by Juan Robin II
They say to err is human. They also say a craftsperson is only as good as the tools they use. In the battle against errors, it is best to find the right tools from the start instead of fixing issues later.
When it comes to data entry, stats show a standard error rate of 6.5% in most datasets.
The same study showed that data management and could lower those rates to as little as 1.3%.
If you want to get your data house in order and aim for those lower numbers, you are going to need some help. So read on to learn what you need to know to top the competition and squash errors.
Data Entry Optimization
Let’s be real, data quality is about data optimization. Data perfection doesn’t exist and demanding it creates more problems than it could solve.
To improve data quality you need to be removing errors frequently and efficiently. The following tips provide ways to do just that. We’ll also elaborate on how each tip synergizes with others.
Setting a standard for what data looks like when entered makes a big difference to end quality. Much like a style sheet exists for any given publication to keep all of the writers consistent. These incorporate standards on how to address issues of abbreviations, spelling, and word use.
To form a standard you start with knowing enough about the type of data you are getting to know how it will be formatted. A company working in the US will collect and format census data differently than a Japanese company. Mostly this comes from differences in dating standards and naming standards.
2. Fixed Values
Once a set of standards has been developed, fixed values for certain blocks should be created. Consistent formatting for values related to static input increase accuracy.
The classic examples come from madding menus for state names, provinces, or countries. These drop-down menus speed up data entry but also prevent typos from confusing the system and generating bugged reports.
Additional fixed data entry tips: avoid making the fields too small to see the whole entry after completion. This will give data workers the ability to spot check for the same roots as South Carolina vs South Dakota without rechecking the drop-down.
3. Key Data
Drop-down menus with fixed values start the ball rolling on the idea of key data points. Information that needs to be gathered and entered for every customer/client/item should be made mandatory and easy to access.
This both prevents the data from being submitted without completion and gives structure to the data entry forms.
When a piece of data becomes vital in the future, revisit and revise forms to incorporate the newer information as a key necessity. This process helps keep data entry from getting stale and improves data organization.
4. Duplicate Cures
Duplication in data entry bots down the system and slows down all processes from entry to retrieval.
Duplicates also create redundancies where errors in entry can occur more often. Worse, the errors that do occur cause more problems than single entry errors.
Prevent duplicates through programs that allow you to create duplicate detection scans. The process of creating key data and fixed value fields also aids in preventing information redundancies.
Not all fields need to be locked down in this way. Your standards will restrict the number of abbreviations and variations which cause duplication. You don’t run into a Bob vs Robert vs Bobby issue when you have a style sheet created.
Finally, don’t fear the loss of time in stamping out duplicates when they’re found. Taking a system offline and going through it to purge duplicates is good maintenance. It is also a better use of time than finding snowballing issues later on.
Related to duplication, though tangentially, is the integration of programs. Any business using multiple systems (which is most businesses) should make sharing the data across programs easy. This saves time, maximizes the efforts of data entry for the business as a whole, and limits duplication errors.
Businesses specifically using Excel can view here to find handy data automation tips and programs.
6. Administration and Data Steward
Creating a data administration group keeps the system from bloating. They oversee the standards and set the unchangeable fields. This also provides a channel for data entry workers to offer tips and advice they see from the ground and have it integrated.
These functions can also be done under a data steward. Not a manager of the people doing data entry so much as a person responsible for data quality. This person can test and access third-party tools and do simulations of data design.
A dedicated data steward raises a business’s data profile by providing expertise. Expertise, that too often gets swept onto IT managers that have little connection with what the data does outside of the systems.
7. Run Reports
Run sample and test reports routinely. Even though it can take time to produce a report through the system and sometimes locks up resources. A report represents the end point of the system.
Errors and problems can be spotted faster in the end product than by going over every bit of the data with a hunch there is a problem but no roadmap. Running reports also provides necessary information to revise standards and data optimization efforts.
8. Improve Entry Environment
Finally, consider the human factor in data entry. The single best way to improve data flow is to keep the people entering the data working well.
This starts with laying out understandable standards. Then adding a feedback system that rewards employees for accuracy as well as efficiency. Toss in an incentive for improving the automation of the systems.
Finally, the right combination of ergonomics keeps workers comfortable and de-stressed. Consider the benefits of the environment, desks, and chairs. Nothing spawns errors in a system like too little time and a feeling of being trapped.
The Right Tools
Data entry quality comes from a bottom-up approach. Data powers your business decisions and keeps you on top of the game. In business, you need to take advantage of every tool you can.