We are surrounded by huge amounts of data. Data is everywhere and is gaining huge importance and relevance in today’s world.

There are many firms that are performing tasks of gathering, retrieving and managing data. This requires systems that can help us handle that much amount of data.

Machine Learning has helped us in gathering and managing data, but poor data quality has a massive impact on machine learning, with numerous experts pointing to data as the differentiator for AI and ML

What is Machine Learning?

Machine Learning is a field of Artificial Intelligence that helps in making machines self-dependent in decision-making. With machine learning, they get the abilities of reasoning, learning from past experience and self-learning.

Now, coming back to data, let us discuss why data is so important.

Significance of Data Quality for Machine Learning

Whatever industry you work for, you may have come around a huge chunk of data.

This data may have helped you in making your decisions, predicting future behaviour from past trends and many more. So you cannot ignore the importance of data for sure.

For example in business, analysis of data has helped organizations to grow and improve their sales to many folds.

Data scientists collect, relate and analyze the relevant data. By doing so they are in a position where they can analyze all the aspects before coming to any decision.

Machine learning as a service has helped organizations handle tons of data.

Moreover, data is helping organizations to bridge the gap between customers and organizations.

The organization has a deep insight into consumer demands, their likes and dislikes.

This further helps in improving the sales of the organizations.

Why is Quality Data Necessary?

Data is broadly classified into two types.

Structured data and unstructured data.

Unstructured data is the data that is understandable by humans.

On the other hand, machines understand structured data.

The aim should be to create high-quality data that can help machines to generate desired results. As we all know that machine learning is hugely dependent on data, so it becomes necessary that the data provided must be of good quality.

What Affects the Quality of Data?

The relevant question: what is the cause of poor data quality?

There may be several reasons that cause the quality of data to deteriorate. For example:

• Improper Data Entry: This is one of the reasons for getting poor-quality data. Humans commit mistakes in data entry that affect the final results when results are obtained from this data
• Duplicate Records: Duplicacy in the records also deteriorates the quality of data. If the same entity is recorded multiple times, this creates a deviation in the results.
• Compatibility Issues: In the older days the systems were not that complex. But this is not the case in today’s scenario. Today systems are more complex. If we migrate data from older systems to newer systems, it is prone to errors.

Machine Learning applications are highly dependent on data.

So, only high-quality data should be fed to the system. Thus by pattern recognition algorithm they can predict future outcomes or performance. Let us discuss how poor-quality data can impact machine learning:

• Data Selection: This is the first step in feeding the data to the machine learning algorithm. The data selected should be relevant. The amount of data may be less but the quality of data should be good. If not done properly it impacts your output results to a great extent. So always keep in mind to get the data from reliable sources. Also, you must be well aware of the scope of the data.
• Deviation from Results: In machine learning applications, the importance of good quality data is nowhere less than the algorithms. This is because your algorithms are of no use if the data fed is of poor quality. This makes the output of the machine learning business application deviate to a huge percentage.  It may sound harsh but your machine learning applications are useless if data is not correct.
• Increase in Cost of Production: Poor-quality data increases the cost of production. This is because poor-quality data needs to be processed, cleaned and filtered before feeding into the machine learning models. As these algorithms take a huge amount of data, it may take a lot of time and resources to clean up the data and make data ready for feeding. This increases the cost of production of the machine learning application.

How can we Improve Data Quality:

Having discussed the impact of poor data quality in machine learning, the question arises: How we can improve the quality of our data?

The quality of data can be improved by some of the following techniques:

• Use Applications to Clean Data: There are many applications that can help to clean the data. All you need to choose the application according to your needs and situation. This data is then fed to the machine-learning business applications.
• Data from Reliable Sources: There are numerous sources from where you can collect your data. Always try to get data from reliable and authentic sources only.
• Update Your Data Frequently: Day-by-day data is becoming obsolete. For example addresses of persons, age, marital status etc.Due to this data is becoming obsolete. So there is a constant need to update the data frequently.

Conclusion

Recognize the indispensable role of data quality in the realm of AI and ML. Uncover why data quality is essential for driving successful AI and ML initiatives in our whitepaper.

In the end, we can say that good quality data is of utmost importance for machine learning products to function properly.

Bad quality data can hugely deteriorate the performance of machine learning business applications as data is the key point of any business.

Machines are trained from historical data to check insights and make decisions for enterprises.

It can lead to huge losses when improper data is fed to the machines. An example could be the stock market, where even a decimal mistake can lead to a decision of investing in the wrong stocks.

Reporting bias has the power to skew perceptions and decisions