The success of your business depends on the quality of your business decisions, decisions which in turn depend almost entirely on the quality of the data you rely on to make them. Data is the life-blood of any business, and data health is the key to successful, data-driven policies. Accurate, relevant and up-to-date data are vital for the continued growth and success of your business.<\/p>\n
Data is constantly acquired from a wide variety of sources such as POS systems, loyalty programs, customer surveys, etc. In order to maintain growth and increase profitability, businesses will also regularly need to acquire third party data, which they can purchase from reliable, trust-worthy data suppliers<\/a>. Irrespective of the source of the data, data health is an absolute necessity if you don\u2019t want to end up spending lots of money and effort with absolutely no return on your investment.<\/p>\n When you are considering the health of your data, be it data collected in-house or third party data which you purchased, you need implement a data health care system which continuously monitors, validates and repairs the ever increasing amount of data available to you. It is helpful to think of data health in the same way as you would think of dental care. If you don\u2019t brush your teeth regularly, you will soon end up with bad teeth. It is the same with data.<\/p>\n If you don\u2019t maintain your data frequently and regularly, you will very soon end up with corrupt data which will automatically lead to wrong conclusions being drawn from the bad data supplied. Just like brushing your teeth, maintaining data health is necessary to ensure the continued supply of good data on which you can base effective and productive business decisions. But, as with dental care where, despite regular brushing, you still need a regular visit to the dentist, your data too needs a regular and proper professional care to ensure top quality data is available at all times.<\/p>\n Data health can be ensured in two ways. Both methods are based on prevention, detection and repair. The difference between them lies in where the emphasis is placed on those 3 components to ensure top quality data health.<\/p>\n Data maintenance focusses mainly on preventing data corruption at entry point, through validation of the data at source. Constant validating and, when necessary correcting of your data through regular checks, is a vital part of maintaining data health. With the necessary training data maintenance can easily be, and usually is carried out in-house<\/p>\nData Maintenance And Data Cleaning<\/h2>\n