5 Ways to Improve Data Entry Accuracy

Whether you input data into a computerized database or spreadsheet, the process is termed as data entry. Data entry tasks may seem simple and redundant, but they are essential in making smart business decisions. No matter what industry you are doing business with maintaining data accuracy should be your top priority.

Some time in future, the data entry operations will be automated but till then organizations have to depend on resources and perform these tasks manually. Even if you hire a good professional team for managing these operations, it will be hard to say that the data lifecycle will complete without any complications.

Phases where enterprises could be found compromising on data quality

Stage 1: Data Acquisition

The data quality may take a beating in the initial stage itself. Missing values, inconsistent measurements, proxy variables are some of the factors that would affect data quality.

Stage 2: Internal Data Processing

Data stored will be regularly processed. With the process performed redundantly, the data can become inaccurate over time.

Stage 3: Data Manipulation

Each time you transfer, translate or reproduce data, chances of manipulation is very high.

Now the million-dollar question, how to maintain data quality?

It’s quite a challenge, because, organizations produce a considerable amount of data on a day-to-day basis and the volume generated can be overwhelming to monitor.

How to improve the efficiency of Data Entry Process?

1. Normalize Data Anomalies

Anomalies in a database are common, but this results in corruption and data inaccuracy.

An anomaly can be defined as setbacks that occur when a database is unnormalized and all data is stored in a single table.

Anomalies occur in three ways,

• INSERT

Insert anomaly materializes when certain attributes cannot be added to the database without the presence of other attributes.

• DELETE

Delete anomaly occurs when certain attributes are lost due to the deletion of other attributes.

• UPDATE

Is nothing but data inconsistency that occurs due to data redundancy and partial updates.

Through normalization, it is possible to reduce data redundancy. The process involves arranging of columns (attributes) and tables (relations) of a relational database.

2. Perform Data Profiling or Data Archeologyis

Data profiling is also referred as data archeologyis. Under this process, data values within the data set are assessed and statistically analyzed for consistency, uniqueness, and logic.

Data profiling only assists in identifying and understanding anomalies, business violations and not the data inaccuracy. The purpose of data profiling is to,

• Assess data quality and check whether the data conforms to any particular pattern or standard.
• Easily determine metadata of source database that is inclusive of value patterns, distribution, key candidates, functional dependencies, and foreign key candidates.
• Evaluate risks involved in integrating data into new applications.

By systematically addressing data anomalies and other inconsistencies, you can maintain good data quality.

3. Perform Error Analysis

Following data profiling, if you found inconsistencies in your database, you should perform an in-depth error analysis.

Basically, two methodologies are used,

• ERROR CLUSTER ANALYSIS

Here the complete data set is analyzed to identify the source error.

• DATA EVENT ANALYSIS

The events where data is created and changed are closely analyzed to identify the root cause of the errors.

4. Standardize the Data Entry Process

Even a simple data entry process should have a standardized approach because at the end of the day your database should be filled with quality data that you would assist you accurately in the future.

There are different ways to standardize your approach towards data entry,

1. Before data is entered into the database, make sure it is correct, complete, formatted, and verified.
2. Understand whether data normalization is required. (Know the data entry points).
3. Chose data standards to maintain accuracy and consistency.
4. Create normalization matrix and run it against your data.

5. Monitor the Feedbacks

Even though it is a simple non-revenue generating task, data entry has become an indispensable process of every organization. If you are to avoid the same repetitive mistakes, a comprehensive effort should be made to accurately perform data profiling and monitoring database for possible inaccuracies.

Resources engaged in the process should effectively take feedbacks from field staff and other operators and work towards improving the process.