Written Interview with Karl Crowther, VP, MEA, Alteryx

1. How can GenAI enhance the accuracy and efficiency of data analytics processes?

GenAI presents an opportunity to fundamentally change the way that enterpris perform analytics in two key ways: One, genAI makes analytical tools easier to use, which in turn makes tasks easier to perform and creates efficiencies for an enterprise.  When teams can pick up tools faster, and find them easier to use, they can add value more quickly.  Because genAI allows people to perform analytical tasks using natural language, anyone can become a data-driven contributor – not just those in specialized roles.

Two, generative AI has a powerful ability to process and analyze vast amounts of unstructured data to create business value.  This unlocks analytics opportunities that were previously much more challenging to execute such as reviewing the 10-K of a publicly traded company and synthesizing key business strategies.  In the past, this would have required someone to read, analyze, and summarize what is traditionally a very lengthy document.  When applied at scale, these types of use cases can significantly enhance the accuracy and efficiency of analytics.

2. In what ways can GenAI automate data cleaning and preprocessing tasks in data analytics?

One of the most promising aspects of generative AI is that it will lower the skills barrier for analytical tasks, making analytics more accessible and easier for anyone, especially non-technical users.  This is because generative AI allows people to perform analytics with natural language, as opposed to code or specialized skillsets.  For example, genAI can automate data cleaning and preprocessing by identifying and correcting errors, filling missing values, and normalizing data formats – all with natural language-based instructions. It can also be instructed to detect or surface anomalies or outliers that could skew analysis results.

3. How will GenAI impact the speed and scalability of data analysis for large datasets?
   (From Peter M blog post — would this need to be paraphrased? This might not address the “large datasets” part) Organizations that don’t take advantage of genAI are simply going to fall behind on operational efficiency measures; they’re not going to see as much return on their human capital. Generative AI can extend the power of one analyst into three or four — not by being a carbon copy of that analyst, but by automating the mundane, repetitive tasks, like creating governance documentation, from analysts’ daily lives. In historical context, genAI can complete these tasks almost instantaneously.

4. What role will GenAI play in generating insights from unstructured data sources?

GenAI will play a big role in generating insights from unstructured data sources. One of the biggest promises of genAI is that it creates new opportunities for automation that weren’t as feasible or easy as before.  Many of these automation opportunities are related to unstructured data analysis.  Generative AI will play a huge role in generating insights from unstructured data.  For example, here at Alteryx we’re using generative AI to summarize analytics automation workflows based on semi-structured data.  This is a great governance benefit to our customers, who need to document the inputs, outputs, and key logic steps of their workflows, but often don’t have the time to do it manually.

5.      What are the potential risks and challenges of integrating GenAI into existing data analytics frameworks?

At Alteryx we believe that generative AI has a lot of promise.  But it is not a panacea, and it’s important to recognize that there are other AI & machine learning techniques that may be a better fit for some analytical tasks.  As a result, one of the challenges is to understand when genAI might be the best technology choice for a use case, where it may need to be combined with other techniques  — or even avoided for some uses cases.

Having a responsible AI framework in place to ensure that the multi-faceted aspects of using AI responsibly are always being considered for projects and implementations is key for enterprises.  AI adoption has impacts across the business, from technical implications and business value to legal and security considerations and even workforce effects.  Responsible AI frameworks can help enterprises work through these considerations in a systematic and structured manner.

6. How will GenAI change how data analysts and data scientists interact with data and analytical tools?
   GenAI automates the repetitive, mundane tasks that are part of data analysis — such as prepping, connecting, and cleaning data — which distract data analysts and data scientists from making those truly breakthrough discoveries with their data. Analysts and data scientists are critical, creative thinkers; genAI, when applied safely and intelligently, gives them the freedom to pursue new, bigger ideas, without being bogged down by administrative data tasks.
   It will also make analytical tasks easier, as it allows for natural language interfaces to execute analytical tasks. (see #2 above)

7. In what ways can GenAI democratize access to advanced data analytics capabilities for non-technical users?
   The use of natural language interfaces to execute analytical tasks represents a tectonic shift in how data analysis is done – because it means that anyone can derive meaningful insights from data that can generate true business impact. Alteryx is founded on the principle of providing analytics for all and genAI is unlocking this potential faster than ever.

GenAI also makes advanced analytics more accessible for non-technical users by automating tasks across the entire analytics lifecycle. From preparing and cleaning data to connecting data sources and analyzing – even through to presenting the data – generative AI acts as a guide to provide tips and instruction for effective data analysis. For example, the Alteryx AI Platform for Enterprise Analytics has generative AI capabilities that can create PowerPoint presentations and other types of descriptive communication in a way that makes the analytical results more consumable.