Automating manual data entry is an imperative for businesses not just for improved efficiencies and turnaround times, but for maintaining critical accuracy standards.
A Malaysian student went on a multi-million shopping spree for 11 months, splurging 2.7m (AU$4.6 million) on designer handbags, clothes, jewelry, mobile phones and much more – all from luxury brands including Chanel, Cartier and Christian Dior.
The trigger was an unlimited, though accidental overdraft she received from Westpac – Australias largest bank. The bank soon realized its gaffe caused by a data entry error but the damage had been done. It was faced with a non-recoverable dent, as all the charges against the student were dropped. In a similar ruling earlier a man charged with fraud for withdrawing $2.1 million was evicted. Here again the culprit was a data entry error by the bank.
These two examples then are not unique, and represent an obvious challenge with data collection. But the problem is far wider reaching, affecting all elements of the business and totting up to a huge 3.1tn in unnecessary business expense in the US alone.
Data entry errors affect the profitability and brand reputation
Data entry errors can disturb every single data management effort of an organization, adversely affecting its profitability and brand reputation. Priorities to core business activities, lack of skilled professionals or minimal exposure to advanced tools and technology are some impediments in ensuring quality data. This makes data entry service providers a much preferred option for businesses.
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5 Sources of Data Entry Errors & Quick Win Solutions
1. Data entry error due to ambiguous data
John’s birth-date is May 19, 1974. If a personnel database has a BIRTH_DATE format which accepts data in USA format, data entered as 05/19/1974 is correct. A date entered as 05/18/1974 would be inaccurate because it is the wrong value. A date entered as 19/05/1974 also would be wrong because it is a European representation instead of USA representation. Here, reviewer will be unsure if the date entered is invalid or just erroneously represented.
A leading Data Management Solutions Provider in Texas, USA, used the Reasonableness Test for processing insurance claims. A macro alert signals the operator when a value for date of birth entered is not in a format normally entered or as per the pre-defined format.
While entering collected data, The alert not just sends a signal that things are not in order but also asks the data entry operator whether he or she would want to continue. This not just paced up processing of 120,000 insurance claims but also increased the data accuracy manifold. The key element here is perfect benchmarking to compare predefined date formats and the format of the date entered which can give a seamless process with high accuracy levels.
2. Data entry error due to value representation consistency
If two values are both correct and unambiguous, it creates problems for reviewers. The data value entered as habile data and HabileData, both refer to the same company.
However, because both the records are inconsistent, they result in data inaccuracy.
Data Validation and Completeness Check, ensures that the data entered in a specific field is in the correct format. If the data entry professional enters text instead of a number or a field is left blank, or the value entered has a blank space; data validation check point would produce an error and alert the data entry operator and the quality check personnel. It also keeps a check that no fields are empty and no values were missed.
3. Data entry error due to missing values
Blank fields can be accurate but not valid. For admission forms in the education industry, if the field COLLEGE_LAST_ATTENDED is left blank, it can be accurate; as the applicant might have never attended a college. But if a BIRTH_DATE field is left blank, it can never be accurate as all applicants certainly will have birth dates. The field COLLEGE_LAST_ATTENDED left blank will be inaccurate and invalid, if the applicant has attended the college.
With a turnaround time of just 24 hours for every batch of records, 4,271,710 student records comprising of career codes and respective scores were entered with utmost accuracy for creating a centralized repository for a University in USA.
So how was it accomplished without errors of missing values?
Encoding a Value for NULL helped in identifying blank fields. To maintain the accuracy of data, an optional data element should be allowed to encode a value for NULL. Data entry experts create a support system of macros containing keywords to take care of empty fields keeping in mind the queries and COUNT, GROUPBY, and other commands that formulate results easily & correctly. It will compel the data entry professional to go back to source data to find out the correct value that needs to be entered in the field.
4. Data entry error due to valid values
For medical records used for insurance claims, if a data field is used to store the color of person’s “eyes”, the value of “hair” would certainly be invalid. The value brown as Peter’s eye color, though valid, is inaccurate; as his real eye color is blue.
Completeness and Validity Check will consider a value “valid”, only if it is a part of a collection of possible accurate values represented in an unambiguous and consistent way. One small challenge here is that the validity check macro nowhere can identify if the value is accurate, and not all values are accurate because they are valid. For a value to be accurate it must be a correct value which can be determine through data cleansing.
5. Data entry error due to change-induced inconsistencies
A car manufacturing company has a color field designed specifically to accept customer preferences in red, blue and yellow color – only. However, a market strategy compels them to make necessary changes in their system to record up to 30 different colors, all of which are variations of red, blue and yellow. Also because it is a new range of cars, none of the old records were required to be updated. It is obvious that this database will suffer inconsistent data representation of colors; that certainly will cross lines with old records at some point of time.
Completeness and Validity Check helps in such scenarios, too. However, the challenge as mentioned earlier, about identifying if the value is accurate or not, persists here as well.
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Leverage RPA to Improve Data Entry Accuracy
The Internet is flooded with resources about transcription & transposition errors, and advice to use “double check” methods and improve working environments to improve accuracy. However, scope of these process standardization measures remains limited.
Robotic Process Automation (RPA) can make business processes smarter without fundamental process redesign. It leverages machine learning capabilities, and artificial Intelligence to enhance manual data entry processes, ensuring greater accuracy.
Templetised and trained bots are programmed to perform routine yet critical business processes by mimicking the way data professionals interact with applications and following simple rules to make decisions. The bots extract information from images stored in the forms processing servers, process that data and transfer it to respective APIs, designated databases, predefined user interfaces, or into legacy systems. Data entry can be performed by software robots with very little human interaction, typically to manage exceptions.
How intervention of bots enriches manual data entry
So, what are the specific benefits that RPA offers?
- RPA enabled work flows, tools and software bots do away with repetitive work resulting in enhanced productivity.
- Free up a lot of man hours from structured and dreary tasks which can be utilized for other productive work.
- Tools and bots support automated error reports which raise a red flag for erroneous data entered.
- Conformity with robust data entry standards such as geo-coding, matching, data monitoring, data profiling and linking to improve the overall quality of the data entered.
- Increased efficiency and reduced errors translate to cost savings and viable operations.
Data management best practices may help organizations identify inaccurate data – but not all of them, and fix only a small fragment of what they found. Immediate improvements in data accuracy can be done in short time with considerable payoff – but getting business databases to low levels of inaccuracies and keeping it that way is a long-term process.
ML and RPA have been widely accepted by industries across verticals to do away with laborious and manual data entry processes. RPA and ML is enabling enterprises big time with superior data entry solutions to constantly enter, maintain, and manage more accurate and valuable business data.
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