Data entry is a significant process in any business organization and as the data grows, businesses typically utilize professional data entry services to handle the data management challenge. Customer data is a very important asset and this must be entered correctly in the marketing database. Errors in this data could prove costly. However, such inaccurate entries are being made at many dealerships. When customer data is entered and reentered into various dealership software platforms, the information is often changed considerably. Errors from such double entry typically result from software platforms not being integrated, or from processes not being accurate. There is an additional opportunity for error with every additional data entry point, and these errors can prove costly.
Most bad data starts with human error. Several reports highlight that double entry is often the culprit for creating inaccurate records or bad data, especially in the case of dealerships. Several departments of an organization also may have to enter the same data again.
Dealer Marketing Magazine has discussed a simple example that shows the drawback of double entry.
Imagine a client named Brian Smith walks into your dealership. In the single entry, his information is entered correctly. But when re-entering the same information into the next platform, there are chances for his name to be misspelled as “Bryan”. Though “Brian Smith” and “Bryan Smith” is the same person, if systems fail to catch that, Brian will be receiving two emails, two mailers, and two phone calls every time.
It is also important for firms to track the amount of time their staff spends on re-entering customer data. The time wasted reentering and correcting data in dealership software platforms will impact business revenue.
How Bad Data Impacts Growth
- IBM reports highlights that bad data costs the U.S. economy $3.1 trillion every year.
- Experian QAS also found that bad data has a direct impact on the bottom line of 88% of all American companies.
- Halo business intelligence report says that about 92% of businesses admit that their contact data is not accurate and about 66% of organizations believe they are negatively affected by inaccurate data.
Addressing data quality is critical to achieve immediate improvements in efficiencies and profitability. Successful dealers are now identifying and eliminating barriers such as double entry with measures such as –
- Identifying time-consuming gaps where double entry is taking place. Finding out specific problem areas track the amount of time spent entering data across the dealership to find the right solution.
- Choosing integrated software platforms can eliminate most of those chances for human error, thus streamlining processes. Eliminating double entry also enhances customer care by decreasing the buying time for customers.
- Standardization of processes for collecting customer data, such as what you collect and how you collect it, will promote overall database accuracy. Clear and consistent processes will point out inefficiencies and allow employees to correct them and they can easily fall into a routine, resulting in time savings and increased data accuracy.
An effective way for businesses to ensure clean data is by outsourcing their data entry requirements to a reliable provider.
Dual Data Entry as a Quality Assurance process
Organizations can ensure good data and avoid bad data in their databases with dual data entry system that is part of the Quality Assurance process of data entry companies. This helps reduce the number of data entry errors in computer data sets. Compared to single data entry, double data entry helps in getting a cleaner database. Dual data entry involves two data entry operators entering data into two independent data sets and these two data sets are electronically compared. When differences exist, the correct values can be determined referring the original data sheets and the two data files can then be corrected. The errors that will exist after making these corrections are only when the two independent entry operators make the same exact data entry error, which is highly unlikely. Reports show that the time required for two people to enter data is minimal compared to the time required to manually identify and correct data entry discrepancies.