As CEO and founder of AnswerRocket, Alon leads product innovation and brings actionable analytics to business people, so they can get their questions answered faster. Prior to founding AnswerRocket, Alon co-founded and was Chief Technology Officer of Radiant Systems for 25 years. He served as Radiant’s Chairman from 2004 to 2011, when the company was sold to NCR for $1.3 billion. Alon has a B.S. in Computer Systems Engineering from Rensselaer Polytechnic Institute.
Could you share the genesis story behind AnswerRocket?
We started AnswerRocket with the vision that anyone should be able to get easy answers from their data, just like interacting with a personal assistant. The idea stemmed from our frustration sitting in management and board meetings, where questions about business performance metrics couldn’t be answered on the spot. We’d then have to go off and spend days or weeks doing additional analysis, which felt wasteful because the data was already available and should be quickly accessible. We wanted to create an experience where anyone in the enterprise could simply ask a question and get an immediate, insightful response the moment these business questions cropped up.
How does AnswerRocket leverage AI to transform traditional analytics?
AnswerRocket has been leveraging AI to make data analytics accessible and approachable to analysts and business users alike for over 10 years. Most recently, we’re applying generative AI technology to create a conversational AI assistant called Max. Business users can chat with Max to explore and analyze their data – they don’t need to know SQL or how the data is organized to get a good, meaningful answer. Max is connected to AnswerRocket’s suite of analytical applications–or Skills, as we call them–which enables it to perform advanced analyses ranging from statistical, diagnostic, and even predictive analysis. For analysts and data scientists, we’re transforming their workflows by giving them the tools to create reusable, AI-powered Skills in our Skill Studio. It’s about capturing their expertise and analytical know-how in specialized AI analytics agents that their users can successfully interact with.
What are some of the key benefits your clients experience with Max, your AI data analyst?
Max removes the technical and data literacy barriers that have long made data analytics solutions difficult for business users to adopt. By embracing the chat paradigm, we offer users a familiar experience for engaging with Max, allowing them to explore data and ask questions in their own words.
Speed is another huge benefit. Max automates complex analyses to deliver relevant, interactive answers and insights immediately. Plus, the outputs are tailored to match the existing analysis and reports our clients are used to, eliminating the learning curve and enabling users to quickly derive insights without adapting to new formats. We’re trying very much to meet users where they are and to find ways to fit Max into their current workflows seamlessly.
Can you elaborate on how integrating OpenAI’s GPT-4 LLM enhances Max’s capabilities, and what unique advantages does it offer to businesses?
Integrating OpenAI’s GPT models into Max really took its capabilities to the next level. LLMs significantly improve Max’s natural language understanding and generation. Coupled with our analytics platform and specialized DS/ML applications developed over the course of a decade, we were able to create an AI assistant that could handle more complex queries and deliver more nuanced answers. This was a huge leap forward in enabling an intuitive, enjoyable chat experience for users. GPT’s ability to generate human-like responses helps users feel more comfortable and confident in using the platform–as if they’re chatting with a colleague.
With the rapid evolution of language models like GPT-4o and Claude 3, how does AnswerRocket plan to stay ahead of the competition and continue innovating in the AI analytics space?
The space is moving incredibly fast and that’s been keeping us extremely busy. We’re getting our hands on every major model that’s being released, experimenting and learning all along the way. We’ve designed AnswerRocket to be model-agnostic, with the view that customers will want to be able to select a specific model or even multiple models to support a variety of use cases. We want to enable that level of flexibility and guide users in selecting the best models for the job at hand.
How does AnswerRocket ensure that its platform is user-friendly for business users who may not have technical expertise?
Max, our AI assistant, plays a big role here. Not only is Max designed to understand what the user is asking for and return an answer, it also helps steer users towards useful answers. For example, we know starting from a blank screen can be intimidating to users, so we give them sample prompts to jump off of. Similarly, Max can provide suggested follow-up questions alongside its answers, leading users to the next relevant insight. Max can also probe further if more detail is needed to perform a requested analysis – the aim is to keep the conversation going and to get the user to the answer they’re seeking. Finally, users can give feedback on Max’s responses. This is helpful to inform administrators on where the experience needs to be tuned or improved.
Can you discuss the customization options available with Max and Skill Studio?
Our vision for Skill Studio is to give data analysts the tools to create AI-powered assistants for virtually any data analysis use case under the sun. It’s about helping them stamp their processes and best practices into reusable and composable AI agents that can make quick work of tough data analysis tasks.
With Skill Studio, users can create custom Skills tailored to their unique analytical processes. This includes defining specific data sources, analytical methods, and visualization preferences, along with building and modifying reports and workflows. Max supports complex analyses using multiple data sources, both structured and unstructured, to tell more comprehensive data stories.
Skill Studio offers a full development environment with a low-code interface, making it accessible for both technical and non-technical users. You don’t have to start from scratch: We provide pre-built components and templates for analyses, charts, and tables. Additionally, we have purpose-built AI assistants for specialized tasks. Of course, you can also create your own custom blocks and integrate your own machine-learning models.
Could you share some examples of how AnswerRocket has driven significant business outcomes for your clients, such as improving decision-making or increasing operational efficiency?
AnswerRocket has driven significant business outcomes across various industries, including fields such as consumer goods, pharma, insurance, financial services, and professional services.
One of our standout success stories is with AB InBev, the world’s leading brewer. Like most companies, they faced challenges with the manual and time-intensive process of turning raw data into actionable insights. By integrating AnswerRocket’s AI assistant, Max, they transformed this process.
For example, the time required to turn raw data into actionable insights was reduced from 20 days to 3 days, allowing their brand managers to make timely decisions. Report generation, which used to take days, now happens within hours, delivering insights quickly across 17 markets. Business users are now able to self-service answers and insights on demand by chatting with Max.
In terms of productivity, our AI solutions freed up 160 working days for AB InBev’s insights team, enabling them to focus on strategic tasks. The solution has scaled from the European team to global operations, demonstrating its broad impact.
These improvements in efficiency and decision-making are not isolated to AB InBev. Many of our clients have seen similar outcomes, with faster insights, more strategic use of resources, and better business results.
Given the importance of data security and compliance, how does AnswerRocket ensure that its platform meets enterprise-grade security standards?
Data security and compliance are absolutely crucial. We’ve implemented several robust measures to ensure the protection of our users’ data.
We use advanced encryption methods to secure data both at rest and in transit. This means that data is protected at all stages of processing. On top of that, we have strict access controls. Access to data and analysis is restricted to authorized users only, ensuring sensitive information is kept safe.
We also adhere to industry standards and regulations like GDPR and CCPA, which is key for compliance with data protection laws. We never make copies of the data while it’s being analyzed. This maintains data integrity and confidentiality.
We conduct regular security audits and vulnerability assessments. These help us proactively identify and mitigate potential risks, ensuring our security measures are always up to date.
These steps underscore our commitment to safeguarding our users’ data and maintaining the highest standards of security and compliance. It’s about making sure our users can trust us with their data every step of the way.
What are your thoughts on the future of AI in enterprise analytics?
AI will revolutionize enterprise analytics by significantly enhancing productivity and efficiency. Today, much of the work involves manual data collection, processing, and reporting, which is time-consuming. With AI, these routine tasks will be automated, allowing teams to focus on higher-level, strategic work.
Imagine a future where an AI assistant can pull data from various sources, perform complex analyses, and generate reports almost instantaneously. This will enable analytics and business teams to spend more time interpreting data and developing strategic recommendations.
The role of analysts will evolve from being doers to orchestrators of AI-driven processes. They will guide AI systems to explore different hypotheses, validate results, and ensure the insights align with business objectives. AI will allow individuals to be much more powerful, meaning a small team could achieve what currently requires much larger groups.
Ultimately, companies that embrace AI early will have a competitive edge. They will be more productive and innovative, responding to market changes swiftly. As AI reshapes enterprise analytics, it opens new possibilities for growth, making it an exciting time for businesses willing to lead the charge.
Thank you for the great interview, readers who wish to learn more should visit AnswerRocket.
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