Systems | Development | Analytics | API | Testing

Winning With Data in the Fight Against Fraud, Waste, and Abuse

Fraud, waste, and abuse (FWA) in government is a constant, multi-billion dollar issue that challenges agency leaders at all levels and across all sectors, from healthcare to education to taxation to Social Security. The scope and scale of public spending—federal outlays alone were approximately $6.6 trillion in fiscal year 2020 according to the Congressional Budget Office—make FWA an inherently difficult problem to solve.

Does Your Company Suffer From a Lack of Data Democratization?

In the era of big data, an unprecedented amount of data is available to companies to drive growth. Yet up to 73% of companies’ data never get used. What are the bottlenecks to accessing data? And why is there such a wide gap between the data we have in our data lakes and data warehouses and the data we end up using for making business decisions? The smoking gun is in the hands of data democratization.

A CFO's Perspective: Understanding The Positive Business Impact of a Modern Financial Analytics Approach

I recently sat down with CFODive to discuss the importance of modern financial analytics in transforming the way financial leaders and their organizations operate – a topic that is only becoming increasingly prominent. Business strategies have had to rapidly adjust to address market volatility, consumer trends, and unpredictable world events. These dynamics have forced finance teams to rethink how they are using data and analytics and take a more modern approach.

What is a DevOps Test Toolchain and Why it Matters for Your Mobile App Development

The digital experience is now primary to our everyday lives. Our recent consumer report, Every Experience Matters, dove into quality and how it affects consumer behavior. We know, for example, that 20% of users will abandon a brand after encountering even one error on a mobile app. At the user level, everything comes down to customer experience.

6 Reasons Why Python Is Best for Apps Using AI, ML and Data Analytics

There are a variety of technology stacks for Artificial intelligence (AI), Machine learning (ML) and data analytics applications. However, the ideal programming language for AI must be powerful, scalable and readable. All three conditions are met by the Python programming language. With outstanding libraries, tools and frameworks for AI, ML and data analytics, Python has proven success leveraging all three technologies.