Data entry errors leading to erroneous databases prove disastrous to the success and profitability of any organization.
Though the thrust on quality data has heated up board room discussions for long, businesses continue to fail miserably in improving data entry accuracy. Priorities to core business activities, lack of skilled professionals or minimal exposure to advanced data management tools, technology & expertise are the impediments.
How data quality proves costly to companies?
Poor data quality costs nearly $9.7 million per year to organizations across the globe, says Gartner. In the US alone, businesses tend to loose approx. $3.1 trillion annually due to poor data quality. Erroneous decisions made using bad data are inconvenient and prove extremely costly to various business functions.
Common data entry errors result into data quality problems. Erroneous decisions based on poor quality data not only create inconvenience, but also prove extremely costly. Inaccurate data continues accumulating, especially when no one is using it regularly. With technological advancements, several companies make decisions based on bad data without realizing its consequences.
Do companies take data entry job seriously?
Companies make numerous efforts and invest millions of dollars in deriving and developing data management strategies. But usually these strategies are developed in and around incurring robust data management software, observing daily data transactions to reduce data redundancy, security of data in form of backup and overall storage as well.
Similarly, hefty dollars are spent to accrue extensive technology for data warehouse, data mart, and data mining. It helps them conveniently access the data, though inaccurate; anytime and anywhere. But all these developmental moves are made in and around increasing the efficiency of collecting data, and secured storage of data. No measures are taken to eliminate data entry errors and improve the accuracy of data and organizations are struggling to find answers to:
What is data entry error?
Information entered in the wrong way or order. It is common like typing words rather than numerical data or numbers rather than words. Common data entry mistakes are transcription errors & transposition errors.
How to avoid common data entry errors?
- Prioritize accuracy over speed
- Double-check all data entries
- Use software tools to automate as many processes
- Train employees on the importance of accurate data
- Provide good working environment that promotes focus
- Hire sufficient workforce to avoid overloading your staff
What is accuracy in data entry?
Accuracy in data entry refers to if the value entered and stored in a field is correct. To be correct, a data value entered must be correct and should be represented in a consistent and unambiguous form.
How to improve data entry accuracy?
- Identify primary sources of inaccuracies
- Perform regular analysis
- Standardize processes
- Monitor progress
- Enable automation
Why is accuracy so important?
If data accuracy levels are low, insights will be inaccurate, and the decisions made to use it will yield poor results. This is why organizations should realize that data quality is more important that data quantity. Too much focus only on gathering as much information as possible, without thinking about if it’s correct and how it can be used – will never help.
to increase business efficiency.
Why is data accuracy important for overall data quality?
While investing in data processing, a lot of firms now are prioritizing the most important element collection and accuracy. Accuracy describes the degree to which given data is correct, which is used to glean actionable insights. This is the reason organizations consider data quality over data quantity. Too much of focus only on collecting data without thinking about its quality will not help them succeed in todays data driven markets.
Companies with data, as their best asset, can improve their everyday decision making. Now if decisions are made with help of data with poor accuracy, the insights will be lacking and the decisions it will impact will be disastrous. This is not applicable only to the top management; instead it applies to everyone from bottom to the top.
The second reason is inaccuracies resulting in poor data quality are a barrier for organizations to take that leap towards digital transformation. Every 8 out of 10 Machine learning (ML) and artificial intelligence (AI) for modern data governance projects are withheld due to dirty or low quality data. 96% of these projects have run into data quality problems and data labeling required to train AI.
Data entry errors cost millions of dollars in time & efficiency to organizations
Data entry done without appropriate skills and adequate technological tools leads to errors. These errors may seem small but can cost organizations millions of dollars, time and efficiency to correct.
5 common data entry errors that affects businesses:
- Ambiguous data
- Value representation consistency
- Change-induced inconsistencies
- Valid values
- Missing values
Tips to improve your data entry accuracy
Data entry errors make achievement of 100 percent data accuracy a herculean task for most of the companies. Errorless data entry and accurate data always has a bottom-up approach. Data management best practices can help organizations to identify inaccurate data; but not all the errors and inaccuracies are identified and rectified. The organization always has to ensure that the data entered is accurate and is readily available as and when required.
Impromptu data improvements with immediate payoffs are no rocket science, however keeping the business database away from inaccuracies and maintaining that stature is a long-term process that needs dedication. Also, organizations are equipped to fix only a small fragment of what they found due to time and skill impediments. Outsourced data entry companies equipped with required skills, experience and technology knowhow can rapidly transform vast volumes of your critical data into reliable business information.
to avoid data entry errors?