Consistent Data Quality refers to the state of a data resource where the quality of existing data is thoroughly understood and the desired quality of the data resource is known. It is a state where disparate data quality is known, and the existing data quality is being adjusted to the level desired to meet the current and future business information demand.
Data is the most important component of a computer system. A common concept in computer science is called Garbage in Garbage Out (GIGO) which refers to the fact that no matter how sophisticated and perfect any software application or computer systems is, if the data entered is not the correct data or not of good quality, the output will always be garbage. In programming, poor data quality may cause a bug which is hard to trace.
Data are said to be of high quality, according to JM Juran, "if they are fit for their intended uses in operations, decision making and planning". In business intelligence, data are of high quality if they accurately represent the real life construct that they represent.
Data warehouses are the main repositories of company business data which include all current and history data. Business intelligence mainly relies on these data warehouses so they can know the industry trends. With the information recommended by the business intelligence, a company can already strategize to gain competitive edge over competitors. For instance, if they know that the products or services of the competing company is gaining strong acceptance among the customer and the effect of this is reflected in the analysis of the company’s business intelligence, the decision makes can try to come up with innovations to cope up with the competitor.
And so companies should make a strong emphasis on having consistent data quality so they do not get garbage information from the data warehouse. Marketing efforts typically focus on name, address and client buying habits information but data quality is important in all other aspects as well. The principle behind quality data encompasses other important aspects of enterprise management like supply chain data and transactional data.
The difficult part with dealing with data is that it may sometimes be very difficult or to an extreme case, impossible to tell which is good quality data and which is bad quality data. Both could be reported as identical through the same application interface. But there are some guides to improve and have consistent quality data within the business organization.
- It is important to involve the users. People are the main doers of data entry so they can be used as the first line of defense. On the other end, people are also the final consumers of data so they could also be in the last line of defense to have consistent data quality.
- Having somebody or a group of skilled and dedicated staff to monitor the business processes is a good move for the company. Data may actually start as good data but will turn bad through time as it decays. As an example, a project prospects list will definitely get out dated. Decayed data, data which become irrelevant through time, are hard to detect and could cause damage and lots of monetary losses. A good business process monitoring ensures timely and accurate update. It is also important to streamline process when possible so that the number of hands touching the data will be minimized and the chances of corrupting data will be greatly reduced.
- The use of a good software can help maintain consistent data quality. There are may credible software vendors where a company can buy application from.