5 Best Practices to Improve Data Entry Process

A data entry job might be one of the most non-glamorous positions in organizations, but it is definitely the most critical operations of successful business development. Data entry, these words are spoken with more weight today, because they have to deal with big data. And enterprises should take impressive measures so as to not hurt their business value and time from data inaccuracies.

People running businesses are realizing that the chunks of data that is being generated on a day-to-day basis can help them make crucial business decisions. And inaccuracies would only push them to pay for the overhead costs.

Now, we know that the basic nature of a human is to do mistakes. Is it always the same mistake/misstep? But why do mistakes happen or where do our resources find it challenging to deal with the data? And lastly, how to deal with such setbacks?

In this blog post, we will find answers for each and every query and of course, the solution to address such issues.

Common Challenges in Data Entry Process

Data entry is ubiquitous and differs for every organization.

With digitization, organizations went paperless for better business management. Data entry is the primary operation involved, where resources have to take data from paper documents, catalogs, invoices and transfer them manually to the required software application for future processing.

The whole process looks pretty straightforward, but every phase presents resource with unique challenges resulting in data inefficiency.

Now, let’s see the common challenges faced by data entry operators.

• Inadequate Knowledge:

Even if you choose data entry as your career, you should have a certain skill set to deal with the every-day operations. Basic computer skills, good speed, accuracy, are all good but resources cannot promise quality data entry service unless the organizations familiarize them with new equipment and other programs.

• Unacceptable Benchmark:

The huge amount of data or data that require more understanding may stir trouble if the benchmark set is too high. This can be visibly the actual human error.

• Complex Data Fields:

Different data types to capture, what is the probability that 1 out of 100 employees don’t do a mistake. It’s not just unclear fields, but sometimes data entry operators may run into missing values. And in such events, the possibilities are they may assign meaningless or default values, which later will create conflicts.

• No Proper Quality Check:

You can say there is a violation of 1-10-100 rule.

What does the rule say?

The rule states that prevention should take more priority as it takes much less to detect rather than correcting one.

Now the question, how to deal with these?

There are more cases that lead to data entry inaccuracies and maybe I’ll mention them in further sections. For now, let’s comprehend the methods for improvising data entry processes.

What are some of the key Methods to Optimize Data Entry Processes?

1. Identifying the Source of Data Inaccuracies

When the word ‘data’ comes into the picture, it’s not only about day-to-day values or information that is accumulated within the organization. It is also inclusive of external data that is consumed. Whatever may be the case, but if businesses do find inaccuracies in data, they should try identifying the source as it will be easier to fix.

Sometimes the source of the mistake may be as simple as a data operator entering improperly to a complicated system error. Understanding the sources will give a larger scope to assess, monitor, and improve

Common areas where data inaccuracies can be witnessed are,

2. Using Data Entry Validation Program

Performing validation, what does it generally mean?

It signifies towards accounting for the correctness, meaningfulness, and security of data that is input to the system.

After the data is input, it is validated, where data cleansing is performed, as in, the corrupt or inaccurate data will be detected and modified/removed from the database.

Under validation program, the operator is required to verify the data entered more than once before it is actually acknowledged. Even though the process may seem time-consuming, business will be spared from future anxieties.

3. Utilizing Advance Software Programs

Let’s take the data entry validation program, what if it has bugs or the program structure is inaccurate?

It doesn’t make out to be a human error, but still, it accounts for some kind of data inaccuracies. You are running a global organization, and then using cheap software program will cause data inefficiencies.

Every business has different needs, even the software program should be chosen based on these.

Let’s say, for instance, you have a start-up, the data generated will be comparatively less to global corporations, and hence you will not be required advanced software.

Viking software solutions, Lexmark, goCanvas, and iUgum data software are some of the popular data entry software programs available.

4. It should never be Speed Vs Accuracy

Speed and accuracy are the two key skills that every data entry operator should possess.

Speed is good but not the cost of accuracy. People often get weighed down under deadlines, and this where organizations will find bonus if they are quick. But on the other side, quality would have been compromised at least at some point.

Ask anyone; their opinion will be like Accuracy > Speed, but when they have to face the deadline, it would take the reverse order, and that says it. ‘Quality is compromised’.

Today, data entry is beyond a mundane task, thanks to big data. To avoid getting consumed under short deadlines, organizations should hire more resources and maintain the quality of work.

5. Adopting Data Entry Accuracy Standards

The boring, conventional data entry job has become more exciting and operators should tread more carefully if the data comes with pre-defined standards.

Data accuracy standards such as data monitoring, geo-coding, matching, data linking and data profiling allow resources to be within the loop of defined standards.