Any organization that deals with customer, prospect, supplier, distributor, product and service information, uses all kinds of data in their day-to-day business processes. Identification of a customer or a product within an automated system, using a specific id-number, the name or any other identifying feature, is a key issue in these processes. Furthermore, it is a task that needs considerable attention, since the collection and management of data is essentially error-prone. People make mistakes, names are understood incorrectly, numbers are typed in the wrong order; there are just too many reasons for defective data and poor information quality.
The collective term ‘business data’ is often used without a precise notion of what business data actually contain. It is not just the customer identification numbers and product codes. Naturally, the sort and the importance of data used in a business process will differ from organization to organization. However, a closer look at the seemingly endless variation will show that names and addresses of persons and organizations are as detailed and complicated as they are identifying. The following classification will show the details of names, addresses and complementary data.
* In personal names we will encounter: given (first) names, middle names, initials, surnames, surname prefixes, surname suffixes, forms of address, titles, functions, qualifications, professions, patronymics and nicknames.
* The name of an organization can consist of virtually everything: legal forms, fantasy words, natural language words, personal names, numbers, Roman numerals, ordinals, letters, acronyms, geographical indications, suffixes, articles, prepositions, conjunctions, indication of year of establishment and non-alphabetical signs.
* Postal Address data combine recipient information with delivery points: countries, regions, towns, districts, proximate towns, delivery service indicators, delivery service qualifiers, postcodes, addressee and mailee indicators, thoroughfare names, thoroughfare types, house or plot numbers, house number additions, building names, building types and delivery point access data, such as wing, floor or door.
* Complementary data used in business processes include: phone numbers, fax numbers, e-mail addresses, dates of birth, contract dates, social media account id’s, product and brand names, product codes, product numbers, gender indication, financial data, lifestyle data and transaction data.
Defining the data groups as precisely and as detailed as possible, is the first step towards useful interpretation. People, applying their natural language processing capabilities, structure the information as they interpret it. They will use their frame of reference, which includes their knowledge dictionary, their linguistic repository, statistical information and mathematical information.
Knowledge-based interpretation, incorporated in an automated system to solve data quality issues, must work in exactly the same way. Consider the following examples: Continue reading ‘Short question, complex answer: Who is who and what is what in your database?’