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Organisations are scrambling to use tools like ChatGPT to take data analytics to the next level.

Read on to discover the capabilities of new natural language processing AI– and the pitfalls.

ChatGPT Capabilities

Analysing unstructured data

NLP tools can process large amounts of unstructured data quickly, unlike traditional analytics tools that rely on structured data formats. With NLP, companies can extract valuable insights from unstructured data like text and speech. By analysing language patterns, NLP can identify trends, sentiments, and hidden insights.

For instance, let's consider a company analysing customer feedback from social media. Human analysts manually read and interpret each comment. Instead, Chat GPT efficiently processes customer feedback, providing fast, valuable insight in a fraction of the time.

Realtime informed decisions

In today's fast-paced business landscape, quick decision-making is crucial. NLP tools enable real-time data analysis, empowering companies to make informed decisions instantly.

Predictive analytics

Companies can harness the power of NLP to analyse news articles, social media and other relevant content within its industry. Identifying emerging trends and allowing businesses to gain foresight into potential future market conditions. Armed with this valuable information, businesses can make data-driven decisions about product development, marketing, and business planning.

Data analytics accessibility 

Lastly, NLP allows non-technical users to analyse data more easily, enabling a broader range of individuals to leverage data analytics effectively. Unlike existing data tools requiring extensive technical expertise. 


Chat GPT Pitfalls

Data bias

The effectiveness of tools like ChatGPT relies heavily on the quality of the training data. If the data used to train the model contains biases, the output generated by the model may also reflect those preconceptions, leading to unfair or misleading outcomes when utilising the data for decision-making processes.

Data privacy 

When businesses employ NLP to analyse data, safeguarding data privacy becomes paramount. It is crucial to implement robust security measures to protect sensitive or confidential information.

Transparency challenges 

Systems like ChatGPT are complex. It's hard for people to understand the technical processes behind their conclusions, and difficult to identify and resolve output issues. Organisations need to implement measures that enable easy explanation and interpretation of NLP or AI models they employ to instil confidence in the results they generate.

In conclusion

As the technology evolves, explainable AI and machine learning should make NLPs more transparent and boost confidence. We’ll see more and more organisations adopting them to analyse unstructured data, create personalised customer experiences, and drive better decision-making in real-time. NLPs look set to become an integral part of data analytics by streamlining existing processes, offering insight into future market conditions and improving accessibility for non-technical users.

Boston Hale is a specialist recruiter in the data and analytics sector. If you are looking for your next role in data analytics or looking to hire for your team, get in touch today with one of our experienced consultants.

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