In machine learning, there’s a good old saying, which goes as follows: garbage in, garbage out. In the era of big data, this has become an underlying statement for anyone working with data.
Simply put, if you feed your model with bad, low-quality data, it’s nothing more than junk for the algorithm. Such a model will produce unclear, inaccurate analytics and unreliable results. All the work done on your AI project would be in vain. Therefore, the quality of labeled training data has a profound effect on how effectively a machine learning task will run and what results it will deliver.
However, the research states that the quality of data in their organizations has to be improved, according to 91% of tech decision-makers, who also claim (77%) that they don’t have confidence in the business data their companies use. The corporate sector is gravely concerned about data quality.
The truth is that global industries are getting more and more dependent on data, which is why there’s a vast amount of tools and techniques they use to verify the quality of their data. They are considered to be more efficient than general quality tests. Yet, not all these methods can address data issues in machine learning, like overlapping classes or noisy labels.
Data scientists may spend less time debugging the machine learning pipeline to enhance model performance by assessing data quality. Using intelligently defined metrics and creating matching transformation operations to solve the quality gaps is the optimal solution.
With that said, let’s take a look at the basics of assessing data quality for machine learning and big data analytics, as well as the key tools used to automate the process. While we provide a more succinct overview of this topic, we also recommend you read the article on quality data expertise.
Quality Data: An Elusive Dream?
If the quality of your data is insufficient, your organization’s decision-making process, customer happiness, and execution plan can be severely compromised. More specifically, the accuracy, difficulty, and efficiency of all the data-dependent tasks, including machine learning and deep learning, can be significantly impacted by poor data quality.
To enable seamless integration into model development, there are several techniques and tools available for assessing data quality. However, the majority of data quality solutions only allow the evaluation of data sources at specific times. Thus, it’s the user’s responsibility to arrange and automate. The automation of this process per se requires several crucial steps. They include gathering data from various sources, monitoring data quality, and properly addressing any issues with the data quality.
Assessment of data quality is necessary to ascertain if the data are of the proper type and of sufficient quality to support the system’s objectives. Functional components must be provided at all levels, including data sources, collection, encoding, transfer, data verification, reviewing, and processing, in order to produce quality data.
In this article, we’ll talk about automated approaches to data quality evaluation and various tools used for this procedure. Their main purpose is to:
- Assess the sources of the data;
- Verify the quality of gathered data;
- Create a plan of action to improve the system and data.
Data Quality Challenges in Big Data and Machine Learning
Before we delve into the topic, it’s important to look at the main issues in data quality one often deals with in machine learning and big data. This will help us better comprehend the value of the data quality assessment process.
Big Data
As the data volumes across the industry grow fast, it becomes harder to control the quality of such data. The quality of big data itself is frequently cited as the primary challenge for big data.
Cloud deployments, data heterogeneity, and streaming data all provide additional difficulties. Numerous data management solutions have been proposed under the general heading of NoSQL to satisfy the storage and retrieval requirements of various big data applications. There are several data structures and query languages available in NoSQL systems. However, the scalable performance of the existing NoSQL systems is prioritized over the quality of the data.
More and more companies are adopting big data-driven, sophisticated, and real-time analytics for both tactical and overarching decision-making. The basis for such projects, particularly for predictive and prescriptive analytics, are machine learning algorithms. Not only are these two concepts intertwined by internal processes, but also by the main obstacle to the deployment of sophisticated analytics. That is, poor data quality.
Machine Learning
Machine learning provides a distinct set of data quality issues than big data does. Model representation, methods for evaluating model accuracy, and model optimization make up most ML algorithms. Thus, data quality assessment for machine learning applications is a challenging undertaking given that these three elements are intricately interwoven.
The raw data is often an unsuitable format for machine learning tasks, especially in supervised learning. It can be, however, examined for variables and characteristics, which are extracted afterward. In addition, it’s necessary to develop general patterns that may be used to recognize these features. In this case, data quality problems are shown up as duplicate data, outliers, highly correlated variables, many variables, and missing data.
So, building advanced machine learning models and big data applications may both be significantly hampered by low-quality data. And so assessing data quality is an obligatory step in achieving high-performing models and trustworthy analytics.
Key Dimensions of Data Quality to Consider
Source: A Short Review of the Literature on Automatic Data Quality
Data are assessed for quality according to a predefined set of criteria. These criteria are dependent on the business context of the data, including the type of information it represents, its urgency, and its intended use. To do data quality evaluation, it’s best to employ both bespoke data quality rules and data quality dimensions.
The collected data quality is evaluated to:
- Make sure the information retrieved matches the field’s legitimacy and contains all six characteristics of the data quality dimension;
- Manage, capture, report, and process good data to examine the dataset system’s capacity;
- To develop and put into action strategies that would improve data collection and management at all levels.
All these factors considered, data quality assessment is becoming more automated. Compared to the current data quality models, the automatic approach eliminates the requirement for subjectivity-related human intervention in assuring data quality. Real-time, well-defined automated system procedures are used to ensure that all the established data quality characteristics and dimensions are met.
Here’s the list of standard parameters that are employed to assess the data quality for efficient machine learning applications:
- Class Overlap
- Data Labeling Accuracy
- Class Parity
- Feature Relevance
- Data Homogeneity
- Data Fairness
- Correlation Detection
- Data Completeness
- Outlier Detection
- Data Duplicates
Now, let’s see what automation options are used to assess whether the data we use is good and of the highest quality.
What Are the Automatic Data Quality Assessment Tools?
Tools that enable data quality automation must provide dynamic measurements, reports, and infographics. The following is a list of some of the most used tools for evaluating the quality of today’s mass volumes of business data.
- IBM Infosphere Information Server for Data Quality
This solution delivers end-to-end tools for checking the quality of data. They may be used to clean, standardize, match, and preserve data lineage. In addition, they can be applied to continually analyze and monitor the data’s quality.
- IBM Infosphere Information BigQuality
This program has been designed specifically for data audits on Hadoop (open-source software framework) systems. For Hadoop data, this solution delivers features for full integration and governance.
- IBM Infosphere Information Quality Stage
For situations like business intelligence, data warehousing, application migration, and master data management initiatives, the solution aids in managing data quality for big data applications.
- Amazon’s Deequ
The tool is used in-house at Amazon to check the quality of sizable production datasets. Every time a dataset is updated, the tool computes data quality metrics, validates the quality restrictions, and uploads the datasets to users. Problems with data quality do not spread to consumer data pipelines.
- SAP Information Steward
With this tool, you can develop and execute data validation rules, as well as the scorecard-based monitoring of data quality. Additionally, it offers the ability to catalog metadata across their system landscape and analyze the relationships between their enterprise data.
- SAS Data Quality
Data quality issues may be resolved without having to transfer the data to a new platform using this tool. It enables quality validation to simplify the task of problem detection and profiling, preview data, and establish repeatable procedures to maintain high data quality.
- Talend Data Fabric
This option covers data integration, integrity, and governance on a single, cohesive platform. Every user receives the data they want thanks to the Talend Trust Score, which offers a concise picture of data in terms of its quality, relevance, and popularity.
- Qualdo
The data quality assessment option to examine data from a variety of angles that are thought to be crucial for operations involving downstream data. Data analytics teams can stay constantly on top of data quality thanks to this solution.
- Informatica Data Quality
Without using IT, this tool facilitates the development and testing of data business rules. It delivers top-quality data by effectively standardizing, validating, enriching, removing duplicate data, and consolidating data.
Final Thoughts
Summing up, data quality assessment is a fundamental business asset for the effective management of corporate data and its quality. An automatic approach to this process works for different data quality dimensions and significantly cuts down the time needed to prepare the data. In addition, this enhances the quality of training data and, thus, increases the performance of big data and machine learning applications.
As an alternative, businesses can turn to data pros who can improve the quality of their data manually. One such company is https://labelyourdata.com/, which specializes in secure and quality data annotation services to help clients prepare their data for various machine learning tasks.
The significance of high-quality data for developing effective and reliable AI systems cannot be overstated. Tools that help with data quality assessment can speed up the model creation and deployment process, improve productivity overall, and improve the data preparation phase.
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