Automation: A Guide to Improve Data Capture Accuracy
iTech Data Services

Automation: A Guide to Improve Data Capture Accuracy

10Jul
Read Time: 6 minutes

The accuracy of the data on which companies base their judgments is only as good as the data itself.

The data collection process provides a significant amount of a company’s business intelligence. This process is particularly useful in businesses like freight billing and auditing, insurance and banking, and back-office services like accounts payables, accounts receivables, Human Resources, invoicing, and sales and purchase order processing.

With the recent technological advancements, Automation holds substantial promise in Data Capture accuracy.

Read on to know how Automation can optimize Data Capture among businesses.

Challenges in the Current Data Capture Processes

Essentially, data capture is the process of transferring data from a physical to a digital format. Manual data capture can quickly become difficult, drab, and mind-numbing, resulting in several issues.

The following are the primary issues in current data capture processes:

Divided Focus on Core Business Tasks

Because manual data entry can be tedious and time-consuming, employees are left with no time to focus on their core business tasks.

High Costs

In manual data capture, employees must get trained correctly, which costs both time and money. Thus, manual data capture can incur high costs and can be challenging to do consistently.

Increased Error Rate

Regrettably, when individuals don’t get trained properly, misunderstand remarks, or cannot read forms accurately, there can be an upsurge in errors, thereby lowering the brand’s credibility, and ultimately, having a significant impact on internal operations and customer satisfaction.

Increase in the Amount of Data Capture

Any unexpected rise in the amount of data that needs to get captured could increase errors. This increase could be due to a surge in sales or the establishment of a new location. In-house data capture teams may also get strained as a result of this.

Misinterpretation

When people manually do data collection, there is always the risk of ambiguities and misinterpretations, which can ultimately affect data quality, and the decisions are undertaken based on them.

Strained Quality Control

Quality control is a crucial part of manual data collection, and it must be handled and carried out with caution to guarantee that the data is accurate. Manual data capture is vulnerable to basic human errors, scalability challenges, employee turnover, and continual training since it relies entirely on humans and human decision-making.

Time Consuming

Despite how quickly one can process or type data, manually entering data takes a long time and can be difficult at times. This time drain can cause people to lose focus and waste more time, ultimately leading to a delay in data availability.

However, with the advancement of technology and the exponential growth of automation, manual data entry is becoming less and less necessary.

Here’s how to ensure accuracy of data entry

Automation reduces, and in some instances, even eliminates the challenges mentioned above, but it must get implemented properly. Following that, here are some suggestions for automating data collecting processes:

Concentrate on Data That Is Useful

Data accuracy, relevance, readability, accessibility, completeness, and timeliness, are important factors in evaluating business data quality. Among these factors, data accuracy gets frequently regarded as the most crucial of these characteristics to attain.

Nonetheless, according to one study, American businesses now feel that 32 percent of their data is inaccurate, up from 25 percent in 2014. In other words, in the vast majority of cases, the problem is escalating.

Data experts will have their job cut out to solve the problem and reach a more acceptable data capture accuracy rate. Data values, for example, must reflect “the right value” while still representing unambiguous forms. Moreover, businesses will face the well-known data quality problem known as “GIGO” – garbage in, garbage out – unless they take corrective action.

Examine and Analyze Errors

Increasing the frequency and quality of quality checks is a simple method that businesses may undertake. The technique gets based on the idea that if no quality checks get performed in the future, every piece of data will degrade in quality.

Increasing the number of checks, in addition to the frequency, can pay off big time. Clients love knowing that incorrect data will not contaminate their bright new business systems, even if the result set contains zero records. To put it another way, measure twice and cut once.

Standardize Processes

Companies must verify results regularly, regardless of the data capture accuracy rate they choose. Sight verification, double-key data capture verification, field validation, program adjustments, and post-processing reports are all actionable data capture accuracy verification methods.

Moreover, operators who collect data must understand what constitutes an error and when an error becomes an error. Application interpretation, field, and keystroke errors are all common sorts of errors.

Finally, data accuracy targets should get expressed as a percentage of total data collection. Whereas many businesses use lower percentages, typically set at 90% or less, to make achieving their data accuracy targets easier, companies can and should use greater data capture accuracy criteria.

Introduce Smart Automation tools like Machine Learning

Poor data acquisition is estimated to cost $14.2 million per year, according to one study. Aside from data predictions, AI, particularly Machine Learning, aids in data quality improvement by automating the data acquisition process through intelligent capture. Smart automation ensures that all relevant data gets captured and that the system is error-free.

In essence, Machine Learning can collect data without the need for manual intervention. Employees can skip administrative tasks and focus more on the customer if the most crucial facts get gathered automatically.

Here are some ways of how Machine Learning can help with data capture accuracy:

Detect and Identify Duplicate Records

Redundant and duplicate data entries can result in out-of-date records, resulting in poor data quality. You can use AI as ML to reduce duplicate records in a database and maintain precise golden keys. Without sophisticated techniques, identifying and removing repeated entries in a large company’s database is challenging. Intelligent technologies that can detect and remove duplicate keys can help an organization combat this.

A good example of AI implementation has intelligent features turned on by default to guarantee that contacts, leads, and business accounts are clean and free of duplicate entries.

Discover Anomalies

A minor human error can significantly impact the usefulness and quality of data in a CRM. An AI-enabled system can detect and eliminate flaws in a system. Moreover, you can also implement anomalies based on machine learning to improve data quality.

Include Third-party Data

AI can increase data quality by contributing to it, in addition to correcting and maintaining data integrity. By offering better and more complete data, third-party businesses and governmental units can considerably improve the quality of a management system and MDM platforms, allowing for more accurate and informed decision-making. Furthermore, AI gives recommendations on what to extract from a given set of data and how to integrate them.

Ultimately, when a business has all of its data in one location, it has significantly better chances of making well-informed decisions.

Observe and Provide Feedback

Whenever business leaders investigate what creates data inaccuracies in their firm, they should not be surprised if a long list of external and internal causes of errors emerges. Here are three of the most common sources they are most likely to come across:

  • Data Movement: Data Movement occurs when data gets altered wrong as it transfers from one database or system to another attributable to “disconnects” between various databases.
  • Data Decay: This includes discrepancies that develop over time as changes occur but don’t get represented in the data records. Marital status, phone numbers, and addresses are all common examples.
  • Incorrect Values: This occurs when the “wrong” value is input at the start and never changed. Even if the cause is as basic as a typographical error, data inaccuracy would still be the outcome.

Considering that recurring problems might emerge merely due to a lack of continuing maintenance and monitoring, optimizing data capture accuracy is an ongoing goal.

For illustration, if contact information data is left unmanaged for only 30 days, it is anticipated that around 2% of it will become “bad.” Now the question remains: What is the solution?

Businesses can adopt reliable data quality tools, such as the ones listed below, to help them stay ahead of the data accuracy curve:

  • Data Capture Accuracy Training: Employees must typically get trained and retrained about increasing data quality, regardless of their skill level.
  • Data Monitoring: This option contains deviations that are automatically corrected depending on pre-defined business rules and principles.
  • Data Profiling: Analyzing the data regularly.
  • Data Standardization: Ensuring that data meets the requirements of predefined data quality techniques.
  • Geocoding: Fixing name and address data with automated pattern-matching methods.
  • Matching and Linking: A data comparison that links data records that are similar but somewhat different.

The Future of Data Capture Industry with AI

As the application of AI exponentially grows by the minute, the following previews the future of the Data Capture industry:

Increased Speed of Analysis

Previously, data processing and analysis had to get done manually. These activities are done instantaneously with AI technology, allowing firms to solve problems faster and allowing data management professionals to focus on other core and more vital responsibilities.

Real-time, Streaming Analytics

Organizations will perform real-time streaming analytics to acquire up-to-date data, accurate to the second, as AI gets progressively integrated into the enterprise.

DevOps Workflows will overtake Application Development

Suggestions and recommendations will become more refined as AI and machine-learning systems learn and grow. This workflow could lead to more firms implementing AI-based DevOps workflows for application development, empowering engineers to integrate and deliver software upgrades that continuously take advantage of AI knowledge and learning.

AI will Transform all Industries

Industries will shift dramatically as AI advances and enterprises build workflows that enable them to maximize value. With AI-powered tools and other innovations, healthcare providers, banks, freight companies, Human Resources, and every other industry will be able to deliver more efficient, cost-effective services.

Conclusion

One of the most pressing difficulties that both large and small organizations face daily is the trade-off between speed and accuracy in data quality. Businesses that respond to the problem more promptly and effectively will gain a competitive advantage over their competitors. This advantage is made possible by the use of AI in data capture.

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