Data Warehouses
Power your most ambitious roadmap projects and activate your data like never before.
The majority of organizations can agree that disparate data has a strong negative impact. A data warehouse is one data management solution that serves as a centralized point to normalize and empower the activation of data from multiple sources and systems. By extracting and transforming data from core sources, including your enterprise resource planning (ERP), customer relationship management (CRM) and content services (ECM), you can understand the bigger picture of your organization.
By identifying a common data point, such as a social security number, patient ID or vendor code, user profiles can be curated. This identifier(s) can be used to connect that user across all of your core systems and enable a first-class user experience for them while powering reporting, automation and machine learning for your organization.
With a strategically implemented ECM application, your data isn’t just available, but it’s dynamic. You can create a “Google-like” experience for your team with the ability to search for records based on key terms within them. The solutions we support integrate with more than 500 different applications with limited time spent on custom coding, making it one of the fastest deployments for full data visibility.
Disjointed Systems
As you grow and empower your team with more tools, the data gets exponentially more complex, right when you need it most. In the beginning, we all had fewer systems, so exporting and importing data was somewhat manageable, albeit risky. In any modern enterprise, the tools are abundant. A data warehouse allows you to extract the most important data points from each system and normalize them to power business intelligence and machine learning including core use cases such as automated fraud detection or a 360 citizen experience..
Unmanageable Amount of Data
If you have more data than you know what to do with, a data warehouse could power a game-changing solution. Take fraud detection, a common use case for the public sector and healthcare payers. When a user’s data is normalized across all systems the fraud detection process can be largely run by automation and machine learning. Information is checked and automatically confirmed across all systems, flagging your team members when an anomaly is detected, and refocusing your team’s time on resolving them.
Poor Customer Experience
The seamless user experience we all dream of can be brought to life through the aggregation and normalization of your data when you have the right place to consolidate it. Once the data from multiple systems is normalized you have the ability to build a single interaction point with your stakeholders where all of their pertinent data and content can be uploaded or retrieved.