10 trends to foster growth based on AI, Data and Analytics
Posted: Tue Jan 21, 2025 5:20 am
The exponential growth of artificial intelligence (AI), together with the diversification of its applications, heralds the arrival of a new era in terms of business productivity.
Traditional AI and generative AI in particular have great potential to substantially improve data management.
Despite the constant evolution of this technology, the lack of data quality controls and the low traceability of information can cause a negative effect, affecting the reliability of the results.
How can organizations boost their development based on artificial intelligence, data and data analytics ? We tell you in this article.
Artificial Intelligence: A Powerful and Challenging Tool
Increased efficiency, optimised uruguay phone number lead decision-making and increased productivity. These are just some of the many benefits that artificial intelligence brings to businesses. However, achieving these advantages is not without risks.
The lack of traceability and quality assessment of data used by AI poses a huge danger to both organizations and society, since unverified—or partially verified—information can compromise the credibility of records.
This scenario highlights the need to find a model that, in addition to guaranteeing the volume, speed and variety of data, also ensures the validity and value of the information.
Trends to foster growth based on AI, Data and Analytics
To overcome this challenge, ensuring that all data is trustworthy and has real value in the AI economy, it is not enough to know the current guidelines for AI .
It is also essential to know which AI, analytics and data trends will prevail in order to understand how to incorporate them into business practices.
Hybrid artificial intelligence will make up for the lack of maturity of the different AIs
Contrary to popular belief, generative AI will not replace all of the previous tools that this technology provided.
Far from disappearing, mature AI can expand its reach and fill the gap in generative AI development, especially when it comes to fraud analytics and customer churn analysis.
To take full advantage of its potential, organizations must establish the ultimate goal before starting to implement AI. That is, the problem to be solved.
They also need to avoid making generative AI the entire budget. There are other valuable methods of using this technology for analytics, such as machine learning.
Finally, it is essential for data analytics teams to monitor all ongoing AI initiatives. Only then can they be sure that they are consistent with the rest of the projects underway.
Generative AI will improve the data consumer experience
Just as there are ultra-specialized profiles, there are also people who seek to obtain quick answers, but do not have the time or skills necessary to carry out in-depth analyses.
At this point, generative AI will serve as a source of knowledge. It will augment visualizations and automatically generated insights optimized with natural language explanations.
To facilitate this process, companies are encouraged to look for ways to achieve greater impact with AI-integrated analytics initiatives.
They can also use embedded analytics, alerting, and application automation technologies to generate context-aware insights and visualizations.
Traditional AI and generative AI in particular have great potential to substantially improve data management.
Despite the constant evolution of this technology, the lack of data quality controls and the low traceability of information can cause a negative effect, affecting the reliability of the results.
How can organizations boost their development based on artificial intelligence, data and data analytics ? We tell you in this article.
Artificial Intelligence: A Powerful and Challenging Tool
Increased efficiency, optimised uruguay phone number lead decision-making and increased productivity. These are just some of the many benefits that artificial intelligence brings to businesses. However, achieving these advantages is not without risks.
The lack of traceability and quality assessment of data used by AI poses a huge danger to both organizations and society, since unverified—or partially verified—information can compromise the credibility of records.
This scenario highlights the need to find a model that, in addition to guaranteeing the volume, speed and variety of data, also ensures the validity and value of the information.
Trends to foster growth based on AI, Data and Analytics
To overcome this challenge, ensuring that all data is trustworthy and has real value in the AI economy, it is not enough to know the current guidelines for AI .
It is also essential to know which AI, analytics and data trends will prevail in order to understand how to incorporate them into business practices.
Hybrid artificial intelligence will make up for the lack of maturity of the different AIs
Contrary to popular belief, generative AI will not replace all of the previous tools that this technology provided.
Far from disappearing, mature AI can expand its reach and fill the gap in generative AI development, especially when it comes to fraud analytics and customer churn analysis.
To take full advantage of its potential, organizations must establish the ultimate goal before starting to implement AI. That is, the problem to be solved.
They also need to avoid making generative AI the entire budget. There are other valuable methods of using this technology for analytics, such as machine learning.
Finally, it is essential for data analytics teams to monitor all ongoing AI initiatives. Only then can they be sure that they are consistent with the rest of the projects underway.
Generative AI will improve the data consumer experience
Just as there are ultra-specialized profiles, there are also people who seek to obtain quick answers, but do not have the time or skills necessary to carry out in-depth analyses.
At this point, generative AI will serve as a source of knowledge. It will augment visualizations and automatically generated insights optimized with natural language explanations.
To facilitate this process, companies are encouraged to look for ways to achieve greater impact with AI-integrated analytics initiatives.
They can also use embedded analytics, alerting, and application automation technologies to generate context-aware insights and visualizations.