generate code that checks the data for violation of these constraints. This process is referred to below in the bullets "workflow specification" and "workflow execution." For users who lack access to high-end cleansing software, Microcomputer database packages such as Microsoft Access or File Maker Pro will also let you perform such checks, on a constraint-by-constraint basis, interactively with little or no programming required in many cases.
  • Workflow specification: The detection and removal of anomalies are performed by a sequence of operations on the data known as the workflow. It is specified after the process of auditing the data and is crucial in achieving the end product of high-quality data. In order to achieve a proper workflow, the causes of the anomalies and errors in the data have to be closely considered.
  • Workflow execution: In this stage, the workflow is executed after its specification is complete and its correctness is verified. The implementation of the workflow should be efficient, even on large sets of data, which inevitably poses a trade-off because the execution of a data-cleansing operation can be computationally expensive.
  • Post-processing and controlling: After executing the cleansing workflow, the results are inspected to verify correctness. Data that could not be corrected during the execution of the workflow is manually corrected, if possible. The result is a new cycle in the data-cleansing process where the data is audited again to allow the specification of an additional workflow to further cleanse the data by automatic processing.
Good quality source data has to do with "Data Quality Culture" and must be initiated at the top of the organization. It is not just a matter of implementing strong validation checks on input screens, because almost no matter how strong these checks are, they can often still be circumvented by the users. There is a nine-step guide for organizations that wish to improve data quality:
  • Declare a high-level commitment to a data quality culture
  • Drive process reengineering at the executive level
  • Spend money to improve the data entry environment
  • Spend money to improve application integration
  • Spend money to change how processes work
  • Promote end-to-end team awareness
  • Promote interdepartmental cooperation
  • Publicly celebrate data quality excellence
  • Continuously measure and improve data quality
Others include:
  • Parsing: for the detection of syntax errors. A parser decides whether a string of data is acceptable within the allowed data specification. This is similar to the way a parser works with grammars and languages.
  • Data transformation: Data transformation allows the mapping of the data from its given format into the format expected by the appropriate application. This includes value conversions or translation functions, as well as normalizing numeric values to conform to minimum and maximum values.
  • Duplicate elimination: Duplicate detection requires an algorithm for determining whether data contains duplicate representations of the same entity. Usually, data is sorted by a key that would bring duplicate entries closer together for faster identification.
  • Statistical methods: By analyzing the data using the values of mean, standard deviation, range, or clustering algorithms, it is possible for an expert to find values that are unexpected and thus erroneous. Although the correction of such data is difficult since the true value is not known, it can be resolved by setting the values to an average or other statistical value. Statistical methods can also be used to handle missing values which can be replaced by one or more plausible values, which are usually obtained by extensive data augmentation algorithms.