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Self-Service Analytics goes conversational
Conversational self-service analytics allows users to obtain information by formulating requests in natural language. The blog post explores the opportunities and requirements for successfully implementing this approach based on experiences from several projects.
Conversational self-service analytics uses AI-based methods to enable users to perform data analysis through conversation, as if they were talking to a human. Software vendors like Microsoft, SAP, AWS, and Tableau are expanding their product portfolios to leverage natural language conversation, allowing users to interact with data, create reports, and perform various analysis functions.
NLP is a key building block in this approach, aiming to bridge the gap between human communication and computer understanding. There are several types of NLP systems, including rule-based, deep learning, and hybrid systems. Each has its strengths and weaknesses, but hybrid systems aim to combine the best of both worlds for more effective natural language interfaces.
Opportunities offered by conversational self-service analytics …
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The Future of Artificial Intelligence Jobs
In today's rapidly evolving world, the field of AI is making significant strides, raising concerns about job security and employment opportunities. While it's natural to worry about the impact of AI on traditional job roles, it's essential to recognize that AI will create as many, if not more, opportunities than it displaces.
The Promising Careers in AI, why they are in High Demand?
There are a few reasons:
✔️ Booming AI Market: The global AI market is valued at over $136 billion and is projected to grow by more than 13 times over the next seven years.
✔️ Business Embrace AI: A staggering 83% of companies have incorporated AI into their business plans, recognizing it as a core strategic focus.
✔️ Data Efficiency: 48% of enterprises use some form of AI for efficient data management.
✔️ Healthcare Integration: 38% of healthcare providers will use AI for diagnostic purposes
AI has …
What is an Example of Conversational AI?
The world of artificial intelligence (AI) has made remarkable strides in recent years, with conversational AI taking center stage. This technology allows computers to understand, interpret, and respond to human language in a way that is both meaningful and useful. One prime example of conversational AI in action is the NLSQL tool. In this blog post, we will delve into the features of NLSQL and how it is revolutionising the way businesses interact with their data.
NLSQL is a cutting-edge tool that enables users to interact with their databases using natural language queries. By leveraging the power of conversational AI, NLSQL simplifies data analysis and visualisation, making it accessible to users with varying levels of technical expertise. This innovative tool is designed to integrate seamlessly with popular platforms like Microsoft Teams, ensuring a smooth and secure user experience.
Key Features of NLSQL, which differentiate it from competitors:
1. Handling Natural …
How to calculate carbon footprint?
Nowadays, the need for sustainable business practices is more important than ever. Companies are increasingly being held accountable for their environmental impact, and governments are implementing additional taxation based on CO2 emissions. One innovative solution to help companies decrease their carbon footprint is through the use of Natural Language SQL (NLSQL) technology. In this blog post, we will discuss how NLSQL can be utilised to calculate carbon footprints, generate ideas for reducing emissions, and create actionable plans for a more sustainable future.
How NLSQL Can Help Companies Decrease Carbon Footprint?
NLSQL is a powerful tool that allows users to interact with databases using natural language, making it accessible to employees at all levels. By integrating NLSQL into a company's IT ecosystem, it becomes possible to calculate the carbon footprint of various operations without the need for sensitive or confidential data transfers. This ensures that the calculation process remains secure and …
Overcoming the Biggest Problems for Doctors with ICU Patients Data Accessibility through NLSQL for Healthcare
Intensive Care Units (ICUs) are critical areas in hospitals where patients with life-threatening conditions are treated. The success of patient care in ICUs depends on timely access to accurate and comprehensive data. Unfortunately, doctors often doesn’t have data analytics skills and face multiple challenges in accessing and interpreting this data. This blog post highlights biggest problems for doctors with ICU patients data accessibility:
1. Data Fragmentation
ICU patients often have multiple sources of data, including electronic health records, lab results, and imaging studies. This data is often scattered across various systems, making it difficult for doctors to have a comprehensive view of a patient's health in dynamic.
—> By integrating with existing hospital systems, NLSQL for Healthcare can consolidate information from multiple sources into a single, unified chat interface. This allows doctors to access all relevant patient data through intuitive interface, eliminating the need to navigate multiple systems.
2. Time …