While humans are striving to ace new technologies world over, another set of humans are set at developing new technologies by the minute. With every development, the study of what worked well and what did not is what is leading us on. Machine learning and data quality are two such interrelated fields.
Day after day, machine learning is becoming an important function in several business sectors. Machine learning programs run on data and the need for large amounts of data to train the machine like a well-oiled engine is more than ever. But more than large amounts of data, good data quality is crucial to get the desired end result.
Data management deals with data quality, which is what makes the output given by analytical applications authentic. Analytical applications give businesses an insight into their standing in the industry. The current analytical advancements being made in the tech industry are remarkable but as far as data quality is concerned, it is not up to the mark which is potentially damaging for a business that depends on a machine learning program.
More Data, Clean Data
Machine learning systems need more data, but where is the data? If we take the example of the retail industry, data can be collected over multiple years. Once the data is extracted and collected, the quality of it should be determined. It is a machine learning engineer’s job to do that, to put the data in a comprehendible context from a business point of view.
Responsibilities of A Machine Learning Engineer
The engineer’s first responsibility should be to understand the needs of their clients and their customer base. This implies a business should work with a machine learning consultant first who will make a guide on how machine learning should be used to fit the particular business model. Next, the machine learning engineer will begin to process the data from the system to label and categorize the data with the help of a domain expert. This is where the problem lies. Most machine learning projects are undertaken in the absence of a domain expert. This results in the faulty categorization of the data, operator error, or mistaken assumptions about the output given by the machine learning system.
Machine learning engineers dedicate most of their time sorting the data from the inception, so if the machine learning product gives incorrect data at the beginning, the incorrectness will compound ever since. This results in unsupervised machine learning.
Supervised & Unsupervised Machine Learning
Supervised Machine Learning refers to the process of using examples of pairs of input/output to map a function to its corresponding item. With such models, the performance can be measured from the start with the assurance of zero data error.
Unsupervised machine learning contradicts this. It has no data labels and no actual way to measure the performance of the algorithm. With such programs, the goal is to find out the underlying structure of the data and split it into various categories. But there is a plus to unsupervised machine learning. These algorithms have the ability to see patterns in the data that humans may not be familiar with. So while choosing a machine learning approach, it is important to understand the purpose for which is it is being used in the business.
Data Quality matters for machine learning. Unsupervised machine learning is a savior when the desired quality of data is missing to reach the requirements of the business. It is capable of delivering precise business insights by evaluating data for AI-based programs. But there is a no-size-fits-all solution for a business.
(This is a slightly modified version of an article originally published in Analytics insight. The original article can be found at https://www.analyticsinsight.net/how-important-is-data-quality-in-machine-learning/)