How generative AI is changing the face of analytics


The rapid ascent of generative AI has become a global sensation, largely fuelled by the popularity of widely available large language models (LLM) powered by 3rd party data. However, amidst this buzz, generative AI has the opportunity to move beyond the hype and into widespread production across the enterprise.
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The capacity of generative AI to accelerate decision intelligence has the potential to provide business value across many customer-facing operations and functions. One area where generative AI could wield substantial influence is within the realm of data analytics.

Generative AI has the potential to bring about significant transformations in the data analytics landscape. Delivering business decision intelligence at scale by reducing time to insight has become critical to overcoming today’s business challenges.

This innovative technology, rooted in machine learning, holds the capacity to revolutionise how data is interpreted, processed, and utilised by narrowing the time and skills barriers needed to extract meaningful insights from data.

Generative AI and its impact on data analytics

Correctly trained, AI algorithms have been extremely helpful in generating insights based on data, but it has largely operated in silos by a few trained specialists.

Generative AI has the potential not only to democratise AI to a wider business audience but also to accelerate the process of getting the insights required to make decisions from the sea of data within every organisation and every industry.

According to Forrester, “Companies across all verticals and maturity levels are finding opportunities to implement AI.”

Paul Baptist, VP, Solution Engineering, APJ, Alteryx

From text-mining to PDF data extraction and natural language processing (NLP), generative AI and large language models are lowering barriers to entry, empowering everyone to ask analytical questions and breaking down silos to engage the world’s imagination in how we understand data.

Recent Alteryx research highlights this trend for AI-driven insights; 89% of companies currently using generative AI reported seeing either substantial (34%) or modest (55%) benefits of the technology.

For those organisations already using generative AI, the main drivers for leveraging generative AI were “to predict our business performance and industry trends” (56%) and “to improve the speed and efficiency of existing processes (52%).

As we blend generative AI technologies into the analytics stack, we’re seeing exciting new ways of enabling decision-makers, analysts, data scientists and developers to collaborate and develop analytical insights in real-time.

Alteryx recently debuted a generative AI engine, Alteryx’s AiDIN, to combine the power of artificial intelligence (AI), machine learning (ML), and generative AI with the Alteryx Analytics Cloud Platform to accelerate analytics efficiency and productivity.

With the new launch, Alteryx aims to provide businesses with new ways to interact with, improve, and reimagine analytics.

By making businesses more efficient and reducing human error and intervention, generative AI can not only churn bigger profits for companies but also provide a better customer experience.


One example is being able to automatically produce documentation of different types of data transformation into a myriad of international languages. This functionality is also great for Risk Management teams to manage audits and/or used to manage data governance.

Automating the creation of this documentation is a critical step for a data analyst that takes time in manual efforts.

Accelerating those efforts through generative AI is at the intersection of driving business efficiencies with big time-saving value and proving to be an ideal application for this emerging technology. 

Evolving processes with generative AI

Presently, companies mostly employ AI capabilities to overcome labour-intensive, cumbersome process that take long hours and a heavy cognitive loads to perform manually.

AI can make this a much simpler and more efficient process by automating key processes, like data preparation, allowing the algorithm to focus on detecting anomalies and generating comprehensive reports based on the data collected and analysed.

By empowering non-technical users to build and automate processes without needing to write code, employees across different departments and functions can quickly create solutions tailored to their specific needs and workflows.

This can reduce manual labour and enable the opportunity for decision-makers to perform more creative tasks.

Moreover, it can present large reports and analyses in different formats with simple summaries that can be used in boardrooms and shared with stakeholders directly – wasting less time on data collection, analysis and interpretation and shifting the focus on action.

From data collection to analysis, interpretation to reporting, generative AI has the potential to revolutionise decision-making by enabling employees can take action and stay ahead of the curve.

Considerations when adopting generative AI

While businesses stand at the cusp of a transformative AI era, businesses must also recognise the inherent risk of using imperfect or non-compliant data, potentially heavy with age-old biases and carefully consider data ethics, governance, security, and privacy concerns / requirements.

AI models generate insights that are only as good as the data they have access to. Based on this “garbage in, garbage out” concept, it’s imperative that human oversight ensures generative AI is paired with quality data and high-quality datasets and analytics processes.

For generative AI to be successful, businesses must recognise the inherent risk of using imperfect or non-compliant data. If inaccurate data is input to a program, the output will also be inaccurate.

Only by establishing mechanisms and adopting best practices that align human creativity with AI tools will enterprise CIOs balance the promise of generative AI with the appropriate guardrails that ensure governance requirements are met. 

AI can deliver at every level if leaders invest in the right tools, automation, and upskilling training.

Strategies to neutralise AI bias

Businesses must also know the inherent risk of using imperfect or non-compliant data to feed AI models. Not only could this data be flawed or incomplete, but it could also contain unmitigated elements of historical systematic bias.

To ensure that AI accurately reflects our evolving ethical landscape, we must institute mechanisms for recognising bias and incorporating diverse perspectives right from the data collection phase.

By establishing these mechanisms and adopting best practices for bias identification, we heighten our awareness of potential issues. Further, this inclusiveness acts as a crucial checkpoint, enabling us to pinpoint and rectify discrepancies at an early stage.

By doing this, we empower those closest to the data to detect inconsistencies, resulting in a robust strategy to weed out biased data before it influences our AI models.

Adoption of generative AI and where to begin

While AI has become truly viable, it’s when you put accessible AI into the hands of non-technical users and empower them to deliver repeatable AI-driven insights that it becomes truly valuable.

But first, organisations need to build a culture where data is the basis of every decision and strategy and where all employees are on board with this approach and mindset.

The second step in embracing any new technology and providing access at scale is to get everyone in the team on board.

There is naturally some fear that technologies like AI will replace human workers. But employees must understand that technology is not meant to replace them, it will augment their roles and allow them to spend more time on other tasks.

From healthcare and financial services to manufacturing, CPG and retail, decision-makers across every industry can utilise generative AI innovations to improve efficiencies and customer experiences.

As businesses plan their AI strategies, they must contemplate their analytics proficiency and business priorities to determine how and where to leverage the capabilities of generative AI for maximum impact.

Success is about aligning data analytics strategies with generative AI potential.

Paul Baptist, VP, Solution Engineering APJ, Alteryx 

With over 20 years of experience in technology, the majority of those focused on customer experience technologies, I have been working with companies large and small across the globe to transform their businesses and to use data to serve their customers better. Prior to joining Alteryx, Paul led both sales and presales teams at Salesforce and ServiceNow. 

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