Driving Enterprise Value with Unhindered Data Accessibility
We are an enterprise software company, and when we see another company doing something that makes a real bottom line impact we are sure to do more than tip our hat to them. K3 has off the shelf connectivity into and out of Snowflake that allows companies to not just better control, clean and store their data, but also set up a framework to capture data that would otherwise be lost due to time and expense. Data is not the new oil. Data is the new everything. Here’s your key. By the way. For most companies Snowflake is only a part of the picture. That’s why K3 is designed to feed Snowflake, Databases, and APIs all from the same instance.
We said it before. Data is not the new oil. Data is the new everything. That’s why companies need a platform to Get Data Done. Don’t let the sophisticates fool you. Enterprise Data Management is just getting all your company data together in a way that it can be quickly assembled into information. Key word: quickly. Anything that gets in the way of data velocity needs to be left behind. K3s entire purpose is to provide low code integration and transformation to increase the velocity of data.
Spend less time wrestling data
and more time on analysis
It’s a sad truth. Too much time on data analysis is focused on data prep. Most of us cut our teeth in Excel, slamming vlookups and other formulas to get where we wanted. K3 delivers better data shaping right out of the box.
Trade surveillance is a case study in enterprise data management. K3 enables trading firms to capture complex order and trade data in real time, transforming it to a harmonized standard, to inform an analytical framework. That’s a clever way of saying K3 provides a better surveillance solution, not by offering a smarter algorithm, but better data management.
How does one ensure trading activity does not result in fines? Real time trade limits. K3 is the only solution with real time limits capability, direct access to exchange data, and a calculation engine that takes into account the complexity of your portfolio.
Building a data warehouse used to be career suicide. The failure rate of data warehouse projects was astronomical. So, what changed? Staying competitive requires corporations to do more with data than merely store it for posterity. Now data from a wide variety of sources spanning legacy, on-premise and cloud applications must be considered to make cogent decisions. Analysis has also evolved beyond statistical approaches to data science capable of answering questions never posed before. These answers unlock enterprise value and require data management to keep pace with demands.