Jared Parker, CEO at Rasgo comments more on today’s AI era and how it will create new models and opportunities for brands and marketers:

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Welcome to this MarTech Series chat, Jared, tell us about yourself and more about Rasgo

Prior to Rasgo, I spent more than a decade in the Big Data space, which led me and my co-founder, Patrick Dougherty, to identify a major problem in the world of analytics: the friction analysts and business users face trying to uncover new insights from enterprise data. We started Rasgo and quickly evolved to being at the forefront of the Generative AI disruption.

Rasgo revolutionizes the data analysis process by offering a unified natural language interface and a suite of optimized AI agents to automate the most painful elements of surfacing new insights. Rasgo’s AI engine proactively searches metadata and query history to accelerate data discovery, writes, tests, and executes SQL queries for higher velocity analysis, and generates the appropriate visualization, while distilling the results into actionable business recommendations. Throughout this process, we ensure the data analyst remains involved, enabling human decision-making at critical junctures to optimize for accuracy and trust.

We’d love to hear from you about your latest product enhancement – Rasgo AI and how it benefits end users?

 Rasgo AI empowers data analysts and knowledge workers to automate the time-consuming and repetitive elements of their workflows while maintaining human oversight for accuracy and governance. Rasgo AI is highly differentiated because it leverages GPT for Intelligent Reasoning to think and act like a business analyst. This is significant because analysts are entangled in a tedious cycle of low-value tasks, often taking days or weeks to answer a single question from data. This disrupts decision-making and underutilizes the true potential of an organization’s high-value personnel. By offloading low-value tasks to AI, Rasgo empowers these users to focus on decision-making and driving enterprise value – providing our customers faster time to value, significant improvement in team productivity, and an overall greater degree of job satisfaction.

What in your view are some of the top challenges that still plague data teams and how do you see newer technologies like GPT and others start to change the game for data processes and teams?

The conventional data analysis process is incredibly time-consuming, spanning multiple steps and stakeholders. Answering a single question from data involves the discovery of relevant tables, writing and debugging SQL queries, creating dashboards in BI tools, and translating results into comprehensible business recommendations.

GPT helps by abstracting away the complexity of writing SQL, and with the emergence of GPT Code Interpereter, can even interpret and summarize data, but GPT is trained on the internet, it doesn’t understand your data. Rasgo teaches GPT an organizations data, using only the metadata available in your data warehouse, without extracting any raw data from your environment. This allows users to leverage the power of GPT while providing the organization with data privacy and protection. It’s the best of both worlds.

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What should data teams today do better or more of to drive better internal practices that ensure streamlined management/maintenance of their data warehouse?

In this era of AI, data teams are pivotal in upholding data quality and reliability. Those championing data quality and thorough documentation are paving the way for substantial competitive advantage within their organizations.

At Rasgo, we’ve dedicated significant investment to empower data teams as “AI Managers”, enabling them to serve as a true administrator of trust and security. By providing these users with a centralized hub, they can score the AI readiness of each table, feed GPT with tailored instructions, and formulate templates for specific company analyses. Through these capabilities, data teams become the driving force behind internal innovation.

What are some of the key pointers that data teams should keep in mind when using self-serve systems and analytics as early first-time users/adopters?

Generative AI isn’t a mystical solution. We’ve discovered that collaborating between humans and AI to establish explicit rules, instructions, and safety parameters is what ensures trust and security when handling enterprise data. A range of best practices includes comprehensive data documentation and equipping AI with instructions that offer guidance and oversight during critical stages of the analysis journey.

This approach not only promotes enhanced consistency and governance but also streamlines the end-user experience as a considerable amount of the previously held tribal knowledge becomes accessible to the AI. With these guidelines in place, users can pose broader prompts, trusting that the data team has effectively embedded the required context within the AI manager.

If you had to share five thoughts about the state of martech in the next decade, what would it be?

Increased Hyper-Personalization: Powered by AI and data analytics, marketers will capture more data on individual preferences and behaviors, enabling them to deliver an even greater amount of personalization

AI-Driven Insights: AI will play a central role in transforming marketing analytics. AI will abstract away the complexity of predictive modeling, sentiment analysis, etc. to provide marketers with actionable insights for informed decision-making. AI-powered tools will guide campaigns and strategies based on data-driven recommendations.

Security & Privacy Laws: Data security and privacy laws will continue to become more stringent, forcing Martech providers to build compliance guardrails into every new offering.

Immersive Experiences: Augmented reality (AR), virtual reality (VR), and mixed reality (MR) will merge with martech to create immersive experiences. Marketers will leverage these technologies to engage customers in entirely new ways, from interactive product demonstrations to virtual shopping experiences.

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