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By the end of 2025, the world will potentially generate 180 zettabytes of data, an increase of 129% over 2021’s 79 zettabytes.
Given such numbers, collecting and storing data is no longer enough. Businesses must also extract relevant information and identify patterns automatically from it.
Automated data capture that incorporates machine learning is the answer to complex datasets that carry a treasure trove of valuable information. It allows businesses to draw critical insights, predict trends, mitigate risks, and drive profits.
Compared to traditional data capture methods, such as manual data entry, file transfer, or scanning, machine learning data extraction offers increased speed, accuracy, flexibility, and cost-effectiveness.
At the same time, implementing an ML based automated data capture system requires careful consideration of business goals, requirements, and organizational barriers.
This comprehensive guide will show you the optimal ways to implement a machine learning model for data capture.
Read along!
Machine learning is a subset of artificial intelligence that uses data and algorithms to improve a software or computer system's accuracy, predictability, and recall. A well-trained ML algorithm recognizes trends and patterns across large datasets and uses its knowledge to perform specific tasks and make predictions. It also uncovers key insights that increase an organization's decision-making prowess, improving key growth metrics and profitability.
Machine learning for data capture is one of the many use cases of ML, which involves extracting data from various sources such as texts, images, and videos using machine learning algorithms. Companies that deal with high volumes of structured and unstructured documents, such as loan applications, invoices, agreements, and legal contracts, can incorporate machine learning data extraction.
By doing so, they avoid data errors and attain maximum accuracy while deriving more strategic value via insights and analytics. The ML algorithms are trained to capture relevant information and identify patterns in large and complex datasets.
These algorithms extract data using numerous techniques, such as scraping, NLP, image, speech, video recognition, and optical character recognition (OCR). All in all, the method you use for ML based data capture depends on the type of your data and its use case.
By implementing ML-based automated data capture, organizations can collect and analyze large amounts of data accurately and gain a competitive advantage via predictive analytics. At the same time, planning implementation requires careful consideration of the following factors.
Before getting on with the implementation, ensure that you have understood the existing bottlenecks in your system. Conduct a thorough workflow analysis to understand them and your business’s priorities. For example, the existing priority for a real estate company might be predicting property values, and for a lending company, it could be a risk assessment.
Recognizing these aspects contributes to a sound implementation strategy where you know exactly what data to enter and decide the rules and format accordingly.
Identify structured and unstructured data sources, such as transaction details, bank statements, invoices, applications, etc., to understand the requirements and resources for data extraction and preprocessing.
Metrics such as accuracy, precision, and recall help you evaluate the performance of your ML algorithm. By establishing these metrics, you can ensure the project aligns with your business goals.
Data preparation transforms raw data into a machine-readable format for further processing and analysis. Dirty data negatively impacts the performance of machine learning models. It leads to issues such as overfitting, model biases, and poor sentiment analysis that affect customer satisfaction.
Preprocessing ensures the cohesion of entry types, making them suitable for a machine learning model while increasing accuracy and efficiency.
The following are the most critical stages in data preparation:
This technique involves removing or handling missing or erroneous data, such as duplicates, missing values, or outliers. For instance, if the number of bedrooms is missing from a property listing, you could impute the values by using the median value of the number of bedrooms for similar properties in the dataset.
This process involves transforming the data into a standard format to make it appear similar across all records and fields.
An example is scaling data such as age and income levels. The former (age) has values in years, and the latter (income levels) has values in dollars. Through standard normalization techniques such as min-max scaling, z-score normalization, or log-transformations, you can scale the features to a range of 0 to 1.
Data labeling is the stage where you can assign labels or tags to a dataset. These labels are used to train an ML model to recognize patterns and make predictions based on new or unseen data.
For example, suppose an insurance company wants to build an ML model that can automatically classify claims and assign them to the appropriate claims adjuster. In that case, they can do so by labeling the data with tags that identify the type of claim. E.g., medical claim or car accident.
It involves selecting, extracting, and transforming relevant features from the data to enhance the accuracy and performance of the model.
For example, age, gender, policy information, and marital status are relevant features to predict the likelihood of a customer making policy claims.
Depending on the nature of your problem and goals, select appropriate ML techniques, such as unsupervised, supervised, or semi-supervised algorithms. While unsupervised algorithms use unlabeled data that is unstructured and unprocessed, supervised algorithms require clean and labeled data. On the other hand, semi-supervised algorithms are used when the dataset contains both labeled and unlabeled data.
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To determine whether a supervised, unsupervised, or semi-supervised model is the best for your data capture goals, consider the following:
Take stock of the availability of labeled data such as credit score, income, and employment status that accurately represent target variables. Examples of target variables include the chances of loan default or the price of a house.
Supervised learning is the best choice if your system has large amounts of labeled data because the model predicts outputs based on the input features using labeled training data.
Consider the availability of resources in your organization. Models that fall under supervised learning require significant human intervention. You must ensure that you have the necessary expertise to support data labeling.
A combination of supervised and unsupervised learning, aka semi-supervised learning, is ideal for those using a small amount of labeled data and a large amount of unlabeled data. It is beneficial where labeling large amounts of data is costly and time-consuming.
Take into account the data capture use case of your organization. While supervised learning models help with pricing predictions and customer churn rates, unsupervised learning is ideal for anomalies such as fraud detection.
The complexity of your data directly impacts the performance of an ML model. Different ML models handle different levels of complexity, and the wrong model leads to inaccurate predictions and poor performance.
If the data is highly dimensional and you have a dataset of housing prices that includes variables such as location, size, number of bedrooms, etc., then dimensional reduction can help reduce the complexity of the data.
Consider the model's scalability to ensure better performance on larger datasets and a growing number of inputs. Unsupervised learning models are easier to scale than supervised models as they do not require labeled data. They also need fewer resources and expertise when it comes to integration.
After preparing the data and selecting the learning algorithm, it is time to execute the following critical stages for implementation.
Improving and optimizing ML models for best performance and accuracy often precedes actual training. Using techniques such as feature engineering and hyperparameter tuning, you can enhance the accuracy and performance of a model while controlling various aspects of the learning process, such as the complexity, the learning rate, regularization, and so on.
In this stage, you train the selected ML algorithm using the preprocessed data. It involves feeding the data and adjusting the model’s parameters using deep learning, random forest, clustering, etc.
After training, the next step is to evaluate the model’s precision. It is the stage where you split the data into training and test sets. Techniques include cross-validation, confusion matrix, and sensitivity analysis.
After rigorous testing, the best-performing models undergo preparation for deployment. They enter a production environment to learn more from live data. It also involves integrating the trained model into a larger system, such as a web or mobile application or your CRM.
The deployment process depends on various factors, such as the available data, hardware resources, and DevOps processes in the deployment environment. Take into account performance, scalability, data traffic, security, and version control for optimal deployment.
ML models must be continuously monitored and tested post-deployment to ensure accurate predictions and optimal performance. They are liable to degradation over time due to changes in the input and output variables. Continuous monitoring exposes issues such as model drifts and training-serving skew.
Data capture and collection implications are significant, especially when sensitive information is involved. It is essential to ensure that you have consent for data collection and that your system complies with data privacy regulations such as the SEC and CCPA. Also, look for SOC certification in your machine learning system.
So long as you can understand the logic and reasoning behind a model’s predictions, you can call it interpretable. Building an interpretable model entails training with simple features. It is also necessary to document the various preprocessing and training steps to monitor its inner workings.
A model that can be understood, particularly by non-experts, is likely to be trusted and adopted by stakeholders. The model must generate explanations for its predictions or recommendations and be transparent in its decision-making process.
To prevent biases, ensure that your training data is clean and comprehensive. A model trained with ethical considerations overcomes personal assumptions, prejudices, and human subjectivities. If the historical data used to train an ML system includes discriminatory lending practices against certain groups, the system may learn to replicate these biases in its lending decisions.
Before integration, it is essential to thoroughly assess an organization’s legacy system and identify compatibility issues and potential risks.
Integration with legacy systems is challenging for numerous reasons, such as data incompatibility, a lack of standardization, security risks, and resistance to change.
Adopting middleware is often a solution to bridge the gap. Another way is to start small with the implementation before rolling it out across the organization. You can encourage adoption and ownership by demonstrating the practical benefits of the new system, communicating your plan, and letting people take charge of the workflow design.
Data capture will continue to play a critical role across industries for predictive analysis and strategic insights. The more data we successfully capture, the higher our chances of building high-performing ML models with greater accuracy.
At the same time, ensuring clean and comprehensive data is essential. For this, human intervention will remain pivotal, and we must pay utmost attention to preprocessing.
With significant decisions, such as loan approvals, risk assessments, and medical diagnoses, riding on the precision of ML models, ensuring that the data does not carry our subjective biases is more critical than ever.
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