Alteryx's Trevor Schulze: Unlocking Business Value with AI

We speak with Trevor Schulze, SVP and Chief Information Officer (CIO) of Alteryx, about the importance of businesses having a modern data stack to gain greater data insights
AI Magazine speaks with Alteryx's Chief Information Officer Trevor Schulze about how modern data stacks are essential and how companies can maximise AI

During a time of rapid digital transformation, modern data stacks are becoming a faster and more scalable way for businesses to gain insights.

A data stack is essentially a collection of technologies that enable raw data to be processed before use. Whilst data stacks were originally very complex, more recent iterations are cloud-based and therefore enable organisations to make more informed decisions.

We speak with Trevor Schulze, SVP and Chief Information Officer (CIO) of Alteryx, about the importance of businesses having a modern data stack to gain greater data insights. He examines the role of generative AI in making data analysis more accessible to non-technical users and how new technologies like AI can be leveraged to gain more value from data.

What is the importance of modern data stacks? Do you have any specific use cases?

“With global data creation projected to grow to more than 180 zettabytes by 2025, there is enormous business value potential from unlocking the insights from data and becoming a data-driven business. The challenge is figuring out how to get insights and make decisions from this sea of data.

“For businesses, time-to-insight and time-to-market always win. CIOs and IT leaders know that tech-powered outcomes are the thing to focus on but to move to the next level, we must accelerate the data journey. To do so requires a tech stack built for the era of intelligence, enabling and empowering everyone to use the data from these systems to make decisions faster, better, and more effectively than the competition.

“Historically, most organisations’ data stacks have been built around structured and on-premise data warehouses and on-premises databases. However, these systems are fast becoming antiquated, increasingly expensive, difficult to maintain, and offer limited scalability – especially as data types and volumes increase and processing gets more complex. We’ve seen a huge increase in the variety of data sources organisations need to connect to unlock value. Faced with analysing external data from IoT sensor data, log files, social media, and metadata from API files, many are analysing hundreds of different data sources daily.

“Consider the example of a railway operator which needs to monitor passenger traffic with data that refreshes every few minutes to keep pace with train schedules, so it modernises its data stack to enable real-time reporting. Or a fitness platform which delivers personalised content to increase customer engagement, driving the need for data architecture that centralises dozens of different customer touchpoints and gives marketers a 360-degree view.

“However, when attempting to modernise their data stack, organisations can often fall into the trap of adopting overly complex architectures and tools that require specific technical skills. A modern data stack is only as good as an organisation’s ability to use it. To facilitate this requires infinite scalability to support an organisation’s data storage and processing needs. Stacks that support both on-prem and SaaS-based tools, multiple clouds, and the ability to collaboratively develop and manage data workflows that run directly through platforms such as Databricks, Snowflake, and AWS are all useful elements in a functional data stack. These modern data stacks accelerate the flow of data and insights across the business.”

How can companies ensure their workforce has access to functional modern data stacks?

“For many companies, the data flow is marked by incredible complexity in volume, velocity and variety. If we want to build a modern, data-driven enterprise, we need to start by addressing the data journey. We need to ensure more people can engage with the data and democratise the ability of all our teams to analyse raw data for insights before the business is disrupted.

“The simple answer is that many companies don’t have the right culture for democratising data. Too many controls around their data limit the number of people who can access it. Breaking down these cultural norms that keep data under such tight control and becoming more comfortable with a dynamic governance model will help determine which data can be made more accessible and which truly need to be controlled.

“Many make the mistake of adopting modern analytics tools without considering the range of technical and non-technical employees using them. Low or even no-code applications further improve accessibility while driving data literacy, making it possible for people with functional domain knowledge to contribute to an organisation’s analytics. For example, a code-friendly data stack means a business analyst with no coding skills can work alongside a data scientist using Python or R in the same workflow.

“Accelerating time to insight requires a data stack that doesn’t leave anybody behind; one that empowers different personas to leverage the powerful cloud and AI technologies available to them. It allows employees to tap into best-in-class technologies like AWS, Databricks, Azure, Snowflake, and more.

“Roughly 80-90% of all analytics projects are data engineering–in other words, identifying, gathering, accessing, and normalising data. The analytics piece comes at the end of that long process. When designing a data stack, the analytics process that delivers the most business value will be the one that meets people where they’re at and at the skill level they can deliver insights.”

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How does AI come into this? What are some of the benefits to utilising AI?

“Generative AI offers the perfect capability for decision-makers with no data science skills to deliver insights via a natural language prompt.

“Capitalising on its summarisation, code generation, data and automated insight generation capabilities, anyone within an organisation – accountants, supply chain analysts, merchandising analysts, and more – can leverage generative AI for core business problem-solving in their respective domains. Indeed, the value of generative AI in lowering the barrier of entry to data analysis and accelerating time to insight cannot be understated.

“With access to more data than ever before, businesses leveraging generative AI can uncover new patterns and insights in their data to make faster, better decisions and drive business value. The benefits of this speak for themselves. Not only do more than half (54%) of those using generative AI believe its application in business will lead to greater overall productivity, but it’s believed the technology has the potential to increase global GDP by as much as 7%, contributing somewhere between US$2.6tn and US$4.4tn annually to the global economy.

“But it’s important to remember that any AI-driven system is only as good as the data it’s trained on and the ability of users to ask the right questions, implement the right data techniques, and understand the outcomes. Bad decisions can happen if people aren’t data and AI-literate. If you use it without knowing how to ask the right questions – or if you take it at face value – it will give you the wrong answers.”

Why do you think companies are increasing AI investments?

“Business leaders are striving to break free from uncertainty. They seek accessible technologies that augment their roles by empowering them to become nimble, increase productivity and become more efficient in their day-to-day decision-making.

“The generative AI market is expected to grow at a CAGR of 42% to US$1.3tn by the end of the decade. According to recent research by Alteryx, almost 40% of organisations currently use generative AI in their business, with analytics insights summary and analytics insights generation among the most widely reported use cases at 43% and 32%, respectively.

“The new pathways to knowledge and insights from AI allow non-technical users to execute analytic workflows. Replacing code for natural language prompts business experts with domain-specific context the autonomy to execute data-led analysis by simply asking the right questions.

“With a basic level of upskilling, these decision-makers can use these applications to pull together large reports and analyses in minutes or build and automate processes tailored to their specific needs and workflows. Such accelerated data collection and interpretation use cases help demonstrate generative AI’s transformative potential, crystalise the benefits, and build trust.”

How can companies best take advantage of AI?

For businesses looking to drive advancements in AI, it is critical to:
  • Create a culture of data literacy where teams that know the business understand the data pipeline lineage and know what it takes to pull the right predictive and prescriptive insights.
  • Recognise the importance of curated applications, data governance guardrails, and human review - all core to empowering employees to use data responsibly.
  • Remove the complexities of data science by empowering the workforce to access the power of accessible analytics through a simple and accessible interface.

“The need for data-driven decision intelligence is not new. Businesses have had analysts pulling insights from data workflows for years. Today’s key disruptor is the need to deliver these at scale and the speed of business. While data-driven intelligence will remain the cornerstone of business decision-making for the foreseeable future, accelerating those efforts through generative AI is an ideal application for this emerging technology to drive business efficiencies.

“As we blend generative AI technologies into the analytics stack, we’re seeing decision-makers, analysts, data scientists, and developers transform analytics by reducing time to insight derived from data and lowering barriers to entry for non-technical users to extract value from data. 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. Finance departments can use the technology to analyse financial trends and assess risks, while HR teams can use it to optimise their organisation’s talent pool and analyse employee surveys. Indeed, the beauty of generative AI is that its capabilities can be tailored to most vertical use cases.

“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.”


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