From the name itself, dirty data really offers no good to an organization. Dirty data is basically a data that contains erroneous information. It has been costing companies millions of pounds each year. As a matter of fact, dirty data costs organizations almost Â£500 billion each year, based on the reports from The Data Warehousing Institute (TDWI).
Types of Dirty Data
Data quality is important in business efficiency and productivity. It is important that organizations practice data management. However, dirty data can really be inevitable. In most cases, data quality is being affected by the way data is being stored and managed. Here are the six common types of dirty data:
Incomplete data refers to portions and fields in the database or master data records left blank. Business operations cannot move on to the next step if the data are incomplete. For example in a lending department, data elements including initial loan amount, interest rate, and payment terms are really important.
In contrary to lack of data, there is a duplicate data. Most duplicate data involves customer profiles, redundant values in the database, and repetitive elements. This can be costly for organizations especially when they are performing an inventory.
In this situation, the data are given but are incorrect. In filling in dates, months are represented by numbers 1 to 12, days by numbers 1 to 31, and the year is usually 4-digit. In the case of address, it should consist of street, city, state, etc. Incorrect data can really affect the data quality.
Apart from incorrect data, some data may be factual, however, there are certain elements that are inaccurate. Inaccurate data mostly occur in the address, especially with the abbreviations. Inaccurate data can cost businesses a lot. For example, the inaccurate address can result in wrong deliveries.
Redundancy of data results in inconsistency. This happens mostly with profile names. There are various clients with âMarkâ as their name. These redundant elements may decrease efficiency in business operations. Moreover, it also leads to wrong actions.
Business rule violations
Business rule violations are dirty data that occurs when certain enterprises follow varied data procedures and formats. Most common examples for this are units of measurement, currency, address and date format, etc.
Impact of Dirty Data
Dirty data really hinders business efficiency. It is important that every company execute data quality management to avoid:
Loss of revenue
A single error in the data can lead to a loss of revenue. It can affect the flow of products and services. Dirty data can also decrease trust from clients and customers, leading to reduced conversion rate.
Marketing efforts will also be put to waste. Wrong data can affect how marketers devise their marketing strategy to a specific target audience.
Dirty data will also lead to wrong decisions. Basically, data are so important in business operations. If they are incorrect or inaccurate, the future actions of the company will be affected greatly. Apart from the members of the organization, the clients and customers will also be affected.