As data becomes a more integral element of every business operation, the quality of data collected, stored, and consumed during business processes will influence the level of success achieved today and in the future. When it comes to machine learning, nothing is possible without data. Any machine learning project's odds of success are inextricably linked to the quality of the data employed.
ML models require a tremendous amount of data to train themselves like a well-oiled engine. However, good data quality is more critical than enormous amounts of data in achieving the intended result. As a result, boosting the quality of your data is a significant opportunity.
What is Data Quality?
“We are surrounded by data, but starved for insights” – Dean Abbott, Co-founder and Chief Data Scientist at SmarterHQ
Data quality is a metric that assesses the state of data based on criteria including completeness, consistency, reliability, and whether or not it is fresh. Measuring data quality levels can assist companies in identifying and assessing data mistakes. Data quality management is an integral part of the overall data management process. Efforts to improve data quality are frequently linked to data governance activities, which try to guarantee that data is formatted and can be used across an organization. When collected data falls short of the company's validity, completeness, and consistency standards, it can have a significant negative impact on customer service, employee productivity, and essential strategies.
Why is Data Quality Important in Machine Learning?
In the age of Artificial Intelligence and Machine Learning, data quality is crucial. For any machine learning model to attain high performance. AI/ML is used in real-world scenarios in various industries, including transportation, healthcare, security, and banking. Machine learning is used in all of them for data analysis and prediction, and the success of these industries is strongly reliant on data quality.
Corporations are becoming more data-driven these days. The initial step in any machine learning project should be to assess and improve data quality.
Consistency, performance, compatibility, completeness, timeliness, and duplicate or corrupted records are all checked during the data quality monitoring process. The more good data a machine learning algorithm has, the faster and better it can create results.
Difference Between Good Data and Bad Data
To make quality predictions, a good model requires equally good data. Having stated that, the question remains as to what constitutes good data for a problem and how we can ensure data quality. So, let's take a look at the differences between good and bad data.
The problem is that there is a lot of bad data out there. You will see it everywhere you go. The poor quality data can be a result of human error, multiple points of entry, staff turnover, or your technology might need to be updated so that it can detect errors and duplicates in entries to convert it to good data.
Ways to Achieve Maximum Data Quality
Following are some of the Machine Learning Techniques that help achieve maximum data quality:
Clustering helps you create a structure out of a collection of unlabeled data. It divides data into groups (clusters) based on similarities and differences. Clustering is used to find unique groupings in an unlabeled dataset, and users are expected to determine what constitutes a "right" cluster for clustering results to match their expectations.
Many clustering methods exist, including KNN, K-means, and others. The two high-performance algorithms are DBSCAN (density-based spatial clustering of applications with noise) and HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise).
Association Rule Mining
It's a machine learning approach that finds hidden connections in large datasets that commonly occur together. This algorithm is often used to find patterns and correlations in databases, whether transactional, relational or otherwise.
Compared to most machine learning algorithms that analyze quantitative data, association mining can cope with non-numeric, categorical data, requiring more actions than simply counting. One typical example is Market Basket Analysis.
Apart from the techniques mentioned above, there are some basic steps to get good data:
- Create data quality criteria to help you decide what data to keep, discard, and fix.
- A data collection strategy must be in place. Determine the kind of data you'll need to achieve your objectives and the techniques you'll use to gather and manage it.
- You should also devise a strategy for integrating and disseminating your data across your organization's many departments. Because duplicating data, manually modifying it, or exporting it to multiple software platforms generates opportunities to change it, data quality issues are common at this stage.
- Documenting data quality issues can help in preventing mistakes in the future.
Challenges in Maintaining Data Quality
Following are some of the challenges you would face in maintaining data quality:
- Manually cleansing training or production data at the scale necessary for a typical ML project is nearly impossible.
Data isn't fixed. On a moment-by-moment basis, it changes and grows. In each scenario, the apparent difficulty is to efficiently query heterogeneous data sources, then extract and transform data into one or more data models.
- The non-obvious challenge is detecting data issues early on, which in most cases are also unknown to the data owners.
- Some errors can seep into massive databases or data lakes even with tight monitoring. When data is sent at a high rate, the situation becomes much more overwhelming. Column titles can be deceptive, formatting issues might arise, and spelling errors can go unnoticed. Such ambiguous data can introduce multiple weaknesses in reporting and analytics.
- When there is missing data or an insufficient number of specific predicted values in a data set, it is called data sparsity. Machine learning algorithms' performance and capacity to compute correct predictions can be affected by data sparsity.
- Machine learning datasets are generally interconnected. Several data interdependency scenarios can affect model performance, such as changing the data distribution of one or more features, removing one or more features, or adding one or more features. This makes it difficult to have consistent model performance because a change in data due to the factors mentioned here can cause the model's performance to be unanticipated.
- Due to a lack of data in the early stages of model building, low-value data that contributes only a small amount to overall model performance is included. And, as time passes, one forgets to take this data out of the model. Apart from the cost of maintaining/collecting the low-value dataset, this influences model performance, i.e., the model fails to generalize for unknown data. If the data distribution of the low-valued dataset changes, the model's performance suffers, and debugging becomes difficult.
How to Overcome Data Quality Challenges?
- At regular intervals, evaluate the low-valued data set, calculate its impact on overall model performance, and delete it from the training data set.
- The regularisation process also aids in ensuring that only the most essential features are included in the model. In addition, data interdependencies can be assessed using model interpretability methodologies.
- The problem of data sparsity can be handled by removing sparse features from the model or using a model that could be robust to sparse features. The entropy-weighted k-means algorithm, for example, is more suitable for this task than the standard k-means algorithm.
In this blog, you learned that not just more data but more diversified, comprehensive data is required to solve more complicated problems. With this comes a slew of new quality issues. Data quality is an issue that cannot be avoided by any firm that wants to participate in the machine learning revolution that is already affecting many aspects of today's business landscape. Use data quality management best practices to get a good result, such as using data profiling to determine the frequency and distribution of data values in your data set and creating data quality dashboards and reports. Once your reports are created, set up threshold-based alerts to ensure you are alerted to new incoming quality issues as soon as they arise.