It’s useful for businesses to review data insights and analytics by using reports to track patterns and trends. Data projections can be used to help when making important business decisions anticipating customer needs. When unreliable data, known as “dirty data,” ends up in the mix, your business insights can be compromised. Dirty data is inevitable. Studies show that executives believe around 33% of data is wrong. Even though it can’t be avoided, it should still be minimized. To safeguard your analytics against dirty data, you need understand a little more about it.
What is Dirty Data?
Essentially, dirty data is invalid or unusable data. Dirty data can be incorrect customer records such as invalid zip codes, or just duplicated information. Dirty data can also include data that is outdated or unreasonable, this can happen when the information comes from a time that no longer represents your current business model or procedures.
Where does it come from?
Dirty data often comes from human error. This happens as people entering data become tired or there are just mistakes from repetition, and even differences between the people who are entering the data. Data entry fields that are skipped or left blank can result in information that is missing context that gives it value. Data being entered in the wrong field and typos also can taint useful information. Honest mistakes in data entry can lead to skewed results further along in the process.
How can dirty data be avoided?
To get rid of comprised data, look at the way data is being collected. Forbes says that users should start by understanding the “data journey within the organization.” This means knowing what information is valuable and having a common understanding what data is meaningful and why.
Software solutions can be used to minimize the need to repeat data entry which reduces the chance of data being entered incorrectly. Some software will bring the user’s attention to potential data entry errors and force them to review before they are able to move on. This can also include any data that is being duplicated.
The overall consensus on how to mitigate dirty data is to control data input, implement access to data once it has been entered, and regularly review and cleanse data.
Why does it matter?
Only 34% of business check for data duplication and 20% of businesses never review their data. Data is an asset and it has many values for any business. Eliminating dirty data means that business insights will be more accurate and can help you make accurate decisions about the future. Forbes estimated that dirty data can cost business up to 12% of total revenue. Leveraging data can increase profitability and give your business an upper hand against around 13%-20% of competitors who are not cleaning their data.
