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These benefits save businesses time and money and give them better access to more accurate data.
Data entry is a cumbersome process for businesses in every industry. While necessary, it often represents a disproportionate amount of the time and resources spent running a business. As a result, a number of software platforms have been created over the years to improve data entry processes, make it easier to organize data, and facilitate exporting data between these platforms.
The missing piece in data entry software platforms is automation. Until recently, data entry couldn’t be automated in a way that was accurate and versatile, with systems that could pull data from multiple document types and consistently input that data where it belongs. Machine learning data entry tools leverage the power of AI to do exactly this. Platforms can be trained to capture and organize data the same way a data entry clerk can, freeing up human workers’ time to do more meaningful work.
To explore more about machine learning-powered AI data entry tools, we will dive into:
- Benefits of automated data entry
- How machine learning data entry works
- Integration capabilities
- Common business cases
Let’s get into the details of how machine learning data entry can improve your business processes.
Benefits of Automated Data Entry
Data automation with machine learning provides a number of benefits that businesses in every industry can appreciate:
Benefits of Machine-Learning Automated Data Entry
| Reduced Labor Cost | By reducing overall data entry/data capture workloads, labor costs can be saved on labor hours spent on data entry. |
| Improved Accuracy | AI tools, especially when enhanced with machine learning capabilities, improve the reliability and consistency of data entry by reducing the risk of human error. |
| Faster Data Capture and Organization | Machine learning data entry tools can capture and organize data far faster than human data entry clerks, giving businesses earlier access to necessary information. |
| Image and Blueprint Capture | Machine learning optical character recognition(OCR) technology provides the unique benefit of indexing, organizing, and capturing data from scanned images like receipts, blueprints, engineering drawings, and more. These tools can learn not only where to find the right information, but also where to store it, which represents the largest leap in automated data entry technology in history. |
| Easy Exporting | High-quality machine learning data automation platforms are able to export data seamlessly to existing software, allowing automated data entry to fit seamlessly into existing business operations. |
| More Searchable & Readable Data | Automated data entry tools provide clean, readable dashboards with convenient search filters to help users find and analyze specific data from a large volume of stored data. |
These benefits save businesses time and money and give them better access to more accurate data.
How Machine Learning Data Entry Works
Machine learning data entry automation works by creating tools that learn where to find necessary data, where to store it, and how to organize it or export it to make it accessible to end users. There are a number of technologies integrated into AI tools that deal with multiple document types, and all of them can work in concert to store data from multiple sources in one centralized database.
Data Capture Technology
The three processes leveraged in machine learning data entry tools are optical character recognition (OCR), machine learning (ML) document mapping, and robotic process automation (RPA). While not all tools leverage all three, more complicated business cases require tools with all three.
OCR and ML document mapping are leveraged to index alphanumerical data from images and text documents, identify what data exists in each location on the document, and what to do with it. This provides much of the basic functionality of automated data entry, which is finding, reading, and inputting data from various document types.
For more complex data entry projects that require multiple lookups and reviews, RPA for automating data entry. RPA is used in conjunction with machine learning and replaces multiple manual processes.
Data Storage and Organization
Once the ML data platform learns how to find and identify different data points, index that data, and translate it into alphanumeric characters, it then learns what to do with it. This is done during the onboarding phase, and some tools include data storage and organization actively, and others can be integrated into an existing software stack.
Depending on which is right for your business, machine learning data entry tools will either store and organize the data they collect into their own database or export it to your existing analytics software.
ML Data Entry Integration Capabilities
Many firms adopting machine learning data entry tools opt for fully purpose-built data platforms, centralizing all data processes with a single software tool. While this is a great option for firms looking to completely overhaul their data processes, many businesses are too large or complex to swap their entire software stack for a single, new tool.
Luckily, ML data entry platforms can easily be integrated into existing software stacks, and automated data exporting makes adoption seamless. This means that businesses are able to keep their existing data organizational tools, allowing them to read the information the same way they did when it was manually entered.
Common Business Cases
Machine learning data entry can automate the capture and organization of data from a wide variety of documents, including the following documents depending on the industry:
| Industry | Applications |
|---|---|
| Logistics |
|
| Healthcare |
|
| Municipalities |
|
| Manufacturing |
|
While many more applications for ML data entry exist, the above examples are some of the most common applications we at iTech have seen with the clients we serve. Essentially any documents from which a business currently has human staff entering manually can be automated with machine-learning enhanced AI tools. This doesn’t necessitate the replacement of data staff, but it does drastically reduce their workloads, allowing them to spend more time auditing data for accuracy and analyzing it to equip their company with insights that they may not have had the time to derive otherwise.
In many industries, especially mid-size and smaller firms in said industries, it is often high-skill employees getting bogged down with data-related tasks that can be automated by machine learning. For these use cases, increased production and quality of output end up replacing reduced labor cost as the primary benefit to ML data entry.
Adopt Machine Learning Data Entry Software with iTech
While many of the benefits we have outlined in this guide are inherent to machine learning data entry generally, not all tools are created equal in terms of data accuracy, integration capabilities, and ease of use. Just as software platforms aren’t created equal, their developers and support staff aren’t either. We at iTech pride ourselves on our top-of-the-line onboarding experience and ongoing support, with 24/7 access to support personnel and senior account managers.
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IDS Commander iTech2021
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