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Data analytics is a complex subject. Our FAQ page will help you understand the basics, our product, and how to get started on your journey.

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Unlike a physical warehouse that would store physical pieces of data (print outs of spreadsheets, external hard drives, etc.), a data warehouse today exists as a storage system in the cloud. A data warehouse is a storage system that is designed to store your corporate data, and organize it in a manner that makes it easy to use and easy to find.

Data warehouses are unique because they organize your data in a way that makes it easy to find what you’re looking for. Instead of having spreadsheets all over the floor (metaphorically speaking), the warehouse maintains technology to categorize them so that they’re easy to access at any time.

A data lake is similar to a data warehouse except it takes in EVERYTHING. Like a lake full of water, a data lake can be home to just about any type of data. This includes those emails and spreadsheets that would be in a data warehouse, but can also include audio and video recordings, still images, transcripts, and most anything else that you can think of.

A data lake does not organize or categorize your data in advance - it’s really used as a storage tool for multiple sources of data. Data lakes can be great solutions for AI or machine learning tools that want to see an entire picture of the data available from all sources.

A data lakehouse is something of a hybrid between a data warehouse and a data lake.

Like a data lake, a data lakehouse can take in all varieties of data without any concerns about the format. It can interpret everything from spreadsheets of financial projections to videos of member and employee interactions. What it does differently from a simple data lake though is that it layers the data in a way that makes it more usable, like a data warehouse.

This is why a data lakehouse is often thought of as the perfect in-between solution for those who would have been for both data warehouses and data lakes.

Imagine if you could use your data to tell the future. That’s predictive analytics. It’s a method of using historical data to make presumptions about what might happen in the future - next month, next year, or in the next decade. It can be done manually, or now it is often done using machine learning (although predictive analytics itself is not machine learning). 

Predictive analytics is common within financial institutions to determine important metrics like future cash flow. It can also be used to spot fraud, or to assess the potential of current or future investments.

AI is an attempt to get computers to perform the same processes that we perform with our human minds. Instead of humans doing calculations, reasoning, and interaction, computers are fed enough data so that they can begin to simulate human activity. It may not be quite on the level of human intelligence, but with an increasing amount of data AI is getting better.

AI is not a new phenomenon. You may remember when Watson the IBM computer played on Jeopardy several years ago, and AI was already in use before that. Any time that you see a chatbot on a website, or a search engine that guesses what your search will be before you’re finished typing - those are examples of AI in everyday use.

Machine learning is an attempt to have computers learn the same way that humans do - by building on concepts already learned, and effectively making the machines appear smarter. The concept sounds like science fiction, but it’s actually been around for decades. It’s the same way computers can become excellent chess players, or how they appear to solve complex problems.

In financial services, machine learning is a useful tool to help your tech stack run more efficiently. Machine learning is what will help your analytics tools understand patterns and members’ behaviors, and make predictions that allow you to continually offer better quality service.

Your data analytics are crucial to the success of your operation, so why would you ever want to give up control to a third party? Every financial institution is unique, and so your software solution should be as well. Trying to fit your existing model into a pre-fabricated piece of software is like shoving a square peg into a round hole. Instead, a solution that’s customizable for your needs makes things that much easier.

Staying in the driver’s seat means that YOU have control. You decide what data is collected, and how it is kept secure. You can control how your data is stored, who has access to it, and how it will be used to make decisions. You can also decide who to share that data with, and at what times. That level of control is integral to your data security.

In your financial institution you have access to a tremendous amount of data. Every piece of member spending, every member interaction with the institution, the status of every investment, etc. If you’re still analyzing that data manually, or worse yet not doing much with it at all, you’re leaving untold growth opportunities on the table.

An analytics solution helps you harness that data and use it properly to leverage the institution’s growth. With that data, you can make predictions about investments, understand buying patterns, and make adjustments that best suit your members’ behavior. There’s a lot of data available, but having the right tools makes things significantly more manageable.

Imagine if you had a data analytics solution that worked for you. Picture a solution that fully integrates with your existing stack, and is built to speak to anything else that you may implement. It works fully, is quickly customizable, and offers a much clearer picture based on your data input of your financial forecast.

What if that picture was easier to achieve with the right tools? What if, instead of hiring a team of analysts because you were always frustrated with your old tech, you actually had a tool that worked for you? What if that reporting automation was able to show you data about how the business is changing, and where you may be able to make improvements you hadn’t seen before? That’s the power of Gemineye.

We try to make it quick! We have our customers using Gemineye within a matter of weeks, or sometimes even a few days. We continually find that our implementations take over 70% less time than the competition.

When it comes to customizations, those take a bit more time to complete as expected.Often customizations are going to take several months to build out.

A successful analytics implementation shouldn’t just be about printing reports - it needs to focus on solving problems. It’s great that you can build better reports faster, but if those reports aren’t helping you run your business, they won’t have much value.

A successful implementation means working with a company that rolls up its sleeves, and understands the problems that you’re facing. It means having a partner that looks at the wins that you’re trying to achieve, and how to get there. It means that we work with organizations from the top-down, to ensure support system-wide adoption that works as smoothly at the executive level as it does team-wide.

We have approximately 250 reports with more being added all the time!

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