How AnswerRocket’s AI-Driven Insights Revolutionize Enterprise Analytics

In today’s fast-paced business landscape, making informed decisions quickly is crucial for the success of large organizations. The abundance of constantly growing data poses a challenge, as extracting actionable insights from it becomes a time-consuming process. Enter AnswerRocket, merging the power of ChatGPT with enterprise analytics. In a recent interview with Pete Reilly, COO, and Mike Finley, CTO, they shed light on how AnswerRocket’s innovative approach accelerates decision-making and empowers analytics and insights leaders to unlock the true potential of their data.

Watch the video below or read the transcript of the interview to learn more.

Pete: Hi, I’m Pete Reilly. I’m one of the co-founders of AnswerRocket and the COO of the company. 

Mike: My name is Mike Finley. I’m also a co-founder of AnswerRocket and the CTO of the company. 

What is AnswerRocket and Max?

Pete: AnswerRocket is ChatGPT meets enterprise analytics. Generally, the problem we solve is when we go into really large organizations, they have mountains of data that’s growing every day, and they struggle to make good business decisions quickly from it. And so we dramatically accelerate that process. We’ll take a process that might take a week to figure out how a brand says it’s performing in a particular market and bring it to 60 seconds. What that does is then it enables the business folks to then go ahead and act on that information instead of spending days or weeks analyzing it. 

How does AnswerRocket leverage AI and GPT?

Mike: AnswerRocket has been a leader in this process of engaging naturally with business users for ten years. We’ve introduced many of the popular concepts that are core to this technology now. The advent of GPT, or really large language models in general, has meant an enormous leap forward in AnswerRocket’s ability to both understand the user, not just the facts that they’re asking, but the intent of their question. Intents are things like, is there a comparison? Are we looking for outliers? Are we trying to determine a specific course of action to take? Right. So understanding that as well as the answer that they get back from the solution being something that is far more natural and usable as human language, powered by the language models. 

What makes AnswerRocket different?

Pete: There are other companies in the market that I would say we differentiate additionally in a couple of ways. One is this ability not just to ask a natural language question and get answer from a database, but to actually run a model on that data, to do a forecast on that data, to do a driver analysis on that data, to do a deep market share analysis on that data. Those are capabilities that come with this addition of data science, machine learning, but also combined with deep domain experience to solve really thorny, challenging problems in these vertical spaces. 

How does AnswerRocket unlock answers from structured and unstructured data?

Mike: So it’s important to realize that enterprises have their data siloed in two major sections. Right? There’s databases, traditionally that have been built up over time with imported and loaded data. And then there are also the more informal, unstructured sources that are all the enterprise flows of information, whether that’s email, meeting transcripts, PDF files, reports, PowerPoints. And so one of the key things about the AnswerRocket vision and the vision for Max, the AI agent, is to be able to have enterprise users access data from either one of those repositories at the same time and have them work together to produce answers and insights that really can power opportunities that weren’t available before this technology.

How does AnswerRocket fit in alongside other enterprise BI tools?

Mike: Are we trying to displace the dashboards and the reports and all the things that businesses do today? It’s not about displacing those technologies, it’s about removing the friction, right? So we’ve seen so many examples of companies that have 600 different reports.  They have so many reports, they don’t even know which one to use, right? With something like AnswerRocket in place. You can just ask your question and it will find the right source, whether that’s stored in an existing dashboard, in an existing business intelligence tool. Wherever that information is stored in documents, we’re going to be able to go retrieve it, pull it into the language model, and let the language model answer the question based on those facts. Right? So it’s not about eliminating the value that’s been created by the calculations and the historical trends and things that have been observed, because all those business practices are very valuable and they’re key to how enterprises run. It is about removing the friction of leveraging those things, right? So it’s all about being able to take advantage of them in an easier way, to be able to get to that information faster, to be able to compete better in a way that’s more satisfying to the human users. Because they’re no longer in the tedium of working through the processes of finding data and joining it up and putting it into a spreadsheet.

Pete: Dashboards aren’t going away anytime soon. Look, most companies, the major key performance indicators that people’s bonuses are based on are on these dashboards, right? And it’s a unified way for a company to look at their overall performance, see how we’re tracking, are we hitting our goals or not? That’s here to stay. Changing that out would be herculean effort. But what you end up seeing is that a lot of times these dashboards, they’re really good for just reporting the news, like what happened. They’re really not great then at helping you understand why that thing happened, or what would happen if I did something differently, or what’s going to happen next, or what should I do. It’s terrible at all those things. And that’s where really, I think we come in, is automating those questions and getting users to these business decisions sooner, because what they do today, they go to that dashboard and somebody downloads a bunch of data so they can do all this analysis. And I think I agree with Mike. To me, what ends up happening is those dashboards are in place. They become a data source or something like AnswerRocket. It’s just another place to get governed information that’s been cleansed and approved and so on that people can then use to automate their analysis and make good decisions easily. 

In Conclusion

By leveraging cutting-edge technologies such as GPT and combining data science, machine learning, and deep domain expertise, AnswerRocket brings natural language querying, advanced modeling, and deep analysis capabilities to the fingertips of business users. These capabilities give AnswerRocket the ability to revolutionize how analytics leaders access and leverage both structured and unstructured data, streamline workflows, and extract meaningful insights that drive tangible business outcomes. 

Get AnswerRocket and get meaningful insights from your data now.

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