How to make your AI work with quality data?

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Quality data and AI feed off each other and are essential to the success of any business. Find out how in this blog.
The vast amount and diversity of data within and outside organizations requires a readiness effort to implement digital initiatives, move quickly, and respond to changes in business strategy and processes. At the same time, new tools such as Artificial Intelligence and Machine Learning have enormous potential to improve data quality within companies . How do data and these technologies mutually benefit from making sound decisions and empowering business?





98 % of global executives agree that AI models will play a nurse database role in their organization’s strategies in the next 3 to 5 years.

Source: Accenture



Artificial Intelligence, Machine Learning and the importance of Data Quality
Artificial intelligence (AI) and machine learning (ML) are technologies that support many new business solutions such as customer satisfaction tracking, operational efficiency, and new product development.

These processes require not only large amounts and variety of data to be trained but also quality inputs so that artificial intelligence systems can make more accurate predictions, provide relevant recommendations and automate processes effectively.

The information used must come from the right systems and comply with the relevant standards and policies. Without a solid foundation of data management, AI will be incomprehensible and unreliable.

In this way, data and AI are fundamental drivers for business operation and success:

AI can improve the outcomes of multiple business processes with new ways to incorporate data and AI into daily operations constantly emerging.

Incorporating AI into operations reduces costs and increases productivity.

Quality data and AI enable improved company audits, reducing risk and improving regulatory compliance.

Integrating AI into business and operations engineering enables reinvented workflows, improved customer interactions, product development, and a better employee experience.




A survey of data analysts found that poor data quality has several impacts on their AI and ML projects: 38% reported that their projects took longer, 36% that their projects were more expensive, and 33% said they did not achieve the intended results.

Source: Datanami



Quality data and artificial intelligence feed each other
The success of AI depends on the availability of reliable and timely data and intelligent information management that enables:

Quickly determine the appropriate features for the model to be trained.

Automatically transform data to meet the needs of the AI ​​model (feature extension, standardization, etc.).