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This article along with several others I found, all discuss the advantages of AI (Artificial Intelligence) in DA (Data Analysis).

Such as how, according to a survey done last year, 82% of responding analysts believe AI will enhance their role, allowing them more time to spend on strategic work. With the key points being that utilizing AI for DA will increases efficiency, allow data to be cleaned and sorted faster, potentially even in real time alongside data collection.

In theory and current practice, this appears to be wonderful. However, the concern I've had, which has been investigated as directly related to AI reliability, but I have yet to find anything related to data collection and analysis, is that AI is generating data, which is going to be included in data sets and at some point is likely to be used to train future AI and be included in future analyses.

The reason this is concerning is because in the one study I could find where it was tested, using AI generated data to train the next AI, there was a slight degradation in the first generation, but it deviated further each generation and by the ninth generation, the data was unintelligible. The study also found it was possible that AI created content could account for up to 90% of new content on the internet in the very near future.

To show some representative figures for how much AI content is being generated, 2022 saw the rise of text-image algorithms, with the four primary AI-image generators being Adobe Firefly, DALL-E2, Midjourney and Models based on Stable Diffuse (an Open Source AI). These became available for use by the public between July 2022 and March 2023. As of August 2023, they had produced almost 15.5 Billion images. As at the same time, Instagram, which launched in October 2010, had uploaded a total of 50 Billion images.

Sources :

How Does AI Impact the Future of Data Analysis? - AI influencing efficiency & changing the role of Data Analysts (March 2025)

AI in data analytics: opportunities and challenges - A look at AI affect on Data Analysis, data privacy, ethics & governance (Jan 2024)

Is AI quietly killing itself – and the Internet? - Training AI on AI generated Data (September 2023)

People Are Creating an Average of 34 Million Images Per Day. Statistics for 2024 - Statistics & breakdown on Text-Image AI created images. (August 2023)

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How Does AI Impact the Future of Data Analysis?

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How data analysis is performed is evolving. Where we once drew data insights manually with the help of software to organize our data, we’re now seeing artificial intelligence (AI) take a prominent role.

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Emerging Cybersecurity Threats:

I believe that over the next decade that protecting and safeguarding data will be crucial as developments in advanced cybersecurity threats such as deepfakes, phishing, and AI pose a major threat to personal and business data.

The CyberCX 2025 Threat Report published earlier this year in February 2025 revealed that espionage-related incidents are taking over two weeks longer to discover compared to 2023 and 75% of business email compromise (BEC) attacks involved multi-factor authentication (MFA) being bypassed.

The report detailed that:

Espionage incidents are taking longer to detect

Multi-factor authentication (MFA) is not a silver bullet

Healthcare remained the most impacted industry

Vast majority of cyber incidents are financially motivated

Significant increase in ransomware only attacks

Some stolen data is never advertised on the dark web

Executive Director of Digital Forensics and Incident Response at CyberCX, Hamish Krebs said “Despite the best efforts of defenders over the past 12 months, the global cyber threat landscape has continued to deteriorate as adversaries evolve their tactics and up the tempo of attacks." (CyberCX, 2025)

The report reminds us that evolution in the IT field is not exclusive to specific areas and that it is important to position security as one of the foundational pillars of the future of IT.

Source:

CyberCX. (2025). CyberCX 2025 threat report reveals cyber landscape is changing. CyberCX. https://cybercx.com.au/news/cybercx-2025-

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CyberCX 2025 Threat Report reveals cyber landscape is changing

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CyberCX’s 2025 report warns of rising cybercrime, urging action on threat intel, ransomware, and critical sectors.

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Data accessibility and the workplace.

The more I am learning about the field of data analytics, science, and engineering the more value I see in having those skills be made accessible to the general workplace. Allowing workers of a non technical background to have access to deeper insights in their projects can only increase productivity, however the insights from current level AI tools cannot be a replacement for a human Data Analyst.

Diby Malakar, VP of product management at Alation stated 'Generative AI enables users to uncover and act on insights within their existing tools, transforming productivity and amplifying what’s possible...'. I believe the coming years will result not in data analysts losing a role in the industry, but as guardians of data that is being accessed by the general public through the filter of LLM and frontend AI tools. Ensuring that the output of the systems that we create are accurate.

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Looking Ahead to the Future of Analytics

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The months and years ahead promise to be an interesting time in the data analytics space.AI and machine learning are already making their mark, elevating analytics-and its front-end sibling, business intelligence. End users are seeing their capabilities expand in new directions, with an ability to ask questions they did not even consider with earlier technology iterations. Industry leaders are tracking a range of developments that are defining analytics and BI in the year ahead.

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There remains the burning question, will AI replace data scientists? With the emergence of AI tools and, as mentioned by Drew Hinchey, the increase in use of generative AI in the work place; the question remains "what will happen to data scientists?"

It has been my opinion that data scientists will remain relevant but their role will evolve. I see a genuine necessity for workers to learn to integrate AI skills into their arsenal of tools and equipment.

Late 2022, an article was published in Forbes exploring their 'predictions' on the evolution of data science and suggested that the work of data scientists "will be...augmented by artificial intelligence...and other forms of automation." (Moioli, 2022) https://www.forbes.com/councils/forbestechcouncil/2022/10/13/predictions-on-the-future-of-data-science/

I engage in data driven decision making in my work as a strategic account manager in transport / logistics; and I am in the process of developing analytical tools using the principles of data science to support 'end-to-end' supply chain analysis for the customers and their key suppliers.

I regularly make use of generative AI to support my work by taking over those mundane, repetitive tasks so that I can focus my attentions on a deeper analysis of the data.

I am excited to explore how the role of data scientists are predicted to change with the emergence of AI tools. It is my opinion that data science is not limited to an IT discipline or role.

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Predictions On The Future Of Data Science

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New technologies are profoundly changing the responsibilities and activities performed by data scientists. This is then compounded by further transformations that may soon totally change the nature of such work.

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As data analytics becomes a larger part of how organisations make decisions, it brings a range of ethical challenges that go beyond traditional business issues. Concerns around privacy, bias, surveillance, and power dynamics push the boundaries of existing moral frameworks and call for fresh perspectives.

Kristen Martin (2022), in Ethics of Data and Analytics: Concepts and Cases, points out that while the ethical questions related to data are not new, they introduce unique complexities. Unlike many business decisions, data analytics requires careful attention to accountability, fairness, and the potential for harm - areas where conventional ethical approaches are falling short.

Because analytic systems reflect the values of their creators and have real-world impacts. Developers must actively consider the values built into these technologies. Data analytics is more than just uncovering insights. By introducing multiple ethical frameworks applied to system design and the interpretation of the data we can help challenge default assumptions and ensure that those often overlooked or treated unfairly by data-driven processes are thoughtfully considered, helping to remove systematic bias from industries.

Source: Martin, Kristin. (2022). Ethics of Data and Analytics: Concepts and Cases. Routledge.

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e-Reader | Ethics of Data and Analytics | Concepts and Cases | Kirsten

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Data Storage continues to pose a challenge to the field of Data Analysis. The growth rate for data collection is not matched with our ability to store the data, nor with our ability to process and analyse the data effectively (Kaisler et al.). Due to the advancements in technology and the scope of its usage across countless sectors, there will always be moving goalposts for researchers in the field. An opportunity relating to this could be to have improved frameworks to better discern whether the data is meaningful, i.e., collecting data of value over volume.

Kaisler, Stephen & Armour, Frank & Espinosa, J. & Money, William. (2013). Big Data: Issues and Challenges Moving Forward. Proceedings of the Annual Hawaii International Conference on System Sciences. 995-1004. 10.1109/HICSS.2013.645.

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Studies show that the use of generative ai in the workspace has increased dramatically since its conception. Data from the 2023 June Labour Force Survey states that 38.9% of tasks within jobs are directly exposed to LLMs (Large Language Models). This can cause ripples in the professional standards due to privacy, unethical or harmful data being modelled in AI.

Walkowiak, E., & MacDonald, T. (2024). Generative AI and the workforce: What are the risks? (SSRN Working Paper No. 4568684). SSRN. https://doi.org/10.2139/ssrn.4568684

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See all articles by Emmanuelle Walkowiak

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Generative AI and the Workforce: What Are the Risks?

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The unprecedented and rapid diffusion of generative AI represents a major disruption for the labour market, bringing new opportunities and risks within

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Hi everyone. I'm really looking forward to the challenges and opportunities that get posted here. It's a great resource for sharing ideas and discovery of information and can be useful for your report topic and report. Welcome everyone! Best regards, Phil.

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Artificial Intelligence is reshaping IT by handling repetitive tasks, solving problems and allowing humans more time to work on more important tasks. This will likely result in increased productivity, a reduction of errors and be more cost effective. (Ashley, 2024)

Ashley, M. (2024, April 4). AI and IT: How Artificial Intelligence Is Changing Information Technology Jobs. University of Cincinnati. https://online.uc.edu/blog/how-ai-is-changing-it-jobs/

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Recent digital innovations such as AI, machine learning and cloud-based data systems have led to a growing demand for related skills in the Australian job market (2023 Intergenerational Report, p 87). As companies begin integrating these technologies into business operations, there are increasing requirements for specialists who can competently and ethically manage cloud-based data systems. These new tools also provide opportunities for collecting large volumes of data, leading to a growing need for Data Analysts who can understand and analyse information to support data-driven initiatives.

Source: Australian Government. (2023). 2023 Intergenerational Report: Australia’s Future to 2063.

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While advances in Digital Technology are exciting and promising in a variety of ways, businesses big and small must be prepared for ever changing challenges when it comes to information security and how to prioritise their digital workspaces.

A major challenge confronting businesses today is the widening skills gap in the IT sector. Over two-thirds of IT leaders say there’s a disconnect between their teams’ current capabilities and the expertise needed to reach company goals. Across the board, there’s concern about whether existing teams are equipped with the right skills to succeed.

Blockchain tops the list as both the most sought-after and the most deficient skill. AI and cognitive computing follow closely behind.

Matthijs Van Sterkenburg

Global Knowledge study reveals present and future skills issues facing IT departments worldwide.

Microsoft Pulse

Global Knowledge study reveals present and future skills issues facing IT departments worldwide – Microsoft Pulse

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Matthijs Van Sterkenburg Microsoft, Applications & Infrastructure

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Global Knowledge study reveals present and future skills issues facing IT departments worldwide – Microsoft Pulse