How data quality is impairing your machine learning efforts

Organizations have apprehended the importance of data analytics in their businesses and are looking deeper into data to gain a competitive advantage, implementing machine learning and artificial intelligence to achieve new business objectives and to move ahead of competitors in the industry.

AI and machine learning are used to discover and utilize hidden patterns in unstructured data sets effectively.

However, the adoption of AI and machine learning is critically impaired by the necessity of high-quality data, and changes must be made organization wide to identify and reduce pouches of bad data and create mechanisms that allow the organization to adapt quickly to the data needs and embrace the full potential of these technologies.

Data is the biggest challenge when employing new technologies and the impact of data-driven decisions is larger than ever before. The availability of high-quality data is paramount for technology to have the right impact.

Bad data distorts all the potential insights that could be achieved.

Owning and trusting data are two completely different things, the amounts of data available are increasingly bigger however that data is not suitable or trustable enough to make business-critical decisions with. This lack of trust in the data has a twofold impact, it either inhibits decision making, or even worse, it induces decisions based on false assumptions. This can only be solved by developing strategies to address existing and ongoing issues and implementing a methodology to execute on that strategy.

A data quality framework encompassing data quality discovery, remediation, and monitoring can help organizations approach the data quality issues that hinder the success of analytics or machine learning initiatives.

This approach allows data quality processes within the organization to be performed in a more agile and scalable way, reducing the siloed approach and manual interventions. Resulting in an overall improvement of the value returned from analytics or machine learning.

Considering that data is one of the organizations most critical assets, making data quality the basis of analytics and machine learning initiatives, leverages the value of data and unlocks new insights critical to create the necessary edge on an increasingly competitive business ecosystem.