You may not realise it, but data is a big part of modern life. No matter whether you consume data through things like weather forecasts, generate data through the ways you interact with the world, or interpret data through things like budgets and reports, data is an unavoidable part of modern life.
In the world of big data, specialists such as data analysts and data scientists are crucial, helping to analyze data to provide meaningful and valuable insights. No matter whether you’re a seasoned professional or a recent graduate of data science courses, data can present challenges. Issues such as quality, variety, and volume often present hurdles for even the most seasoned data professional.
Could new artificial intelligence (AI) methods be the key to revolutionising data analytics? With AI transforming the digital landscape, there has never been a better time to scrutinize this emerging technology. Could this be the next step in analytics innovation?
What is AI-assisted Data Analytics?
It can be tricky to understand how AI is used in analytics, which is different from modern wisdom around what AI is. In recent years, the hot topic has become the use of large language models (LLMs) such as OpenAI’s ChatGPT tool to generate information. These models are essentially massive statistical models that utilize a process called machine learning on vast swathes of data to create a model for consumer use.
AI is applied somewhat differently to corporate data, as the needs are inherently different. While machine learning (ML) can be used to learn about data, it’s only one type of AI tooling. Other tools, such as natural language processing (NLP), can also be employed to uncover insights that may not be readily apparent through conventional analytics.
Where automation may act as a replacement for the roles of workers, AI offers an alternate prospect, the opportunity to augment an employee’s work by reducing the requirement to conduct repetitive tasks, while enabling them to dive into data sources that may offer far greater value to end users.
The Many Opportunities of NLP in Data Analytics
Take, for example, large amounts of unstructured data, such as social media contacts, call transcripts, or written form information. Tools such as NLP can help analytics to parse, classify, and interpret data in ways that would have been considered unfathomable as little as a decade ago.
NLP models have achieved great success outside of the data analytics space, for example, when used to detect intent and make inferences during conversations with smart assistants. While you might elicit a giggle when dividing zero by zero, NLP has the potential to revolutionise the way customer service works.
In many modern businesses, it can be challenging to provide instant insights on the issues that customers are facing – it can take time to put data together and to get back to a customer. NLP models offer a way for analysts to delve into unstructured data, allowing models the freedom to parse and categorize data, and allowing analysts the freedom to conduct deeper analysis on partially cleaned and presented data.
Could AI Improve Data Analytics?
There are numerous opportunities to utilize AI in data analytics, and often, it extends beyond simply inferring, interpreting, and analyzing existing datasets. Take, for example, organisations that require regular reporting of KPIs. In these cases, an AI-assisted process may be ideal for processing, interpreting, and publishing information in a consistent report for end-users.
AI has immense potential as an augmentative tool, being able to use natural language to generate code such as Python and SQL could be extremely useful in designing.
With data analytics often being a time-consuming role, assisting analysts with AI-powered tools could hold immense potential in simplifying and streamlining work. As data has evolved, many analysts have acquired skills in a wide range of data tools, from programming languages such as Python and SQL to assistive tools like GitHub Copilot and Augment Code, making the industry a prime candidate for further tech retooling and innovation.
The Challenges of AI-assisted Analytics
While AI-assisted analytics offers immense opportunities for organisations, there are also challenges to overcome. It’s important to first recognise that models are only as good as the data provided to them – as the saying goes, garbage in, garbage out – so for data analysts, it’s important to recognise the limitations that their data may have.
It’s well known that sometimes, analytics can come with unrealistic expectations. It’s vital that analysts have a good grip on data models and don’t become overly reliant on AI to produce results and insights.
There are other considerations to be had, of course. Depending on the AI model used, some can be susceptible to poor quality results – a phenomenon known as hallucination. Other challenges come around the ethical and practical uses of data, a challenge that many organisations, large and small, have fallen afoul of in recent times.
One thing is clear: AI is set to have a transformative impact on analytics, and for many analysts, it presents opportunities to supercharge the value they bring to the business. Ultimately, as with many AI questions, the question will not be ‘will AI be useful?’ – instead, ‘can AI be used effectively?’. To answer that question, we’ll have to wait and see.
