At Gemini Data, we say, “People only hear statistics. But they feel stories.” What we mean is that data on its own is powerful, but with a good story, it’s unforgettable. And that’s what Gemini Explore does – it transforms data and analytics by enabling users to easily and intuitively interact with data using contextual storytelling. It’s all about simplifying and making it easier to see, understand, and communicate the complex – so people can learn faster and do their jobs better.
So how do we do it? There are many features of Explore that make it powerful and easy to use. We’ll dive into just a few of them here.
- Simple, accessible data onboarding
If you’re using a graph database to do knowledge graph exploration, the standard process for any data engineer or scientist is to physically onboard the data, a process where the required knowledge and technical skill set for most databases is limited to a certain group of people – the data engineers and scientists. Once that data is onboarded, the challenge is ensuring that you have both the right information and enough information without having too much.
But Explore dramatically reduces the complexity of the onboarding process and consolidates it within a single interface where a user can just pop in the data, go through the wizards, create the relationships for the object they need, and produce a graph they can explore.
- Conditional Displays
This is a feature we’ve built in that based on the value of a particular set of notes that a user selects, they can choose the size or the coloration of the note. One Explore use case we like is movie databases (you can check it out in our Demo Library). For example, if a movie makes more money over a certain time period, you can set the size of the note to be different – so the lesser amount would be smaller and the larger amount would be bigger. Additionally, this also applies to the edges or the relationship between the notes. A user can adjust the coloration and the thickness of the line to better connect two dots.
Users can also set two separate conditions. For example, you can set sci-fi movies to green and dramas to blue, and so on, allowing users to more clearly distinguish relationships within larger data sets. The user gets to choose the condition they want to match it against.
- Unmatched Data Exporting
Once you’ve onboarded your data, you can begin to explore the data on the graph. From there, a user can export the data however they need. Continuing with the movie database example, if a user has an entire movie database ingested into Explore, but they only want the data set that matches a certain genre, they can do that within the platform. A user would choose another condition, let’s say a particular director with gross sales over a certain number, and they can export that using any decision intelligence platform to create a line chart, pie chart, etc. inside that tool. We’re currently working to connect Explore directly with decision intelligence tools and make it possible to suggest the type of visualization tool a person should use.
- Powerful Data Model
Explore’s data model is one-of-a-kind and completely no-code. With our process, all you have to know is how to click. That’s it. No data science degree or coding experience required. In fact, no-code or low-code is the focus of Explore (and our whole suite of products) so that anyone who wants to do the job, can do it. We’re aiming to achieve data nirvana, where you have a set of data that you feed in, and you just get the result that you want to see. No need for going through different programming, acquiring a new skill, or learning to write code. Just the results you need simply and quickly.
Better Data Contextualization is Possible with Explore
At the end of the day, we want to enable our users to bring the contextual data analysis and commentary they need to fully appreciate an insight, and do it in one platform without an army of data scientists. Explore can uncover connections using graph techniques in combinations of diverse data at scale; help organizations accelerate capabilities to anticipate, shift, and respond; find relationships between people, places, and things; capture knowledge to make it easier to perform queries and answer questions, and increase understanding around organizing and preparing data.