Launching Rogo
Tl;dr: We’re making analyzing data as easy as running a Google search.
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Published on March 1, 2023 by John Willett
The Problem
If there is one challenge that companies of all shapes, sizes, and industries face, it’s making full use of their data. High-quality reporting and analytics has gone from a nice-to-have slide in the investor deck to a mission-critical element of the organization. Businesses that can’t put data behind their decision-making are falling behind their competitors more and more every year.
Companies are trying to meet the challenge. They’re spending hundreds of billions of dollars each year buying, generating, cleaning, and storing data. But the majority of decision-makers still can’t access and analyze it. They’re forced to push their questions onto a data analyst’s backlog and wait (perhaps days) for an answer. What comes back is a static output in Tableau or PowerPoint. Follow-up questions then produce fresh requests, more wait time, and more static outputs.
The results? Slow iteration. Missed insights. Poorer decision-making. Overworked data teams.
Companies are trying to meet the challenge. They’re spending hundreds of billions of dollars each year buying, generating, cleaning, and storing data. But the majority of decision-makers still can’t access and analyze it. They’re forced to push their questions onto a data analyst’s backlog and wait (perhaps days) for an answer. What comes back is a static output in Tableau or PowerPoint. Follow-up questions then produce fresh requests, more wait time, and more static outputs.
The results? Slow iteration. Missed insights. Poorer decision-making. Overworked data teams.
The Trade Off
For at least the last ten years we’ve all known that the central problem is at the interface layer. You might have the most well-modeled, well-engineered production database in the world, but it won’t matter: With the current analytics interfaces available, the non-technical PM at your org still won’t be able to use it.
The concept of “self-service analytics” was born from this realization. In recent years many products have charged out into market under this label. But except for a few tech-savvy folks on the margins who have taken the time to learn tools like Power BI, the problem has not budged. Self-service remains unsolved.
Why is this? Well, because it’s a difficult problem. And the solution required is more radical than people have traditionally thought. There is a challenging product tradeoff that no one has found a way to navigate:
On the one hand, getting correct answers from a database is hard. And the hard part isn’t SQL’s syntax. It’s knowing how the database is structured, knowing what each table looks like, and knowing how those things affect how your query needs to look. So making incrementally friendlier graphical query builders, which is the typical pathway for so-called self-service platforms, is—unfortunately—the wrong approach. No number of custom graphics or lowercase letters can mitigate the difficulties of interacting with a large database.
In response, people have (we think correctly) located natural language interfaces as the future for self-service data analytics. The idea is this: Let the user put their question in their own words, without needing to know the ins and outs of the database—and then let the product do the work. The timing is good. Along came GPT-3, and now text-to-SQL chatbots are a commoditized solution.
But from these end-to-end models the second side of this tradeoff has emerged: accuracy. Putting GPT-3 on a data warehouse and giving it to non-technical folks is like—as a Head of Data recently remarked to us—giving a gun to a toddler. The outputs are frequently wrong, and (even worse) the non-technical decision-maker might not even know it.
The concept of “self-service analytics” was born from this realization. In recent years many products have charged out into market under this label. But except for a few tech-savvy folks on the margins who have taken the time to learn tools like Power BI, the problem has not budged. Self-service remains unsolved.
Why is this? Well, because it’s a difficult problem. And the solution required is more radical than people have traditionally thought. There is a challenging product tradeoff that no one has found a way to navigate:
On the one hand, getting correct answers from a database is hard. And the hard part isn’t SQL’s syntax. It’s knowing how the database is structured, knowing what each table looks like, and knowing how those things affect how your query needs to look. So making incrementally friendlier graphical query builders, which is the typical pathway for so-called self-service platforms, is—unfortunately—the wrong approach. No number of custom graphics or lowercase letters can mitigate the difficulties of interacting with a large database.
In response, people have (we think correctly) located natural language interfaces as the future for self-service data analytics. The idea is this: Let the user put their question in their own words, without needing to know the ins and outs of the database—and then let the product do the work. The timing is good. Along came GPT-3, and now text-to-SQL chatbots are a commoditized solution.
But from these end-to-end models the second side of this tradeoff has emerged: accuracy. Putting GPT-3 on a data warehouse and giving it to non-technical folks is like—as a Head of Data recently remarked to us—giving a gun to a toddler. The outputs are frequently wrong, and (even worse) the non-technical decision-maker might not even know it.
The Solution
It is in this context that Rogo enters the picture with a fully reimagined vision of what working with data can be like. Our platform combines the flexibility and ease-of-use of ChatGPT with the control, accuracy, and security of enterprise business intelligence.
The solution is in our full-suite approach to natural language search. On the one hand, we eschew the black-box language model approach and instead map, where possible, user inputs to an intermediate representation. This means companies have total control over permitted queries, and it means non-technical users can audit queries for themselves. On the other, Rogo also enables SQL generation, charting, report generation, data manipulation, and team collaboration all through search.
As we grow and iterate with users, a lot will change about Rogo’s search. But a few things never will.
First of all, Rogo is for everyone—of all backgrounds. This means a few things. Most importantly, we will always have a free & open tier. Access to data insights has for too long been a privilege owned by an educated, often wealthy elite. Rogo will always be easily available to anyone—whether they’re students, journalists, small business owners, hobbyists, or anyone else—who wants to explore their data and find high-quality insights, regardless of their ability to pay.
It also means that people of all technical backgrounds can use us. We are the easiest entry point into the analytics layer. Full stop. This means meeting non-technical users where they are. Many analytics users, especially non-technical ones, don’t know exactly what they’re looking for at first. Our search is constructive, guiding, responsive, and intuitive—not a call-and-return SQL generator.
Another viewpoint that will never change: Accuracy and audibility are not qualities that can be compromised on. There’s no room for error in data-driven decision-making. Our intermediate representation provides essential control and transparency. Every result in Rogo is editable and auditable. And if a query needs to be checked by a data analyst, Rogo alerts the user and makes this easy.
Also, collaboration will always be a focal point for us. Data, and the products around it, should be a tool for cooperation, transparency, and joint decision-making. Sharing data and insights securely is an essential component of the data workflow—teams are smarter when everyone can see the data.
And lastly, Rogo will always be insanely easy to spin up. We will always strive for the top right corner of the low-effort, high-value matrix. As a self-serve user of Rogo, you can go from data to insights in minutes.
The solution is in our full-suite approach to natural language search. On the one hand, we eschew the black-box language model approach and instead map, where possible, user inputs to an intermediate representation. This means companies have total control over permitted queries, and it means non-technical users can audit queries for themselves. On the other, Rogo also enables SQL generation, charting, report generation, data manipulation, and team collaboration all through search.
As we grow and iterate with users, a lot will change about Rogo’s search. But a few things never will.
First of all, Rogo is for everyone—of all backgrounds. This means a few things. Most importantly, we will always have a free & open tier. Access to data insights has for too long been a privilege owned by an educated, often wealthy elite. Rogo will always be easily available to anyone—whether they’re students, journalists, small business owners, hobbyists, or anyone else—who wants to explore their data and find high-quality insights, regardless of their ability to pay.
It also means that people of all technical backgrounds can use us. We are the easiest entry point into the analytics layer. Full stop. This means meeting non-technical users where they are. Many analytics users, especially non-technical ones, don’t know exactly what they’re looking for at first. Our search is constructive, guiding, responsive, and intuitive—not a call-and-return SQL generator.
Another viewpoint that will never change: Accuracy and audibility are not qualities that can be compromised on. There’s no room for error in data-driven decision-making. Our intermediate representation provides essential control and transparency. Every result in Rogo is editable and auditable. And if a query needs to be checked by a data analyst, Rogo alerts the user and makes this easy.
Also, collaboration will always be a focal point for us. Data, and the products around it, should be a tool for cooperation, transparency, and joint decision-making. Sharing data and insights securely is an essential component of the data workflow—teams are smarter when everyone can see the data.
And lastly, Rogo will always be insanely easy to spin up. We will always strive for the top right corner of the low-effort, high-value matrix. As a self-serve user of Rogo, you can go from data to insights in minutes.
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