Trust your data

whitePaper | May 18, 2021

Next-gen data operations require next-gen data technologies and strategies and Azure is an ideal platform for this modernization to take place. Download this white paper to better understand the strategies, philosophies, and technologies your organization needs to expand analytics abilities, reduce technical debt, embrace AI, and more.

whitePaper | August 25, 2022

Everyday user experiences are being completely reimagined. Requesting a ride through an app is better than calling a cab. Buying online is changing the way we go shopping. And gone are the days of waiting in long lines at your local bank. Data within these modern businesses is in constant motion, flowing, in real-time, across systems, environments, and applications every time a user interacts with the system.

whitePaper | February 7, 2020

Multiple studies have proven that, while the business demand for databases grows, operational teams are constrained by traditional, reactive processes. Combined with budgetary pressures, this causes them to fall short in meeting the demand in a timely fashion. In addition, as companies adopt cloud paradigms, operational teams must manage across on-premises and cloud environments with the same resources. To be effective, companies need to shift from a reactive posture to one of standardization and proactive analysis and planning by leveraging intuitive tooling to automate manually intensive and non-value adding activities and to deliver real-time insights.

whitePaper | February 22, 2023

There are two reasons financial institutions are learning as much about their customers as possible by gathering—and processing—as much data as they can. Firstly, this information is invaluable for delivering more personalized products and services to increase revenues while making smarter decisions and improving operational efficiency. Better customer understanding can improve things like underwriting accuracy, for example, and many others.

whitePaper | June 24, 2022

The reactive approach to discovering, analyzing, and correcting data quality issues in business applications has been marginally effective throughout the era of data consolidation for analytics (data warehousing, etc.) and the subsequent onset of “big data.”

whitePaper | December 1, 2022

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