Bring Your Data together for Insight and Action
75% of your data's value is being wasted...
With traditional data management systems, very little of the value of an organization's data becomes accessible. Because of the rigid nature of these systems, and the technical obstacles in combining data from siloed enterprise systems, data enterprise efforts frequently deliver just a fraction of the value that is possible.
Data Fusion changes the way that data can be used by organizations. Using advanced algorithmic joins and dynamic fusion models, Datanova Fusion Hub's patented technology can combine data at the attribute-level to create a valuable Single Source of Truth.
The advantages in your data.
The capability to access and analyze clean and fully integrated data from every bank system offers tremendous business advantages for banks. When a bank’s data is brought together in real-time to drive insight and action, the beneﬁts can stretch across a bank.
Implement true omni-channel strategies, highlight cross sell opportunities, prospect new customers, identify existing high value customers, evaluate the competition, increase market share, etc.
Optimize stafﬁng models, identify inefﬁciencies, understand branch performance, improve customer targeting, reallocate resources for immediate ROI, etc.
Identify hidden errors, predict customer attrition, highlight unrecognized pain points, identify possible fraud, support compliance, etc.
Born in defense intelligence.
Bred for business.
Datanova Scientific developed unrivaled data and analytics expertise while leading mission-critical projects for U.S. defense intelligence and U.S. federal agencies. Over this time, Datanova proved itself capable of securing and protecting America’s most sensitive data, while delivering exceptional technical capabilities and strategic insights and services.
We’ve now optimized our technological advantage for the financial sector, creating unmatched data unification and fusion capabilities, while streamlining processes and analytics for commercial requirements.
Establishing the analytical relationship between internal & external data
Understanding analytical relationships in data
In an earlier post we looked at the data coming from the external data provider to make sure that it could support the insights we were looking for, and that the external data could be connected to the internal data. That was a structural review of the data. Without some kind of shared variables that could relate the new data to existing data, it wouldn’t be possible to align the different data sets.
At this point of the process, we initiate an analysis of the data itself to uncover what the data from a bank can tell us about the new data that we’ve acquired from a data provider.