Too Long; Didn't Read
One of the biggest challenges businesses face today is making sense of the vast amount of data they collect. Cybersecurity and bad data remain two other prominent areas of concern.
According to a report by IBM/Ponemon Institute, the data breaches in 2022 costed organizations across the globe whopping penalties summing to a total of 4.35 million. Moreover, another report by IBM substantiates that the annual cost of grappling with the odds of bad data in the US alone is $3.1 trillion.
Data engineering and machine learning not only facilitate making more informed and inferential business decisions with large volumes of big data that businesses generate but also help in data structuring and data cleansing so that the data is more secure and compliant to InfoSec compliances like GDPR, CCPA, SOC2, ISO27001, etc. and less vulnerable to cyberattacks. Thus, organizations that use data engineering and machine learning cultivate more resolute customer intimacy frameworks by delivering holistically better value to the customers through their services or solutions and optimizing their revenue and omnichannel brand reputation at the same time.