Machine Learning has the tendency to evoke extreme reactions “ some consider it a superpower while for others, machine learning is just another fad. That said, research has shown that 76% of enterprises have prioritized AI and Machine Learning initiatives over other IT projects.
One in every 10 enterprises now uses multiple AI applications in the form of chatbots, fraud analysis, etc. It’s important to note though that Machine Learning can play a critical role in automating business processes only as long as certain prerequisites are met.
Operational Prerequisites
For Machine Learning models to be successful, the program must suit the context and it must meet certain criteria. Let’s look at some of the basics.
First, There’s The Scope Of The Project To Be Considered
Machine Learning excels at finding patterns in databases and detecting insights from these patterns. This makes it suitable for projects with specific goals. For example, it helps assess the sentiment behind product reviews and uncovering market trends.
Machine Learning can also be quite effective at predicting stock prices. But if the project scope is too wide or the datasets are too big, Machine Learning may become very expensive and time-consuming to maintain.
Manual Biases Can Also Manifest A Negative Impact
Though Machine Learning models may not be explicitly programmed, in some cases manual biases can be built-in. For example, when being used to recruit people, a rule may be entered to exclude candidates from a particular pin code. This may result in weeding out candidates who would have been the perfect fit.
Most Importantly “ Data Quality
GIGO- Garbage In Garbage Out is a term that’s as relevant as it was when first coined as it is today. Machine Learning is data-intensive and the quality of data inputted into Machine Learning models is what determines its chances for success.
The problem worsens when it is combined with manual biases. For example, a candidate may have made a mistake entering his pin code and maybe mistakenly removed from the candidate pool.
Understanding The Vulnerability Of Machine Learning To Poor Data Quality
Machine Learning can be explained as a way for computers to learn how to detect subtle patterns in data without being explicitly programmed on how to do so. The algorithms learn by working on large training data sets and adjusting their internal parameters until they can identify new patterns. The nature of how it works makes Machine Learning models acutely sensitive to data quality. Even a small error can lead to large scale errors.
Bad data can make Machine Learning tools quite useless. The trouble with data compounds when you take into account the fact that Machine Learning models often use diverse forms of data collected from multiple sources. Poor data not only leads to skewed results, but it also makes Machine Learning models take longer time to process data and can make them a financial liability.
Getting Data Ready For Machine Learning Models
Working backwards to understand where the quality error lies once the Machine Learning model has made predictions is extremely difficult. As data becomes more complex, a survey found that data workers are spending more time cleaning data as compared to gaining insights from it. Hence, any and every Machine Learning project needs to start with assessing and improving the input data quality. The data quality assessment cycle is one that must be repeated regularly. Here’s what you need to do.
Verify Data
The first step of getting data ready for training models is to make sure the data is accurate and valid. Take the example mentioned above of the candidate entering the wrong pin code. Data verification would involve checking these details against the address entered and alerting the system as to the discrepancy. In other cases, it would involve comparing the data to be used against data stored in third-party databases to verify it.
Assess Relevancy
It may seem like having a lot of data is a good idea but sometimes, having a finite data set is much better than an infinite one. So, you need to make sure that the data you’re using in the Machine Learning models is relevant to the project.
Complete Data Aspects
Completeness is another critical aspect of data quality. The data records being used for a Machine Learning model must be complete. For example, if you’re trying to assess consumer behavior by demographics, you need to have complete customer records. Addresses must be complete with all street details as well as pin codes. If you’re inputting names, all records must have a first and a last name.
Remove Duplicates
Given that data can be entered through multiple touchpoints, there’s a high risk of duplicates entering the system. Duplicates can have a negative effect on the data analysis results. Data verification and completion helps minimize this risk to a great extent.
However, the records must be checked separately for duplicates as well. Once duplicates are identified, the records must be merged or the duplicate record must be removed to maintain good input data quality.
Final Thoughts
When it comes to Machine Learning, it’s important to note that a learning style that works in one setting may not be as efficient in another.
The efficacy of these models depends on giving the model concrete goals and good quality data sets. Start small with Machine Learning, design algorithms that are specific to each use case and make efforts to improve data quality before the machine Learning models start using it.
With good quality data comes good quality, reliable results. There is no one-size-fits-all approach to improving data quality but once the parameters are defined, the process can be tailored to the project. Most importantly, the effort must be proactive, not reactive. A systemic approach is always the best way to evaluate data quality.
According to a survey, the global market revenue for Machine Learning is set to be valued at US $1.07 billion by 2025. With the right approach, your business can start benefitting from it too.
