Expert Consultation On The Importance Of Data Quality For Machine Learning

By Author: ExpertsConsult
Total Articles: 26
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Machine Learning is an area that requires high data quality, as this sector is very data sensitive. A expert network firm say that this world is dependent on data and digitalization. The world of digitalization is anticipated to double in at least every two years.
Industry experts define machine learning as an area of advanced technology and disruptive innovation, which enables the quality analysis of massive amounts of data. It delivers faster and more accurate results to identify profitable opportunities for the business.
Experts analysis reveals that machine learning is the primary driver of growing digitalization across the globe. It is foreseen to accumulate investments of approx. USD58 Bn by 2021.

Why Is Quality Data Crucial?

Data quality is crucial for every industry. It helps the decision-makers to have confidence in their outputs and enhance their productivity. It enables the industry professionals to spend more time on achieving their primary goals instead of spending time on validating data and eradicating errors. It even boost the efficiency of marketing. Expert consultation reveals that even small errors in data can lead to large scale errors in the output of the decision-makers.
The primary role of a machine learning expert comprises checking the consistency, compatibility, accuracy, and timeliness of data before executing the project.
Expert survey shows that 15% of the organizations across the globe are using advanced machine learning while 20% of the Global expert network say that they have adopted machine learning as part of their core operation. The deployment of machine learning into the business comprises of various challenges including lack of access to data, shortage of skilled workforce to address the challenges of their business.
North America region comprises of the highest adoption rate of machine learning in 2019. Every business is expected to lose approx. 20% of their revenue due to the poor quality of data delivery.

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