The U.S. economy wastes an estimated $3 Trillion per year due to incorrect, inconsistent, fraudulent and redundant data. Businesses incur a very large portion of this cost. The Data Warehousing Institute estimate that the total cost to U.S. business more than $600 Billion a year from bad data alone. That is a very large cost that can easily be reduced by investing a much smaller amount of money earlier business data processing. No business is immune to financial loss from lost sales or extra expenses caused by incorrect data. However businesses are able to limit these losses through proper data cleansing.

The rule of 1:10:100 by W. Edwards Deming says that it takes $1 to verify a record when it is first entered, $10 to clean or deduplicate bad data after entry, and $100 per record if nothing is done to rectify a data issue. Part of this cost is incurred due to mail cost. The average company wastes $180,000 per year on direct mail that does not reach the intended recipient due to inaccurate data. Poorly marketed advertisements to incorrect demographics are another expense of having poor or out of date information.

The most common issues found due to data quality are duplicate and old records. Within one month of receiving customer data 2% becomes incorrect due to moving, death, marriage or divorce. If you are receiving data from an outside source, i.e. other departments, other businesses, these sources may not know how old the records are, if imported data is several months old 10% or more may be inaccurate. By having tools in house, companies can limit the level of incorrect data they deal with. An issues with in house data cleaning is that IT departments are often heavily relied on for that process. IT professionals have expert knowledge in technical issues, but often lack in-depth data processing best practices. Usually companies employ data analysis experts, but again, they often lack in-depth IT knowledge. The strength of utilizing business data is dependent on these professionals ability to function at their best. Often there is time lost or wasted due to lack of clear communication between these two departments. In addition lack of understanding causes incorrect information to be returned and can result in having to redo various aspects of the process.

What does Aim-Smart do to eliminate these issues? Aim-Smart is built with the business user, analysis expert and IT personal in mind. With Aim-Smart business users and analysis experts are able to manipulate data with the speed and accuracy of IT professionals and use their knowledge of advanced data processing techniques to remove or update data as needed. This is done all with in Excel, a comfortable environment for any business professional. This allows the IT professional to focus on the upkeep of the data platforms and focus on maintaining the platforms for data storage. I also removes many opportunities for misunderstanding and communication breakdown. It is estimated that up to 50% of IT expenses are spent on data cleaning. Aim-Smart removes most of the data cleaning process from the IT department. By putting the power of data cleaning in the hands of the business user, IT demands drop and allows the IT professionals to focus on strengthening and updating internal systems within the company. Aim-Smart allows business users to remove data, or update records they find to be out of date. It also allows them to manipulate the data as they want through parsing, standardization and other features. By doing this, company marketing budgets can more effectively be spent on contacts or data results that are accurate. This helps to maximize the use of company money spent on advertising as well as other expenses.

Source:
“$3 Trillion Problem: Three Best Practices for Today’s Dirty Data Pandemic” by Hollis Tibbetts