Organizations often face challenges when collecting reliable and quality data. Prior to implementing strategies to improve the quality of data collection, it is imperative to identify obstacles to consistent data collection. This blog post discusses data collection challenges and how to overcome them.
Artificial Intelligence (AI) is at the heart of a fundamental shift in software engineering machine learning, powered by big data and computing infrastructure. Successful AI applications are built on three pillars: machine learning algorithms, hardware for running them, and data for training and evaluating models. In spite of algorithms and hardware becoming commodities, obtaining high-quality data at scale is still challenging.
It is generally acknowledged that data preparation takes up most of the time spent on machine learning development. Even the best machine learning algorithms cannot perform well without good data , considering how much data quality impacts model accuracy. One of the main reasons many industries are hesitant to adopt AI is a lack of data.
There are three main approaches to data collection. Data acquisition, as the term implies, deals with finding, augmenting, or creating new datasets. Before data can be stored, cleaned, preprocessed, or used for other mechanisms, it must first be acquired from pertinent sources. It involves finding suitable business data, converting it into the necessary business form, and loading it into the designated system.
Data must be gathered from a variety of sources including databases, files, and external repositories in this crucial first step. It’s essential to clearly state the issue you hope to address with an ML model before beginning the data collection process. A structured approach to data collection allows you to clearly see all the data that is available, needed, and missing.
Data labeling is the informative annotation of data so that a machine learning model can learn from them. Because labeling is costly, there are several techniques to use including semi-supervised learning, crowdsourcing, and weak supervision. If you already have data, it is possible to improve it rather than acquiring or labeling from scratch.
The Fundamentals of Data Collection
Collecting only High-quality Required Information
The first step is to decide what information you want to gather. You must decide what topics the information will cover, who you will collect it from, and how much data you require. Whether you’re creating a dataset from scratch or reusing existing ones, it’s best to use high-quality data from the start, Good planning and interrogating your dataset can help you detect issues earlier.
Prepare a Strategy and Methods
During this stage, you will select the method that will serve as the basis for your data collection strategy. You should consider the type, timeframe, and other factors when choosing the best collection method.
Begin Collecting Data
After you’ve defined the data requirements, you’ll begin collecting data.
Determine whether there are any existing datasets that you can reuse as a starting point. You may also need to create a new dataset using an in-house data collection tool. Be sure to follow your plan and keep an eye on how it’s going. Setting a schedule for monitoring your data collection may be helpful, especially for continuous collection.
It is important to track and maintain the accuracy, completeness, relevance, timeliness, and accessibility of the data.
Control Privacy & Security
A sensitive dataset could be required for training ML models, so a product’s privacy and security must be protected and maintained. Information management and data security play a big role in this, considering your dataset may contain sensitive personal data. Remember to properly and precisely balance your information management and data quality.
Correctly labeled data is fundamental in building an effective supervised ML system. Careful consideration of your labelers and the tools they will use helps ensure the accuracy of your labels.
People are more likely to label data correctly if they understand what you’re asking them to label and why, and if they have the tools to do so effectively. Training and testing your labelers’ ability to complete the labeling task responsibly ensures the quality of your data.
Conduct research with your labelers ahead of time to improve task design and instruction clarity. You should test the task with a small group of labelers before launching it fully.
Data Collection Challenges
Lack of Reliable Data
Accurate and reliable data can be extremely difficult to come by. According to a McKinsey survey, 24 of the 100 businesses that have implemented AI initiatives in their operations have encountered this challenge, making it the biggest obstacle for businesses to overcome when implementing AI. In fact, a lack of appropriate data has forced many businesses to postpone or halt their AI implementation efforts. In spite of significant investments in data infrastructure and management systems, a lack of high-quality data can produce subpar insights and forecasts. A lack of useful data can prevent businesses from properly training their AI algorithms.
Dealing with Big Data
Big data environments typically contain large amounts of structured, unstructured, and semistructured data. Information that has been formatted and transformed into a well-defined data model is referred to as Structured Data. Semi-structured data is data that has consistent and distinct characteristics. It does not require a rigid structure like that of relational databases. Unstructured data does not have a predefined organizational form or format, so it is essentially anything that is not structured or semi-structured.
Unstructured data is far more diverse and can provide more detailed insights. It can now be analyzed and used in a variety of ways to benefit a business thanks to new technology such as AI, ML, and computer vision. By 2025, unstructured data is expected to grow to 175 zettabytes (175 billion terabytes).
This complicates the initial data collection and processing stages. Furthermore, data scientists frequently need to filter raw datasets stored in a data lake for specific analytics applications.
The data might be selected from larger datasets that don’t appropriately convey the message of the wider dataset. Data might be derived from older information that might have been the result of human bias, or perhaps there are some issues with the way that data is collected or generated that results in a final biased outcome.
In recent years, it has been discovered that human prejudices contaminate algorithms and data through AI. A 2015 study showed that Amazon’s algorithm for shortlisting resumes was biased against women. It was trained based on resume submissions over the last decade, most of which were from men.
Even though everyone wants to minimize or eliminate bias from their datasets, it is much easier said than done. Biased data can be affected by several factors, unbalanced data being one. Unbalanced datasets significantly hinder the performance of machine learning models. It contains an overrepresentation of data from one community or group while unnecessarily reducing the representation of another.
Improving the Data Collection Process
Maintaining quality, and authenticity of data is vital. If these requirements are not met, we may face incorrect results from AI models.
Let’s discuss some processes that can be implemented at the organizational level, to improve data collection practices.
Removing Duplicates & Anomalies
Duplicate or redundant data is a major issue for organizations because it can compromise quality and raise questions about the source of truth. AI quickly finds duplicates and sorts data according to timestamps and other factors. When it comes to identifying patterns, associations, and uncommon occurrences in a dataset, machine learning programs are notably effective. This can be helpful in a variety of real-world scenarios.
The quality of the data can be significantly impacted by the use of appropriate metrics. These metrics orKey Performance Indicators (KPI) can focus on a variety of data quality issues such as completeness (the proportion of records with a recorded value) and precision (how many records have a meaningful or valid value). Establishing reasonable KPIs aims to improve data quality without creating unfavorable incentives that could compromise data quality.
Automating Data Capture
The management of unstructured data, one of the main causes of business pain and inefficiency, is addressed by automated data capture. Automated data capture systems are a hardware and software setup that automates the data entry process.
Automating data capture can improve data gathering efficiency to speed up the utilization of the information and data that businesses require. Data volumes are increasing, making manual processes more complicated and expensive. By eliminating the repetitive tasks of manual data entry, adding automacy frees up staff to concentrate on other tasks and lowers labor costs. For high ROI data, you can utilize systems that are already gathering data and build models.
Integration of Data Lineage
Data lineage tracing has two components: meta-data and the original data itself. Effective data governance requires data and meta-data traceability as a fundamental component. Any data governance tool on the market today must have the ability to track the lineage of meta-data, making it simpler to store and navigate through datasets and fields with a few clicks rather than requiring data experts to search through documents, databases, and even programs.
Data-centric AI has become more influential in recent years, the primary goal of which is to improve data pre-processing for better model accuracy rather than the model training algorithm.
Good data collection practices are essential in developing high-performing predictive models. The data must be error-free and contain information relevant to the task at hand.