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Data Cleansing

Exchequer
Data
Cleansing

We can offer data cleansing services where we extract your Advanced Exchequer data, tidy and correct any inconsistencies or errors, then put it back.

data cleansing

What is data cleansing?

In the context of Advanced Exchequer, data cleansing is the process of detecting and correcting inaccurate data that’s present within Customer, Supplier and Stock records in your Advanced Exchequer company.

While each data field has a distinct purpose and stores a specific dataset, it’s often the case that its formatting or the contents are incorrect. Such inconsistencies may have been originally caused by hands-on user entry errors, corruption in transmission or storage, or by different data dictionary definitions of similar entities in different stores.

Examples of such inaccuracies include (but aren’t limited to) address lines in the incorrect order, phone numbers in the place of email addresses, duplication of information – the list goes on!

Why is data cleansing needed & how is this done?

Unreliable data can often conclude in issues when running reports from Advanced Exchequer, making analysis of your data a chore for members of your team. This wastes time and subsequently costs your company money due to a lack of efficiency.

With our data cleansing service, we utilise our ExSync and ExReport software to extract a copy of your data to assess it, using our years of experience and expertise to ascertain whether it is in a viable state. In instances where integrations take place with third party systems, it is imperative for accurate data to be in place for it to be relayed between Advanced Exchequer and additional solutions.

After the cleansing takes place, all company data should be consistent within the system.  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 (such as rejecting any address that does not have a valid postal code) or fuzzy (such as correcting records that partially match existing, known records). Data cleansing may also involve activities like harmonisation of data, and standardisation of data. For example, harmonisation of shortcodes (St, Rd, etc.) to actual words (street, road, etcetera).

Standardisation of data is a means of changing a reference data set to a new standard, such as the use of standard codes.

Data Cleansing Disk

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 (where constraints were not implemented in software) or where inappropriate data-capture technology was used (e.g., spreadsheets, where it is very hard to limit what a user chooses to enter into a cell, if cell validation is not used). Data constraints fall into the following categories:

    • Data-Type Constraints: For example, values in a particular column must be of a particular data type, e.g., Boolean, numeric (integer or real), date, etc.
    • 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 cannot be empty.
    • Unique Constraints: A field, or a combination of fields, must be unique across a dataset, for example, no two persons can 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 gender may be Female, Male or Unknown (not recorded).
    • 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/territories is recorded in a separate States table. The term foreign key is borrowed from relational database terminology.
    • Regular expression patterns: Occasionally, text fields will have to be validated this way, for example, 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 (since they are all percentages). 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 – see also Accuracy and precision. 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 (city and state) 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. (in some contexts, e.g., interview data, it may be possible to fix incompleteness by going back to the original source of data, i.e., re-interviewing the subject, but even this does not guarantee success because of problems of recall – e.g., in an interview to gather data on food consumption, no one is likely to remember exactly what one ate six months ago. In the case of systems that insist certain columns should not be empty, one may work around the problem by designating a value that indicates “unknown” or “missing”, but supplying default values does not imply that the data has been made complete.
  • Consistency: The degree to which a set of measures are equivalent across systems (see also Consistency). 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 (the latter knowledge may be specific to a given organization), or simply trying to find the truth by testing both data items (e.g., calling up the customer).
  • Uniformity: The degree to which a set of data measures are specified using the same units of measure in all systems ( see also Unit of measure). 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 (see also data integrity) but is rarely used by itself in data-cleansing contexts because it is insufficiently specific. (For example, “referential integrity” is a term used to refer to the enforcement of foreign-key constraints above.)

Data Cleansing Code Block

Process

  • Data auditing: The data is audited with the use of statistical and database methods to detect anomalies and contradictions: this eventually gives an indication of the characteristics of the anomalies and their locations. Several commercial software packages will let you specify constraints of various kinds (using a grammar that conforms to that of a standard programming language, e.g., JavaScript or Visual Basic) 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 is 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 grammar 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 analysing 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.