A Guide to Implementing Machine Learning for Data Capture
iTech Data Services

Capture and Data Cleaning in Machine Learning Technology – A Guide to Implementing Machine Learning for Data Capture

03Sep
Read Time: 3 minutes

Machine learning (ML) is transforming the technology world in many ways thanks to ML’s ability to learn and improve its algorithm over time. This form of artificial intelligence (AI) technology is already prompting major advances in the realm of data cleaning in machine learning. 

Accurate data capture, followed by data cleaning and unification is essential whether you’re a scientist analyzing a data set or a Fortune 500 company looking to maintain accurate data for use in analytics and decision-making down the road. Data cleaning and unification entails the identification of errors and anomalies, which are then corrected or addressed in a prescribed manner. 

Is Your Project Right for Data Capture and Machine Learning Data Cleaning and Unification? 

By using ML-powered data capture and leveraging machine learning for data cleaning and unification, you can expect lower costs, higher data capture accuracy levels and faster project turnaround times. Of course not every project is well-suited to machine learning-powered data capture technology, so the first step is to determine which data capture method is best for your needs, data type, budget and accuracy requirements.

Generally, the projects that are well-suited to machine learning data cleaning and unification technology involve: 

  • Large volumes of data;
  • A requirement for extreme accuracy; and
  • A need for rapid turnaround.

It should be noted that machine learning-powered data cleaning and unification software can handle all types of data: highly-structured, semi-structured or unstructured data types. The latter two data types can be challenging to process with traditional OCR technology. 

By using machine learning for data cleaning and unification, costs are far more predictable too since humans are largely removed from the equation. Over time, as the machine learning algorithm is refined to suit your data, human intervention may be eliminated entirely. 

Finding the Right Outsourcing Partner for a Machine Learning Data Capture Project

Once you’ve determined that machine learning data cleaning and unification technology is right for your needs, it’s time to find the right outsourcing partner to meet your needs. Outsourcing is ideal for this sort of project, as most companies and organizations do not have the need, time or budget to develop a custom machine learning OCR platform.

You’ll need to seek out a reputable outsourcing partner with a well-proven machine learning OCR data capture product. You should also seek out an outsourcing partner with SOC II certifications and experience performing work on projects that are similar in nature to your needs. 

It’s critical that you select an outsourcing partner who fully understands your needs because this will allow them to provide an accurate time frame, cost and other realistic service level agreement (SLA) expectations. 

Clearly Communicating Your Needs in a Machine Learning Data Cleaning and Unification Project

A machine learning algorithm is only as good as its programming, so you’ll need to take some time to consider your data capture needs; only then can you clearly convey those needs to an outsourcing partner. 

Machine learning data capture, cleaning and unification technology is “trained” to perform tasks in a way that’s similar to how you’d train a human, so it may be helpful to consider the instructions that you would provide to a human data entry specialist. A few questions to consider: 

  • What is the formatting and structure of the data?
  • How should “normal” data be handled?
  • Is there any data that may be difficult to “read” or interpret?  
  • How should any “abnormal” or questionable data be handled? 

By considering the answers to these questions, you’ll be better prepared to clearly communicate what data you need captured, how that data should be processed and what types of modifications may be necessary in order to unify that data and maximize accuracy. This sort of information is critical for your outsourcing partner because they’ll be empowered to send your project on a path to success. 

Monitoring and Fine-Turning Machine Learning for Data Capture, Cleaning and Unification

Machine learning algorithms are designed to self-improve over time, resulting in increased efficiency and accuracy. That said, some degree of data quality monitoring is prudent because no algorithm can get it 100% right 100% of the time. 

By periodically evaluating data quality, you’ll have opportunities to make small tweaks and adjustments to the machine learning algorithm. For this reason, it’s important to collaborate with your outsourcing partners to schedule regular spot checks and evaluations of data quality. 

Maximizing Your Chances of a Successful Machine Learning Data Capture Project

Embarking upon a large-scale data capture project can seem daunting, but partnering with a reputable outsourcing firm that’s experienced in this sort of project will maximize your chances of success. They’ll guide you through the process of verifying that your data capture needs are well-suited to their machine learning technologies. Then, they’ll work with your organization to gain a good understanding of your needs and requirements. Once the data capture, data cleaning and data unification process gets underway, you’ll be engaged to take part in periodic evaluations to verify that the results align with your needs and expectations.

iTech specializes in providing smart machine learning-powered data capture automation and outsourced data entry services. If you have questions surrounding iTech’s services or would like a demonstration of our automation solutions please fill out the below form, or email us at info@iTechData.AI.


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