Total Information Quality Management (TIQM)
TIQM (previously TQdM – Total Quality data Management) the information quality assurance methodology for data quality issues elaborated by the internationally acknowledged Larry P. English.
The task of information quality assurance
According to Larry P. English the responsibility of information quality assurance is:
Meeting consistently the requirements of people working with information through information and information services.
It helps our understanding of the definition, if we study it in details:
Consistently: if an information service – e.g. a price list – varies through time, it will become less reliable. The main characteristics of quality information is that we can count on it, and we don’t have to verify it.
Meeting the requirements: don’t necessarily overcome them. In most of the cases for people who work with information it is perfectly enough if the information contains no more than what they need.
The quality of information is determined by the person who uses it. Those quality criteria that are determined by people who are far from the information chain processes do not make sense. Of course, within a company more people can work with the same piece of information. In this case the differing, opposing requirements need to be harmonised.
Information quality as the main characteristic of products
The above definition of information quality has several important elements:
Information is a product that can be categorised by “good quality” and “inadequate quality” attributes. Information is the end product of business activities, and its coming into being and its maintenance is regulated, no matter if it is stored in a database, or on paper or any other forms. In the information age, data must be treated as a direct product in all forms and not as a by-product of other business processes as it was done previously. Considering information as a by-product the emphasis is not placed on the proper purpose, rather on the system, and not on the ultimate purpose, which is the information itself.
As the information is a product, the same quality improvement principles can be applied to the business processes in order to improve the quality of the information product that are applied to the manufacturing processes to improve the quality of manufacturing products.
Information quality exists only in relation to the individual who uses that piece of information. The product is valuable only if the user needs it. Information is valuable only if the worker needs it in order to accomplish his own processes and achieve his goals. Only those who use information are able to estimate its quality, which depends on how helpful it is along their work. Shirou Fujita, CEO of NTT DATA, the first information service company that won the Deming Award […], said: “In this industry [information systems] we too often emphasize technological advances without having a look at the needs of the user’s circle… Information systems need to become much better and serve the needs of society much better.
Information exists as part of a supplier-user value chain and its relationship. Suppliers are information providers who perform various processes to generate information. Data mediators take the data in one form, such as paper, and rewrite it into electronic form. They play a role in mediating information from the supplier to the user. Users are internal or external intellectual workers. Most people play both roles (supplier/user). As users, based on certain data, they generate other data. Such user is for example a credit assessor, who considers credit on the basis of creditworthiness data.
Every process owner or manager, who leads a data generating or data management process is responsible for not only the physical product, but for data generated along these processes. The integrity of the process is inseparable from the integrity of the produced products. More than this, the responsibility extends to the quality of information in order to serve the other users’ needs and not just the needs of the information producing department. They need to know about the aggregate information needs of all the intellectual workers behind them in the information chain, about information products managed by them and their processes. Failure to adhere to this will force workers in the information chain to detect and improve those data on their own, that were not recorded properly during the initial process.